This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically-guided re-localisation and cross-view factor graph optimisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate point-to-point navigation in GPS-degraded environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot’s global position and orientation. Additionally, we enhance the repeatability of deep-learned keypoints for metric localisation in forests by incorporating a semantically-guided regression loss. This loss encourages greater attention to wooden structures, e.g., tree trunks, which serve as stable and distinguishable features, thereby improving the consistency of keypoints and increasing the success rate of global registration, a process we refer to as re-localisation. The re-localisation module along with the factor-graph structure, populated by odometry and ground-to-aerial factors over time, allows global localisation under dense canopies. We validate the performance of our method through extensive experiments in three forest scenarios, demonstrating its global localisation capability and superiority over alternative state-of-the-art in terms of accuracy and robustness in these challenging environments. Experimental results show that our proposed method can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation through dense forests.

Autonomous navigation in dense vegetation remains an open challenge and is an area of major interest for the research community. In this paper we propose a novel traversability estimation method, the Forest Traversability Map, that gives autonomous ground vehicles the ability to navigate in harsh forests or densely vegetated environments. The method estimates travers ability in unstructured environments dominated by vegetation, void of any dominant human structures, gravel or dirt roads, with higher accuracy than the state of the art: we demonstrate an improvement of over 20% F1 score (from 0.71 to 0.91) on challenging real-world data. Our method is based on 3D voxel representation and introduces a robust colour fusion method to overcome occlusion and frequent changes of lighting conditions in these environments. We also introduce and fuse multi-return lidar measurements into our probabilistic map representation in a recursive manner. Finally, we include information of neighboring voxels to increase our ability to assess the terrain travers ability correctly. These measures improve the state-of-the-art results and allow for effective traversability estimation in very challenging, densely vegetated environments.

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method.

Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretical motion primitives are desirable and/or achievable. This work presents a method to identify effective motion primitives (eMP) from continuous trajectories for autonomous ground robots. The pipeline efficiently performs segmentation, representation and reconstruction of the motion primitives, using initial human-driving behaviour as a guide to create a motion primitive library. Hence, this strategy incorporates how the environment affects the robot operation regarding accelerations, speed, braking, and steering behaviours.The method is thoroughly tested on an autonomous car-like electric vehicle, and the results show excellent generalisation of the theoretical motion primitive distribution to real vehicle. The experiments are carried out on large site with very diverse characteristics, illustrating the applicability of the method.

Accurate environment representation is one of the key challenges in autonomous ground vehicle navigation in unstructured environments. We propose a real-time optimization-based approach to terrain modeling and path planning in off-road and rough environments. Our method uses an irregular, hierarchical, graph-like environment model. A space-dividing tree is used to define a compact data structure capturing vertex positions and establishing connectivity. The same unique underlying data structure is used for both terrain modeling and path planning without memory reallocation. Local plans are generated by graph search algorithms and are continuously regenerated for on-the-fly obstacle avoidance inside the scope of the local terrain map. We show that implementing a hierarchical model over a regular space division reduces graph edge expansions by up to 84%. We illustrate the applicability of the method through experiments with an unmanned ground vehicle in both structured and unstructured environments.

This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints.

Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and “kidnapped robot” problems, where the robot position needs to be estimated without prior information. Among the different sensors that can be used for the task (e.g., camera, GPS, LiDAR), LiDAR has the advantage of operating in the dark and in GPS-denied areas. We propose a new method that uses solely the LiDAR data and that can be performed without robot motion. In contrast to other methods, our system leverages intensity information (as opposed to only range information) which is encoded into a novel descriptor of LiDAR intensities as a group of histograms, named DELIGHT. The descriptor encodes the distributed histograms of intensity of the surroundings which are compared using chi-squared tests. Our pipeline is a two-stage solution consisting of an intensity-based prior estimation and a geometry-based verification. For a map of 220k square meters, the method achieves localisation in around 3s with a success rate of 97%, illustrating the applicability of the method in real environments.

