Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching. However, most of the prior works focus only on location estimation, ignoring orientation, which cannot meet the requirements in outdoor AR applications. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance. Furthermore, we present an approach to extend the single image query-based geo-localization approach by utilizing temporal information from a navigation pipeline for robust continuous geo-localization. Experimentation on several large-scale real-world video sequences demonstrates that our approach enables high-precision and stable AR insertion.

This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments. Existing end-to-end learning-based VLN methods struggle at this task as they focus mostly on utilizing raw visual observations and lack the semantic spatio-temporal reasoning capabilities which is crucial in generalizing to new environments. In this regard, we present a hybrid transformer-recurrence model which focuses on combining classical semantic mapping techniques with a learning-based method. Our method creates a temporal semantic memory by building a top-down local ego-centric semantic map and performs cross-modal grounding to align map and language modalities to enable effective learning of VLN policy. Empirical results in a photo-realistic long-horizon simulation environment show that the proposed approach outperforms a variety of state-of-the-art methods and baselines with over 22% relative improvement in SPL in prior unseen environments.

Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, unified representation of the scene topology will allow agents to act efficiently to move through their environment, communicate the environment state with others, and utilize the representation for diverse downstream tasks. To this end, we propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its environment by simultaneously learning to navigate through said environment. We demonstrate that our approach, GraphMapper, enables the learning of effective navigation policies through fewer interactions with the environment than vision-based systems alone. Further, we show that GraphMapper can act as a modular scene encoder to operate alongside existing Learning-based solutions to not only increase navigational efficiency but also generate intermediate scene representations that are useful for other future tasks.

Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potential to outperform the classical solutions developed for this task; however, they come at a significantly increased computational load. Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget. To this end, we propose a method to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects–alongside raw visual streams such as RGB, depth, and semantic segmentation masks—into a semantically informed, top-down egocentric map representation. Further, to enable the effective use of this information, we introduce a novel 2-D map attention mechanism, based on the successful multi-layer Transformer networks. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach. We show that by using our novel attention schema and auxiliary rewards to better utilize scene semantics, we outperform multiple baselines trained with only raw inputs or implicit semantic information while operating with an 80% decrease in the agent’s experience.

Proper occlusion based rendering is very important to achieve realism in all indoor and outdoor Augmented Reality (AR) applications. This paper addresses the problem of fast and accurate dynamic occlusion reasoning by real objects in the scene for large scale outdoor AR applications. Conceptually, proper occlusion reasoning requires an estimate of depth for every point in augmented scene which is technically hard to achieve for outdoor scenarios, especially in the presence of moving objects. We propose a method to detect and automatically infer the depth for real objects in the scene without explicit detailed scene modeling and depth sensing (e.g. without using sensors such as 3D-LiDAR). Specifically, we employ instance segmentation of color image data to detect real dynamic objects in the scene and use either a top-down terrain elevation model or deep learning based monocular depth estimation model to infer their metric distance from the camera for proper occlusion reasoning in real time. The realized solution is implemented in a low latency real-time framework for video-see-though AR and is directly extendable to optical-see-through AR. We minimize latency in depth reasoning and occlusion rendering by doing semantic object tracking and prediction in video frames.

We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior works were demonstrated on small datasets and did not lend themselves to scaling up for large-scale applications. To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km2 area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method that effectively combines the appearance and semantic cues from both modalities to handle drastic cross-modal variations. Experiments on the proposed dataset show that our model achieves a strong result of a median rank of 5 in matching across a large test set of 50K location pairs collected from a 14km^2 area. This represents a significant advancement over prior works in performance and scale. We conclude with qualitative results to highlight the challenging nature of this task and the benefits of the proposed model. Our work provides a foundation for further research in cross-modal visual localization.

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as “helpful” significantly improve human performance, “incorrect” and “unhelpful” explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanation on a human-AI collaborative task.

We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D georeferenced data, for augmented reality driving applications. The geo-registration process uses our predicted vehicle pose to generate a rendered depth map for each frame, allowing 3D graphics to be convincingly blended with the real world view. We also estimate absolute depth values for dynamic objects, up to 120 meters, based on the rendered depth map and update the rendered depth map to reflect scene changes over time. This process also creates opportunistic global heading measurements, which are fused with other sensors, to improve estimates of the 6 degrees-of- freedom global pose of the vehicle over state-of-the-art outdoor augmented reality systems [5]-, [19]. We evaluate the navigation accuracy and depth map quality of our system on a driving vehicle within various large-scale environments for producing realistic augmentations.

In this paper we present a dual, wide area, collaborative augmented reality (AR) system that consists of standard live view augmentation, e.g., from helmet, and zoomed-in view augmentation, e.g., from binoculars. The proposed advanced scouting capability allows long range high precision augmentation of live unaided and zoomed-in imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and not jitter or drift as the user moves around and examines the scene. The AR insertions for the binocs must work instantly when they are picked up anywhere as the user moves around. The design of both AR modules is based on using two different cameras with wide and narrow field of view (FoV) lenses. The wide FoV gives context and enables the recovery of location and orientation of the prop in 6 degrees of freedom (DoF) much more robustly, whereas the narrow FoV is used for the actual augmentation and increased precision in tracking. Furthermore, narrow camera in unaided eye and wide camera on the binoculars are jointly used for global yaw (heading) correction. We present our navigation algorithms using monocular cameras in combination with IMU and GPS in an Extended Kalman Filter (EKF) framework to obtain robust and real-time pose estimation for precise augmentation and cooperative tracking.

This paper introduces a user-worn Augmented Reality (AR) based first-person weapon shooting system (AR-Weapon), suitable for both training and gaming. Different from existing AR-based first-person shooting systems, AR-Weapon does not use fiducial markers placed in the scene for tracking. Instead it uses natural scene features observed by the tracking camera from the live view of the world. The AR-Weapon system estimates 6-degrees of freedom orientation and location of the weapon and of the user operating it, thus allowing the weapon to fire simulated projectiles for both direct fire and non-line of sight during live runs. In addition, stereo cameras are used to compute depth and provide dynamic occlusion reasoning. Using the 6-DOF head and weapon tracking, dynamic occlusion reasoning and a terrain model of the environment, the fully virtual projectiles and synthetic avatars are displayed on the user's head mounted Optical-See-Through (OST) display overlaid over the live view of the real world. Since the projectiles, weapon characteristics and virtual enemy combatants are all simulated they can easily be changed to vary scenarios, new projectile types and future weapons. In this paper, we present the technical algorithms, system design and experiment results for a prototype AR-Weapon system.

In this paper we present an augmented reality binocular system to allow long range high precision augmentation of live telescopic imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and must not jitter and drift as the user pans around and examines the scene with the binoculars. The design of the system is based on using two different cameras with wide field of view and narrow field of view lenses enclosed in a binocular shaped shell. Using the wide field of view gives us context and enables us to recover the 3D location and orientation of the binoculars much more robustly, whereas the narrow field of view is used for the actual augmentation as well as to increase precision in tracking. We present our navigation algorithm that uses the two cameras in combination with an inertial measurement unit and global positioning system in an extended Kalman filter and provides jitter free, robust and real-time pose estimation for precise augmentation. We have demonstrated successful use of our system as part of information sharing example as well as a live simulated training system for observer training, in which fixed and rotary wing aircrafts, ground vehicles, and weapon effects are combined with real world scenes.

This paper proposes a real-time navigation approach that is able to integrate many sensor types while fulfilling performance needs and system constraints. Our approach uses a plug-and-play factor graph framework, which extends factor graph formulation to encode sensor measurements with different frequencies, latencies, and noise distributions. It provides a flexible foundation for plug-and-play sensing, and can incorporate new evolving sensors. A novel constrained optimal selection mechanism is presented to identify the optimal subset of active sensors to use, during initialization and when any sensor condition changes. This mechanism constructs candidate subsets of sensors based on heuristic rules and a ternary tree expansion algorithm. It quickly decides the optimal subset among candidates by maximizing observability coverage on state variables, while satisfying resource constraints and accuracy demands. Experimental results demonstrate that our approach selects subsets of sensors to provide satisfactory navigation solutions under various conditions, on large-scale real data sets using many sensors.

This paper proposes a novel vision-aided navigation approach that continuously estimates precise 3D absolute pose for aerial vehicles, using only inertial measurements and monocular camera observations. Our approach is able to provide accurate navigation solutions under long-term GPS outage, by tightly incorporating absolute geo-registered information into two kinds of visual measurements: 2D-3D tie-points, and geo-registered feature tracks. 2D-3D tie-points are established by finding feature correspondences to align an aerial video frame to a 2D geo-referenced image rendered from the 3D terrain database. These measurements provide global information to correct accumulated error in navigation estimation. Geo-registered feature tracks are generated by associating features across consecutive frames. They enable the propagation of 3D geo-referenced values to further improve the pose estimation. All sensor measurements are fully optimized in a smoother-based inference framework, which achieves efficient relinearization and real-time estimation of navigation states and their covariances over a constant-length of sliding window. Experimental results demonstrate that our approach provides accurate and consistent aerial navigation solutions on several large-scale GPS-denied scenarios.

In this paper, we expand our previous work on augmented reality (AR) binoculars to support wider range of user motion - up to thousand square meters compared to only a few square meters as before. We present our latest improvements and additions to our pose estimation pipeline and demonstrate stable registration of objects on the real world scenery while the binoculars are undergoing significant amount of parallax-inducing translation.

AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.

This paper proposes a navigation algorithm that provides a low-latency solution while estimating the full nonlinear navigation state. Our approach uses Sliding-Window Factor Graphs, which extend existing incremental smoothing methods to operate on the subset of measurements and states that exist inside a sliding time window. We split the estimation into a fast short-term smoother, a slower but fully global smoother, and a shared map of 3D landmarks. A novel three-stage visual feature model is presented that takes advantage of both smoothers to optimize the 3D landmark map, while minimizing the computation required for processing tracked features in the short-term smoother. This three-stage model is formulated based on the maturity of the estimation of the 3D location of the underlying landmark in the map. Long-range associations are used as global measurements from matured landmarks in the short-term smoother and loop closure constraints in the long-term smoother. Experimental results demonstrate our approach provides highly-accurate solutions on large-scale real data sets using multiple sensors in GPS-denied settings.