This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to map-matching that can be integrated into a particle filter framework for localization. Each particle is re-weighted based on the validity of its current position in the map. In addition, we buffer the trajectory followed by the vehicle and then append it to each particle's pose. We then quantify the overlap between the trajectory and the map's free space. This serves as a measure of each particle's validity given the trajectory and the shape of the map. We evaluated the method by performing experiments with different types of localization sensors: First, (i) we significantly reduced the drift inherent to dead reckoning. By only using wheel odometry and map information we achieved loop closure over a distance of approximately 3 km. We also (ii) increased the accuracy of GPS localization. Finally, (iii) we fused a fragile 2D LiDAR localization with the map information. The resulting system had a higher robustness and managed to close the loop in an outdated map where it had failed before.

Reliable long-term localization is key for robotic systems in dynamic environments. In this paper, we propose a novel approach for long-term localization using 3D LiDARs, coined PoseMap. In essence, we extract distinctive features from range measurements and bundle these into local views along with observation poses. The sensor's trajectory is then estimated in a sliding window fashion by matching current and old features and minimizing the distances in-between. The map representation facilitates finding a suitable set of old features, by selecting the closest local map(s) for matching. Similarly to a visibility analysis, this procedure provides a suitable set of features for localization but at a fraction of the computational cost. PoseMap also allows for updates and extensions of the map at any time by replacing and adding local maps when necessary. We evaluate our approach using two platforms both equipped with a 3D LiDAR and an IMU, demonstrating localization at 8 Hz and robustness to changes in the environment such as moving vehicles and changing vegetation. PoseMap was implemented on an autonomous vehicle allowing it to drive autonomously over a period of 18 months through a mix of industrial and unstructured off-road environments, covering more than 100 kms without a single localization failure.

This work introduces a novel hybrid control architecture for a hexapod platform (Weaver), making it capable of autonomously navigating in uneven terrain. The main contribution stems from the use of vision-based exteroceptive terrain perception to adapt the robot's locomotion parameters. Avoiding computationally expensive path planning for the individual foot tips, the adaptation controller enables the robot to reactively adapt to the surface structure it is moving on. The virtual stiffness, which mainly characterizes the behavior of the legs' impedance controller is adapted according to visually perceived terrain properties. To further improve locomotion, the frequency and height of the robot's stride are similarly adapted. Furthermore, novel methods for terrain characterization and a keyframe based visual-inertial odometry algorithm are combined to generate a spatial map of terrain characteristics. Localization via odometry also allows for autonomous missions on variable terrain by incorporating global navigation and terrain adaptation into one control architecture. Autonomous runs on a testbed with variable terrain types illustrate that adaptive stride and impedance behavior decreases the cost of transport by 30 % compared to a non-adaptive approach and simultaneously increases body stability (up to 88 % on even terrain and by 54 % on uneven terrain). Weaver is able to freely explore outdoor environments as it is completely free of external tethers, as shown in the experiments.

The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VLP-16 LiDAR and an IMU are mounted on an autonomous, full size utility vehicle and used for localization within a previously created base map. We redesign a six degrees of freedom LiDAR SLAM algorithm to achieve 3D localization on the base map, as well as real-time vehicle navigation. We fuse the low-frequency, high precision SLAM updates with high-frequency, odometric local state estimates from the vehicle. The navigation costmap consists of a 2D occupancy grid which is computed from the 3D base map. Relying on this setup, the vehicle is capable of navigating through a complex site completely autonomously. The test site has densely and sparsely built areas, bushland, industrial activities, pedestrians, and other manned or unmanned vehicles. Extensive testing was done using both current and outdated base maps for comparisons, and a high precision RTK-GPS was used for ground truth. So far, more than 60 km of completely autonomous driving has been performed without a single system or navigation failure.

Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that can potentially be suitable for the problem usually target roads with clearly defined curbs and sidewalks. We propose a real-time algorithm for the detection of low obstacles (including, but not restricted to curbs), merging 2-D and 3-D information from stereo imaging. A set of candidate object lines is extracted based on their combined 2-D and 3-D features, tracked over time and clustered according to a novel similarity metric. Finally, a 3rdorder polynomial spline is fitted to each cluster to represent the obstacle. The proposed system can deal with noisy and incomplete point clouds and keeps the model assumptions to a minimum. To evaluate the algorithm, a new stereo dataset is provided and made available online. We present experiments in different scenarios and lighting conditions, illustrating the applicability of the method.

The use of cameras as a sensor for odometry estimation is an active research topic that has seen significant growth in recent years. Most methods, however, are only suitable for standard cameras that rely on reasonable lighting. An alternative to overcome low-light conditions is the use of thermal or long-wave infrared imaging. Although visible spectrum and thermal imaging share many characteristics, it is not straightforward to apply standard visual odometry algorithms to thermal imaging data. In this paper, we propose a practical visual odometry system based on a monocular thermal camera. As monocular odometry suffers from an unknown scale factor, the system performs efficient ground plane detection for targeted feature extraction, such that the scale factor can be reliably estimated if the camera height and pitch are known. We also address the problem of periodic nonuniformity correction, which is a necessary characteristic of thermal cameras that freezes the output potentially for over a second and can severely affect motion estimation. In this sense, we automatically determine appropriate times to perform nonuniformity correction based on the current and predicted camera rotations. Experiments illustrate the applicability of the system and compare it with other state estimation approaches.

Efficient estimation of obstacles is a crucial component in autonomous vehicle navigation. Fusing stereo vision and laser scan data is a widely used approach for obstacle detection and navigation. Stereo vision can detect obstacles and the ground. Planar laser range finders can detect obstacles, but false positives occur when the scans collide with the ground. This paper proposes an algorithm for cooperative fusion of stereo vision and laser data. The algorithm checks the consensus of the two signals before filtering out the false positive laser scans from ground hits. Experimental results for tests in real driving situations in an industrial environment are presented. The results confirm the effectiveness of the proposed algorithm for robust and safe navigation.

Efficient obstacle detection is a key requirement for safe robot navigation. We consider the operation of autonomous vehicles in structured industrial environments. In such scenarios, an usual way to perform obstacle detection is to generate an estimate of the ground and detect elements that are on the path of the vehicle, using the ground as a spatial reference. For this task, 3D occupancy grids are a well-know solution. In this work we extrapolate the concept of 3D grids by considering a discrete-continuous representation of the environment. The discrete nature lies in a 2D grid parallel to the ground whereas the continuous aspect represents the height of each cell in the grid. This framework allows for very efficient clustering, for which we also propose a novel algorithm to cluster potential obstacles. Experiments on an autonomous ground vehicle illustrate the applicability of the method.

Efficient pedestrian detection is a key aspect of many intelligent vehicles. In this context, vision-based detection has increased in popularity. Algorithms proposed often consider that the camera is mobile (on board a vehicle) or static (mounted on infrastructure). In contrast, we consider a pedestrian detection approach that uses information from mobile and static cameras jointly. Assuming that the vehicle (on which the mobile camera is mounted) contains some sort of localization capability, combining information from the mobile camera with the static camera yields significantly improved detection rates. These sources are fairly independent, with substantially different illumination and view-angle perspectives, bringing more statistical diversity than a multicamera network observing an area of interest, for example. The proposed method finds applicability in industrial environments, where industrial vehicle localization is becoming increasingly popular. We implemented and tested the system on an automated industrial vehicle, considering both manned and autonomous operations. We present a thorough discussion on practical issues (resolution, lighting, subject pose, etc.) related to human detection in the scenario considered. Experiments illustrate the improved results of the joint detection compared with traditional independent static and mobile detection approaches.

Bayesian network structures are usually built using only the data and starting from an empty network or from a naïve Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers with a very small computational cost, while maintaining most of the intended meaning of the original model.

Image segmentation is a key topic in computer vision, serving as a pre-step in a number of robotics tasks, including object recognition, obstacle avoidance and topological localization. In the literature, image segmentation has been employed as auxiliary information in order to improve optical flow performance. In this work, an alternative approach is proposed, in which optical flow information is used to aid image segmentation, aiming at scene understading for mobile robots. The proposed system performs dense optical flow analysis, followed by clustering of the optical flow vectors in a four dimensional space (formed by the x and y positions, angle and magnitude of each vector). Results from the clustering are used as ‘seeds’ in the segmentation process, performed by watershed segmentation in our implementation. In addition, the flow ‘image’ is combined with the original image, generating an image better suited for watershed segmentation, reducing the local minima effect often seen in this type of segmentation algorithms. The main pipeline considers the use of multi-modality cameras (visible and thermal-infrared). Since they see substantially different information, multi-modality further improves the amount of features of the resulting flows. Experimental results in urban and semi-urban scenarios with efficient segmentation illustrate the applicability of the method.