In this paper we present an augmented reality binocular system to allow long range high precision augmentation of live telescopic imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and must not jitter and drift as the user pans around and examines the scene with the binoculars. The design of the system is based on using two different cameras with wide field of view, and narrow field of view lenses enclosed in a binocular shaped shell. Using the wide field of view gives us context and enables us to recover the 3D location and orientation of the binoculars much more robustly, whereas the narrow field of view is used for the actual augmentation as well as to increase precision in tracking. We present our navigation algorithm that uses the two cameras in combination with an IMU and GPS in an Extended Kalman Filter (EKF) and provides jitter free, robust and real-time pose estimation for precise augmentation. We have demonstrated successful use of our system as part of a live simulated training system for observer training, in which fixed and rotary wing aircrafts, ground vehicles, and weapon effects are combined with real world scenes.

Camera tracking system for augmented reality applications that can operate both indoors and outdoors is described. The system uses a monocular camera, a MEMS-type inertial measurement unit (IMU) with 3-axis gyroscopes and accelerometers, and GPS unit to accurately and robustly track the camera motion in 6 degrees of freedom (with correct scale) in arbitrary indoor or outdoor scenes. IMU and camera fusion is performed in a tightly coupled manner by an error-state extended Kalman filter (EKF) such that each visually tracked feature contributes as an individual measurement as opposed to the more traditional approaches where camera pose estimates are first extracted by means of feature tracking and then used as measurement updates in a filter framework. Robustness in feature tracking and hence in visual measurement generation is achieved by IMU aided feature matching and a two-point relative pose estimation method, to remove outliers from the raw feature point matches. Landmark matching to contain long-term drift in orientation via on the fly user generated geo-tiepoint mechanism is described.

Visual landmark matching with a pre-built landmark database is a popular technique for localization. Traditionally, landmark database was built with visual odometry system, and the 3D information of each visual landmark is reconstructed from video. Due to the drift of the visual odometry system, a global consistent landmark database is difficult to build, and the inaccuracy of each 3D landmark limits the performance of landmark matching. In this paper, we demonstrated that with the use of precise 3D Li-dar range data, we are able to build a global consistent database of high precision 3D visual landmarks, which improves the landmark matching accuracy dramatically. In order to further improve the accuracy and robustness, landmark matching is fused with a multi-stereo based visual odometry system to estimate the camera pose in two aspects. First, a local visual odometry trajectory based consistency check is performed to reject some bad landmark matchings or those with large errors, and then a kalman filtering is used to further smooth out some landmark matching errors. Finally, a disk-cache-mechanism is proposed to obtain the real-time performance when the size of the landmark grows for a large-scale area. A week-long real time live marine training experiments have demonstrated the high-precision and robustness of our proposed system.

Odometry component of a camera tracking system for augmented reality applications is described. The system uses a MEMS-type inertial measurement unit (IMU) with 3-axis gyroscopes and accelerometers and a monocular camera to accurately and robustly track the camera motion in 6 degrees of freedom (with correct scale) in arbitrary indoor or outdoor scenes. Tight coupling of IMU and camera is achieved by an error-state extended Kalman filter (EKF) which performs sensor fusion for inertial navigation at a deep level such that each visually tracked feature contributes as an individual measurement as opposed to the more traditional approaches where camera pose estimates are first extracted by means of feature tracking and then used as measurement updates in a filter framework. Robustness, on the other hand, is achieved by using a geometric hypothesize-and-test architecture based on the five-point relative pose estimation method, rather than a Mahalanobis distance type gating mechanism derived from the Kalman filter state prediction, to select the inlier tracks and remove outliers from the raw feature point matches which would otherwise corrupt the filter since tracks are directly used as measurements.

In this paper, we present a unified approach for a drift-free and jitter-reduced vision-aided navigation system. This approach is based on an error-state Kalman filter algorithm using both relative (local) measurements obtained from image based motion estimation through visual odometry, and global measurements as a result of landmark matching through a pre-built visual landmark database. To improve the accuracy in pose estimation for augmented reality applications, we capture the 3D local reconstruction uncertainty of each landmark point as a covariance matrix and implicity rely more on closer points in the filter. We conduct a number of experiments aimed at evaluating different aspects of our Kalman filter framework, and show our approach can provide highly-accurate and stable pose both indoors and outdoors over large areas. The results demonstrate both the long term stability and the overall accuracy of our algorithm as intended to provide a solution to the camera tracking problem in augmented reality applications.

We present an innovative path following system based upon multi-camera visual odometry and visual landmark matching. This technology enables reliable mobile robot navigation in real world scenarios including GPS-denied environments both indoors and outdoors. We recover paths in full 3D, making it applicable to both on and off-road ground vehicles. Our controller relies on pose updates from visual odometry, allowing us to achieve path following even when only a joystick drive interface to the base robot platform is available. We experimentally investigate two specific applications of our technology to autonomous navigation on ground vehicles - non line-of-sight leader-following (between heterogeneous platforms) and retro-traverse to home base. For safety and reliability we add dynamic short range obstacle detection and reactive avoidance capabilities to our controller. We show the results for end-to-end real time implementation of this technology using current off-the-shelf computing and network resources in challenging environments.