Vehicle localization is one of the most common tasks in robotics. Using vision as a sensor, most methods perform localization from cameras mounted on board the vehicle. In contrast, we propose a method based on an off-board camera. The system uses a similarity measure between a camera image and a synthetic image generated using a 3-D vehicle model, ideally converging to the true pose of the vehicle. The similarity measure is based on a model of shading appearance, which depends on the surface curvature of the 3-D model. Considering that the area observed by the camera is fairly planar, a rough initial estimation for the position of the object can be obtained using 2-D blob tracking. The 3-D model of the vehicle can be rendered near the vehicle in the real image, and using the proposed similarity measure, it converges to the correct pose. A classification function to discriminate between different vehicles is also proposed, making it possible for the system to identify and track multiple vehicles of interest. A number of experiments are performed with different vehicles outdoors, in a real industrial environment, considering different illumination conditions. The low average error rate compared with a laser-based ground truth illustrates the applicability of the method.

Reliable lane detection is a key component of autonomous vehicles supporting navigation in urban environments. This paper introduces the GOLDIE(Geometric Overture for Lane Detection by Intersections Entirety) system, a vision-based software architecture that uses an on-board single camera to determine the position of road lanes with respect to the vehicle. We propose an efficient vision-based lane-detection system that combines an appearance-based analysis with salient point tracking. The appearance-based analysis consists of segmenting high contrast areas that fit inside a Region-Of-Interest(ROI) on the frame. The salient point tracker selects interesting points based in a reference line, that guides a dynamic ROI. The tracking ROI look for paint lane marks close to the last lane reference found, where road marks are likely to emerge, in order to maintain the usability of the salient point tracker. The tracking is performed with the Lucas-Kanade algorithm and the lane points candidates are selected according to a predefined triangular model. Once such lanes points are detected, the vehicle position is estimated based on the intersection of linearised lanes determined through a vanishing point approach. Experiments and comparisons with other algorithms illustrate the applicability of the method.

Pedestrian detection based on video analysis is a key functionality in automated surveillance systems. In this paper, we present efficient detection metrics that consider the fact that human movement presents distinctive motion patterns. Contrary to several methods that perform an intrablob analysis based on motion masks, we approach the problem without necessarily considering the periodic pixel motion inside the blob. As such, we do not analyze periodicity in the pixel luminances, but in the motion statistics of the tracked blob as a whole. For this, we propose the use of the following cues: 1) a cyclic behavior in the blob trajectory, and 2) an in-phase relationship between the change in blob size and position. In addition, we also exploit the relationship between blob size and vertical position, assuming that the camera is positioned sufficiently high. If the homography between the camera and the ground is known, the features are normalized by transforming the blob size to the real person size. For improved performance, we combine these features using the Bayes classifier. We also present a theoretical statistical analysis to evaluate the system performance in the presence of noise. We perform online experiments in a real industrial scenario and also with videos from well-known databases. The results illustrate the applicability of the proposed features.

Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning, and 3-D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in video-based behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments, initially occluded or obscured dynamic objects can unpredictably enter the path of a vehicle. We propose a system that integrates a vision-based offboard pedestrian tracking subsystem with an onboard localization and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controller's field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. Alternatively, the system can be also applied to vehicles that have autonomous navigation capabilities, in which case, instead of alert lights, the vehicle is halted or redirected. We implemented and tested the proposed solution on an automated industrial vehicle under autonomous operation and on a human-driven vehicle in a full-scale production facility, over a period of four months.

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments initially occluded or obscured dynamic objects can unpre dictably enter the path of a vehicle. We propose a safety system which integrates a vision-based offboard pedestrian tracking subsystem with an onboard localisation and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controllers field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. We implemented and tested the proposed solution on an automated industrial vehicle to verify the applicability for both human drivers and under autonomous operation.

Reliable real-time localization is a key component of autonomous industrial vehicle systems. We consider the problem of using on-board vision to determine a vehicle's pose in a known, but non-static, environment. While feasible technologies exist for vehicle localization, many are not suited for industrial settings where the vehicle must operate dependably both indoors and outdoors and in a range of lighting conditions. We extend the capabilities of an existing vision-based localization system, in a continued effort to improve the robustness, reliability and utility of an automated industrial vehicle system. The vehicle pose is estimated by comparing an edge-filtered version of a video stream to an available 3D edge map of the site. We enhance the previous system by additionally filtering the camera input for straight lines using a Hough transform, observing that the 3D environment map contains only linear features. In addition, we present an automated approach for generating 3D edge maps from laser point clouds, removing the need for manual map surveying and also reducing the time for map generation down from days to minutes. We present extensive localization results in multiple lighting conditions comparing the system with and without the proposed enhancements.