In this paper, we present a unified approach for a camera tracking system based on an error-state Kalman filter algorithm. The filter uses relative (local) measurements obtained from image based motion estimation through visual odometry, as well as global measurements produced by landmark matching through a pre-built visual landmark database and range measurements obtained from radio frequency (RF) ranging radios. We show our results by using the camera poses output by our system to render views from a 3D graphical model built upon the same coordinate frame as the landmark database which also forms the global coordinate system and compare them to the actual video images. These results help demonstrate both the long term stability and the overall accuracy of our algorithm as intended to provide a solution to the GPS denied ubiquitous camera tracking problem under both vision-aided and vision-impaired conditions.

We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of vision algorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data and a distributed visual landmark matching capability with geometric consistency verification. We apply these techniques to implement a tag-along robot, where a human operator leads the way and a robot autonomously follows. We show results for a real-time implementation of such a system with real field constraints on CPU power and network resources.

We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.

This paper proposes a novel approach to discover a set of class specific ldquocomposite featuresrdquo as the feature pool for the detection and classification of complex objects using AdaBoost. Each composite feature is constructed from the combination of multiple individual features. Unlike previous works that design features manually or with certain restrictions, the class specific features are selected from the space of all combinations of a set of individual features. To achieve this, we first establish an analogue between the problem of discriminative feature selection and generative image segmentation, and then draw discriminative samples from the combinatory space with a novel algorithm called discriminative generalized Swendsen-Wang cut. These samples form the initial pool of features, where AdaBoost is applied to learn a strong classifier combining the most discriminative composite features. We demonstrate the efficacy of our approach by comparing with existing detection algorithms for finding people in general pose.

We present a novel building segmentation system for densely built areas, containing thousands of buildings per square kilometer. We employ solely sparse LIDAR (Light/Laser Detection Ranging) 3D data, captured from an aerial platform, with resolution less than one point per square meter. The goal of our work is to create segmented and delineated buildings as well as structures on top of buildings without requiring scanning for the sides of buildings. Building segmentation is a critical component in many applications such as 3D visualization, robot navigation and cartography. LIDAR has emerged in recent years as a more robust alternative to 2D imagery because it acquires 3D structure directly, without the shortcomings of stereo in un- textured regions and at depth discontinuities. Our main technical contributions in this paper are: (i) a ground segmentation algorithm which can handle both rural regions, and heavily urbanized areas, where the ground is 20% or less of the data, (ii) a building segmentation technique, which is robust to buildings in close proximity to each other, sparse measurements and nearby structured vegetation clutter, and (Hi) an algorithm for estimating the orientation of a boundary contour of a building, based on minimizing the number of vertices in a rectilinear approximation to the building outline, which can cope with significant quantization noise in the outline measurements. We have applied the proposed building segmentation system to several urban regions with areas of hundreds of square kilometers each, obtaining average segmentation speeds of less than three minutes per km2on a standard Pentium processor. Extensive qualitative results obtained by overlaying the 3D segmented regions onto 2D imagery indicate accurate performance of our system.

In this paper, we study how to build a vision-based system for global localization with accuracies within 10cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy of traditional short-term motion based visual odometry systems has improved significantly in recent years, these systems alone cannot solve the drift problem over large areas. Landmark based localization combined with visual odometry is a viable solution to the large scale localization problem. However, a systematic study of the specification and use of such a landmark database has not been undertaken. We propose techniques to build and optimize a landmark database systematically and efficiently using visual odometry. First, topology inference is utilized to find overlapping images in the database. Second, bundle adjustment is used to refine the accuracy of each 3D landmark. Finally, the database is optimized to balance the size of the database with achievable accuracy. Once the landmark database is obtained, a new real-time global localization methodology that works both indoors and outdoors is proposed. We present results of our study on both synthetic and real datasets that help us determine critical design parameters for the landmark database and the achievable accuracies of our proposed system.

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem, which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher' s Linear Discriminants and Gibbs Sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.

This paper presents a novel framework, prototype embedding and embedding transition (PEET), for matching objects, especially vehicles, that undergo drastic pose, appearance, and even modality changes. The problem of matching objects seen under drastic variations is reduced to matching embeddings of object appearances instead of matching the object images directly. An object appearance is first embedded in the space of a representative set of model prototypes (prototype embedding (PE)). Objects captured at disparate temporal and spatial sites are embedded in the space of prototypes that are rendered with the pose of the cameras at the respective sites. Low dimensional embedding vectors are subsequently matched. A significant feature of our approach is that no mapping function is needed to compute the distance between embedding vectors extracted from objects viewed from disparate pose and appearance changes, instead, an embedding transition (ET) scheme is utilized to implicitly realize the complex and non-linear mapping with high accuracy. The heterogeneous nature of matching between high-resolution and low-resolution image objects in PEET is discussed, and an unsupervised learning scheme based on the exploitation of the heterogeneous nature is developed to improve the overall matching performance of mixed resolution objects. The proposed approach has been applied to vehicular object classification and query application, and the extensive experimental results demonstrate the efficacy and versatility of the PEET framework.

Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15 Hz update rate on a dual-core 2 GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a simultaneous localization and mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.

Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction. Traditional visual odometry systems drift over time as the frame-to-frame errors accumulate. In this paper, we propose to improve visual odometry using visual landmarks in the scene. First, a dynamic local landmark tracking technique is proposed to track a set of local landmarks across image frames and select an optimal set of tracked local landmarks for pose computation. As a result, the error associated with each pose computation is minimized to reduce the drift significantly. Second, a global landmark based drift correction technique is proposed to recognize previously visited locations and use them to correct drift accumulated during motion. At each visited location along the route, a set of distinctive visual landmarks is automatically extracted and inserted into a landmark database dynamically. We integrate the landmark based approach into a navigation system with 2 stereo pairs and a low-cost inertial measurement unit (IMU) for increased robustness. We demonstrate that a real-time visual odometry system using local and global landmarks can precisely locate a user within 1 meter over 1000 meters in unknown indoor/outdoor environments with challenging situations such as climbing stairs, opening doors, moving foreground objects etc..

This paper addresses the problem of matching vehicles across multiple sightings under variations in illumination and camera poses. Since multiple observations of a vehicle are separated in large temporal and/or spatial gaps, thus prohibiting the use of standard frame-to-frame data association, we employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. Furthermore, since our domain is aerial video tracking, in order to deal with poor image quality and large resolution and quality variations, our approach employs robust alignment and match measures for different stages of vehicle matching. Most notably, we employ a heterogeneous collection of features such as lines, points, and regions in an integrated matching framework. Heterogeneous features are shown to be important. Line and point features provide accurate localization and are employed for robust alignment across disparate views. The challenges of change in pose, aspect, and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences. However, since lines and points are relatively sparse, they are not adequate to delineate the object and provide a comprehensive matching set that covers the complete object. Region features provide a high degree of coverage and are employed for continuous frames to provide a delineation of the vehicle region for subsequent generation of a match measure. Our approach reliably delineates objects by representing regions as robust blob features and matching multiple regions to multiple regions using Earth Mover's Distance (EMD). Extensive experimentation under a variety of real-world scenarios and over hundreds of thousands of Confirmatory Identification (CID) trails has demonstrated about 95 percent accuracy in vehicle reacquisition with both visible and Infrared (IR) imaging cameras.

This paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. The key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. A novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. The individual processes employ a single objective function that is optimized using two nested AdaBoost loops. The outer AdaBoost loop is used to select discriminative exemplars and the inner AdaBoost is used to select discriminative features on the selected exemplars. The proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. Also, our approach fully complies with the standard AdaBoost-based object detection framework in terms of real-time implementation. Experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.

This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.

We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the Locality Sensitive Hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.

Visual recognition of objects through multiple observations is an important component of object tracking. We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association. We employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. The challenges of change in pose, aspect and appearances across two disparate observations are handled by combining feature-based quasi-rigid alignment with flexible matching between two or more sequences. The current work uses the domain of vehicle tracking from aerial platforms where typically both the imaging platform and the vehicles are moving and the number of pixels on the object are limited to fairly low resolutions. Extensive evaluation with respect to ground truth is reported in the paper.

This paper proposes a method for matching road vehicles between two non-overlapping cameras. The matching problem is formulated as a same-different classification problem: probability of two observations from two distinct cameras being from the same vehicle or from different vehicles. We employ a measurement vector consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each match measure in the final decision is determined by a unsupervised learning process so that the same-different classification can be optimally separated in the combined measurement space. The robustness of the match measures and the use of discriminant analysis in the classification ensure that the proposed method performs better than existing edge-based approaches, especially in the presence of missing/false edges caused by shadows and different illumination conditions, and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of over 200 vehicles at different times of day demonstrate promising results.

We present a fully integrated real-time system to track humans with a network of stereo sensors over a wide area. The processing includes single camera tracking and multi-camera fusion. Each single camera detects and tracks humans in its own view and a multi-camera fusion module combines all the local tracks of the same human into a global track. We propose stereo segmentation and tracking techniques to handle multiple humans moving in groups in cluttered environments. We have developed a ground-based fusion method for camera handoff using space-time constraint. We show results and performance evaluation on very challenging data from a 12-camera system.

We propose a novel method for identifying road vehicles between two nonoverlapping cameras. The problem is formulated as a same-different classification problem: probability of two vehicle images from two distinct cameras being from the same vehicle or from different vehicles. The key idea is to compute the probability without matching the two vehicle images directly, which is a process vulnerable to drastic appearance and aspect changes. We represent each vehicle image as an embedding amongst representative exemplars of vehicles within the same camera. The embedding is computed as a vector each of whose components is a nonmetric distance for a vehicle to an exemplar. The nonmetric distances are computed using robust matching of oriented edge images. A set of truthed training examples of same-different vehicle pairings across the two cameras is used to learn a classifier that encodes the probability distributions. A pair of the embeddings representing two vehicles across two cameras is then used to compute the same-different probability. In order for the vehicle exemplars to be representative for both cameras, we also propose a method for jointly selection of corresponding exemplars using the training data. Experiments on observations of over 400 vehicles under drastically illumination and camera conditions demonstrate promising results.

Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos. Such a representation provides an intermediate description that enables applications such as object tracking, video summarization and visualization, video insertion, and sprite-based video compression. Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations. The temporal coherency of motion layers and the domain constraints on shapes have not been exploited. This paper introduces a complete dynamic motion layer representation in which spatial and temporal constraints on shape, motion and layer appearance are modeled and estimated in a maximum a-posteriori (MAP) framework using the generalized expectation-maximization (EM) algorithm. In order to limit the computational complexity of tracking arbitrarily shaped layer ownership, we propose a shape prior that parameterizes the representation of shape and prevents motion layers from evolving into arbitrary shapes. In this work, a Gaussian shape prior is chosen to specifically develop a near-real-time tracker for vehicle tracking in aerial videos. However, the general idea of using a parametric shape representation as part of the state of a tracker is a powerful one that can be extended to other domains as well. Based on the dynamic layer representation, an iterative algorithm is developed for continuous object tracking over time. The proposed method has been successfully applied in an airborne vehicle tracking system. Its performance is compared with that of a correlation-based tracker and a motion change-based tracker to demonstrate the advantages of the new method. Examples of tracking when the backgrounds are cluttered and the vehicles undergo various rigid motions and complex interactions such as passing, turning, and stop-and-go demonstrate the strength of the complete dynamic layer representation.

This paper presents a method for detecting building outlines using multiple aerial images. Since data-driven techniques may not be able to account for variability of building geometry and appearances, a key insight explored in this paper is a combination of model-based data driven front end with data driven learning in the back end for increased detection accuracy. The three main components of the detection algorithm are: (i) initialization. Image intensity and depth information are integrally used to efficiently detect buildings, and a robust rectilinear path finding algorithm is adopted to obtain good initial outlines. The initialization process involves the following steps: detecting location of buildings, determining the dominant orientations and knot points in the building outline and using these to fit the initial outline; (ii) learning. A compact set of building features are defined and learned from the well-delineated buildings, and a tree-based classifier is applied to the whole region to detect any missing buildings and obtain their rough outlines; and (iii) verification and refinement. Learned features are used to remove falsely detected buildings, and all outlines are refined by the deformation of rectilinear templates. The experiments, with improved detection rate and precise outlines, demonstrate the applicability of our algorithm.

This paper addresses the problem of extracting depth information of non-rigid dynamic 3D scenes from multiple synchronized video streams. Three main issues are discussed in this context. (i) temporally consistent depth estimation, (ii) sharp depth discontinuity estimation around object boundaries, and (iii) enforcement of the global visibility constraint. We present a framework in which the scene is modeled as a collection of 3D piecewise planar surface patches induced by color based image segmentation. This representation is continuously estimated using an incremental formulation in which the 3D geometric, motion, and global visibility constraints are enforced over space and time. The proposed algorithm optimizes a cost function that incorporates the spatial color consistency constraint and a smooth scene motion model.

An algorithm is presented for video georegistration, with a particular concern for aerial video, i.e., video captured from an airborne platform. The algorithm's input is a video stream with telemetry (camera model specification sufficient to define an initial estimate of the view) and geodetically calibrated reference imagery (coaligned digital orthoimage and elevation map). The output is a spatial registration of the video to the reference so that it inherits the available geodetic coordinates. The video is processed in a continuous fashion to yield a corresponding stream of georegistered results. Quantitative results of evaluating the developed approach with real world aerial video also are presented. The results suggest that the developed approach may provide valuable input to the analysis and interpretation of aerial video.

This paper introduces a novel application of IBR technology for efficient rendering of high quality CG and live action stereoscopic sequences. Traditionally, IBR has been applied to render novel views using image and depth based representations of the plenoptic functions. In this work, we present a restricted form of IBR in which lower resolution images for the views to be generated at a very high resolution are assumed to be available. Specifically, the paper addresses the problem of synthesizing stereo IMAX(R)1 3D motion picture images at a standard resolution of 4-6K. At such high resolutions, producing CG content is extremely time consuming and capturing live action requires bulky cameras. We propose a Hybrid Stereo Camera concept in which one view is rendered at the target high resolution but the other is rendered at a much lower resolution. Methods for synthesizing the second view sequence at the target resolution using image analysis and IBR techniques are the focus of this work. The high quality results from the techniques presented in this paper have been visually evaluated in the IMAX 3D large screen projection environment. The paper also highlights generalizations and extensions of the hybrid stereo camera concept.

There is growing interest in performing aerial surveillance using video cameras. Compared to traditional framing cameras, video cameras provide the capability to observe ongoing activity within a scene and to automatically control the camera to track the activity. However, the high data rates and relatively small field of view of video cameras present new technical challenges that must be overcome before such cameras can be widely used. In this paper, we present a framework and details of the key components for real-time, automatic exploitation of aerial video for surveillance applications. The framework involves separating an aerial video into the natural components corresponding to the scene. Three major components of the scene are the static background geometry, moving objects, and appearance of the static and dynamic components of the scene. In order to delineate videos into these scene components, we have developed real time, image-processing techniques for 2-D/3-D frame-to-frame alignment, change detection, camera control, and tracking of independently moving objects in cluttered scenes. The geo-location of video and tracked objects is estimated by registration of the video to controlled reference imagery, elevation maps, and site models. Finally static, dynamic and reprojected mosaics may be constructed for compression, enhanced visualization, and mapping applications.

The article presents methods for 3D scene geometry recovery/refinement and pose estimation from motion imagery in two representative scenarios. First, we present a method for pose estimation and scene geometry recovery from extended sequences without prior knowledge of the scheme. Second, we discuss how to recover camera poses when a rough scene model is provided. We show how to extend and refine the scene model using the recovered poses. Finally, we present applications of the above techniques for 3D imagery manipulation such as enhanced visualization for video and 3D insertion of synthetic objects in the imagery.

In this paper we present methods for exploitation and enhanced visualization of video given a prior coarse untextured polyhedral model of a scene. Since it is necessary to estimate the 3D poses of the moving camera, we develop an algorithm where tracked features are used to predict the pose between frames and the predicted poses are refined by a coarse to fine process of aligning projected 3D model line segments to oriented image gradient energy pyramids. The estimated poses can be used to update the model with information derived from video, and to re-project and visualize the video from different points of view with a larger scene context. Via image registration, we update the placement of objects in the model and the 3D shape of new or erroneously modeled objects, then map video texture to the model. Experimental results are presented for long aerial and ground level videos of a large-scale urban scene.

A dynamic layer representation is proposed for tracking moving objects. Previous work on layered representations has largely concentrated on two-/multi-frame batch formulations, and tracking research has not addressed the issue of joint estimation of object motion ownership and appearance. The paper extends the estimation of layers in a dynamic scene to incremental estimation formulation and demonstrates how this naturally solves the tracking problem. The three components of the dynamic layer representation, namely, layer motion, ownership, and appearance, are estimated simultaneously over time in a MAP framework. In order to enforce a global shape constraint and to maintain the layer segmentation over time, a parametric segmentation prior is proposed. The generalized EM algorithm is employed to compute the optimal solution. We show the results on real-time tracking of multiple moving or static objects in a cluttered scene imaged from a moving aerial video camera. The moving objects may do complex motions, and have complex interactions such as passing. By using both the appearance and the segmentation information, many difficult tracking tasks are reliably handled.

We present a set of automatic methods for the recovery and refinement of 3D scene geometry and camera poses from motion imagery. First, we present a two-frame "direct" method, which simultaneously estimates both relative pose between the cameras and 3D scene geometry using information from the images alone. Second, we discuss how to automatically estimate pose and scene geometry using extended sequences. Third, we present methods for recovery of pose when a scene model is known a priori. We show how the recovered pose can be use to extend and refine the scene model. Finally, we present applications of the above techniques for 3D imagery manipulation such as enhanced visualization of video, synthetic view generation and insertion of synthetic objects in the imagery.

We present methods for estimating precise geo-coordinates of objects observed in highly oblique video from an airborne camera in real time. High precision is achieved by registering observed video frames in real time to rendered views of the scene using stored reference imagery. The reference imagery includes previously collected satellite images and terrain maps that have been precisely aligned to geo-coordinates. We present methods that work for both nadir and highly oblique video imagery and under a variety of conditions where any one frame may not have enough information for accurate geo-registration.

This paper presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: 1) two/multiview camera motion constraint (for instance, the epipolar/trilinear constraint) and 2) shape constancy constraint. Previous approaches to the problem either use only partial constraints, or rely on dense correspondences or flow. We employ both the fundamental constraints in an algorithm that does not demand a priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated that for a class of scenes, called sparse 3D scenes in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction, and verification of the fundamental constraints. Results of the algorithm on some difficult sparse 3D scenes are promising.

Presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: (i) a two- (or multi-)view camera motion constraint (for instance, the epipolar/trilinear constraint), and (ii) a shape constancy constraint. Previous approaches to the problem either only used partial constraints or relied on dense correspondences or flow. We employ both of these fundamental constraints in an algorithm that does not demand a-priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated, for a class of scenes called sparse 3D scenes, in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction and verification of the fundamental constraints. The results of applying the algorithm to some difficult sparse 3D scenes look promising.

Multiple images of a scene are related through 2D/3D view transformations and linear and nonlinear camera transformations. We present an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. The algorithm is developed to specifically align and mosaic images using parametric transformations in the presence of lens distortion. When lens distortion is present, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and nonlinear transformations within the same framework. Results of applying the algorithm are presented for the correction of lens distortion, and creation of video mosaics.

We present a validation study of an optical-flow method for the rapid estimation of myocardial displacement in magnetic resonance tagged cardiac images. This registration and change visualization (RCV) software uses a hierarchical estimation technique to compute the flow field that describes the warping of an image of one cardiac phase into alignment with the next. This method overcomes the requirement of constant pixel intensity in standard optical-flow methods by preprocessing the input images to reduce any intensity bias which results from the reduction in stripe contrast throughout the cardiac cycle. To validate the method, SPAMM-tagged images were acquired of a silicon gel phantom with simulated rotational motion. The pixel displacement was estimated with the RCV method and the error in pixel tracking was <4% 1000 ms after application of the tags, and after 30 degrees of rotation. An additional study was performed using a SPAMM-tagged multiphase slice of a canine left ventricle. The true displacement was determined using a previously validated active contour model (snakes). The error between methods was 6.7% at end systole. The RCV method has the advantage of tracking all pixels in the image in a substantially shorter period than the snakes method.

The ability to locate scenes and objects visible in aerial video imagery with their corresponding locations and coordinates in a reference coordinate system is important in visually-guided navigation, surveillance and monitoring systems. However, a key technical problem of locating objects and scenes in a video with their geo-coordinates needs to be solved in order to ascertain the geo-location of objects seen from the camera platform's current location. We present the key algorithms for the problem of accurate mapping between camera coordinates and geo-coordinates, called geo-spatial registration. Current systems for geo-location use the position and attitude information for the moving platform in some fixed world coordinates to locate the video frames in the reference database. However, the accuracy achieved is only of the order of 100s of pixels. Our approach utilizes the imagery and terrain information contained in the geo-spatial database to precisely align dynamic videos with the reference imagery and thus achieves a much higher accuracy. Applications of geo-spatial registration include aerial mapping, target location and tracking and enhanced visualization such as the overlay of textual/graphical annotations of objects of interest in the current video using the stored annotations in the reference database.

The technical foundation for many applications of computer vision to multimedia applications is efficient and robust image motion estimation. These algorithms enable the creation of algorithms for mosaic construction, registration of video to a database multi-sensor registration, 3D estimation and representation, and video content indexing and retrieval. Demonstrations on the above topics will be shown at WACV'98 by the Media Vision Group of Sarnoff Corporation.

We present technical advances that were made in creating consumer level video mosaicing applications with inputs from hand held inexpensive cameras, and with near-real time response. In developing the VideoBrush/sup TM/ technologies, the goal was to give easily usable but robust video mosaicing capabilities in the hands of lay users. We highlight a number of problems that needed to be solved to enable this: 2D & 1D modes of scene scanning and alignment, progressive complexity of alignment models to provide real-time response to the user and multi-resolution blending for high quality mosaic output. The solution to these key technical challenges has led us to video mosaic technologies that can "scan" scenes as diverse as natural panoramic scenes to white boards and documents.

Multiple images of a scene are related through 2D/3D view transformations and linear and non-linear camera transformations. In all the traditional techniques to compute these transformations, especially the ones relying on direct intensity gradients, one image and its coordinate system have been assumed to be ideal and distortion free. In this paper, we present a formulation and an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, in the presence of lens distortion, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and non-linear transformations within the same framework. The new algorithm is evaluated in the context of two applications: (i) correction of lens distortion, and (ii) creation of video mosaics.

In this paper we present a framework for efficient representation, access, and manipulation of video data. Our approach is based on decomposing video information into its spatial (appearance), temporal (dynamics), geometric components. This derived information is organized into data representations that support non-linear browsing and efficient indexing to provide rapid access directly to the information of interest.

The two fundamental advantages of video over still imagery are: (i) the ability capture temporal information, and (ii) the ability to acquire a continuously varying set of views of a scene. These advantages are obtained, however, at the cost of vastly increased amount of data. This paper describes an approach to video representation that is based on frame-to-frame alignment, mosaic construction, and 3D parallax recovery. The basic motivation behind our approach is to enable rapid access to the contents, while maintaining the data in a form as close to the source as possible. This representation supports a wide variety of applications that involve transmission, storage, visualization, retrieval, analysis, and manipulation of video sequences.

Given two arbitrary views of a scene under central projection, if the motion of points on a parametric surface is compensated, the residual parallax displacement field on the reference image is an epipolar field. If the surface aligned is a plane, the parallax magnitude at an image point is directly proportional to the height of the point from the plane and inversely proportional to its depth from the camera. The authors exploit the above theorem to infer 3D height information from oblique aerial 2D images. The authors use direct methods to register the aerial images and develop methods to infer height information under the following three conditions: (i) focal length and image center are both known, (ii) only the focal length is known, and (iii) both are unknown.

In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.>

Automatic building 3D models of objects and scenes from a sequence of 2D monocular images is approached by first building a partial model (possibly noisy) and then extending and refining it. The initial model is built by tracking and reconstructing shallow structures over a sequence of images using the constraint of affine trackability. This model is subsequently used to compute the pose that relates the model coordinate system and the camera coordinate system of the image frames in the sequence. The unmodeled 3D features (those not recovered by the shallow structure reconstruction) are tracked over the image sequence and their 3D locations recovered by a pseudotriangulation process. The triangulation process is also used to make new 3D measurements of the initial model points. These measurements are then fused with the previous estimates to refine the set of initial model points.>

A study is made of the effect of errors in estimates of the image center and focal length on pose refinement and other related (3-D inference from 2-D images) problems/algorithms. The authors show that for 'small' field of view imaging systems, incorrect knowledge of the camera center does not affect the determination of the location of the camera significantly. Incorrect estimates of the focal length only significantly affect the determination of the z-component (i.e. parallel to the optical axis) of the translation in camera coordinates. The output of the pose refinement algorithm is used to calculate the relative orientation between the coordinate frames of the same camera in two or more different positions as a prelude to computation of 3-D depths of new points by pseudo-triangulation. A model of error for this depth based on the amount of error in placing the image center conforms to the errors obtained for experiments with synthetic and real data. New points are located to an average accuracy of 1.5 mm and 0.3 ft for the two real image sequences respectively.>

The authors report on the system and methods used by UMass Mobile Robot Project. Model-based processing of the visual sensory data is the primary mechanism used for controlling movement of an autonomous land vehicle through the environment, measuring progress towards a given goal, and avoiding obstacles. Goal-oriented navigation takes place through a partially modeled, unchanging environment that contains no unmodeled obstacles; this simplified environment provides a foundation for research in more complicated domains. The navigation system integrates perception, planning, and execution of actions. Of particular importance is that the planning processes are reactive and reason about landmarks that should be perceived at various stages of task execution. Correspondence between image features and expected landmark locations are used at several abstraction levels to ensure proper plan execution. The system and some experiments that demonstrate the performance of its components is described.>