Automated fire detection is an active research topic in computer vision. In this paper, we propose and analyze a new method for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. In the latter case, there are large variations in fire and background characteristics depending on the video instance. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. For edited newscast videos, the fire region is usually located in the center of the frames. This fact is used to model the probability of occurrence of fire as a function of the position. Experiments illustrated the applicability of the method.

In this paper we propose a new image event detection method for identifying flood in videos. Traditional image based flood detection is often used in remote sensing and satellite imaging applications. In contrast, the proposed method is applied for retrieval of flood catastrophes in newscast content, which present great variation in flood and background characteristics, depending on the video instance. Different flood regions in different images share some common features which are reasonably invariant to lightness, camera angle or background scene. These features are texture, relation among color channels and saturation characteristics. The method analyses the frame-to-frame change in these features and the results are combined according to the Bayes classifier to achieve a decision (i.e. flood happens, flood does not happen). In addition, because the flooded region is usually located around the lower and middle parts of an image, a model for the probability of occurrence of flood as a function of the vertical position is proposed, significantly improving the classification performance. Experiments illustrated the applicability of the method and the improved performance in comparison to other techniques.

This paper proposes the use of higher order statistical moments in document image processing to improve the performance of systems which transmit side information through the print and scan channel. Examples of such systems are multilevel 2-D bar codes and certification via text luminance modulation. These systems print symbols with different luminances, according to the target side information. In previous works, the detection of a received symbol is usually performed by evaluating the average luminance or spectral characteristics of the received signal. This paper points out that, whenever halftoning algorithms are used in the printing process, detection can be improved by observing that third and fourth order statistical moments of the transmitted symbol also change, depending on the luminance level. This work provides a thorough analysis for those moments used as detection metrics. A print and scan channel model is exploited to derive the relationship between the modulated luminance level and the higher order moments of a halftone image. This work employs a strategy to merge the different moments into a single metric to achieve a reduced detection error rate. A transmission protocol for printed documents is proposed which takes advantage of the resulting higher robustness achieved with the combined detection metrics. The applicability of the introduced document image analysis approach is validated by comprehensive computer simulations.

This paper improves the use of text color modulation (TCM) as a reliable text document data hiding method. Using TCM, the characters in a document have their color components modified (possibly unperceptually) according to a side message to be embedded. This work presents a detection metric and an analysis determining the detection error rate in TCM, considering an assumed print and scan (PS) channel model. In addition, a perceptual impact model is employed to evaluate the perceptual difference between a modified and a non-modified character. Combining this perceptual model and the results from the detection error analysis it is possible to determine the optimum color modulation values. The proposed detection metric also exploits the orientation characteristics of color halftoning to reduce the error rate. In particular, because color halftoning algorithms use different screen orientation angles for each color channel, this is used as an effective feature to detect the embedded message. Experiments illustrate the validity of the analysis and the applicability of the method.

This paper proposes a new coding method that increases significantly the signal-to-watermark ratio in document watermarking algorithms. A possible approach to text document watermarking is to consider text characters as a data structure consisting of several modifiable features such as size, shape, position, luminance, among others. In existing algorithms, these features can be modified sequentially according to bit values to be embedded. In contrast, the solution proposed here uses a positional information coding approach to embed information. Using this approach, the information is related to the position of modified characters, and not to the bit embedded on each character. This coding is based on combinatorial analysis and it can embed more bits in comparison to the usual methods, given a distortion constraint. An analysis showing the superior performance of positional coding for this type of application is presented. Experiments validate the analysis and the applicability of the method.

This paper uses higher order statistical moments to improve the detection in systems which transmit side information through the print and scan channel, such as 2-D bar codes and text luminance watermarking. When these systems print symbols using halftoning, symbol detection can be performed by evaluating second, third, and fourth order statistical moments of the transmitted symbol, in addition to the average luminance level. A print and scan channel model is described. The relationship between the modulated luminance and the higher order moments of a halftone image is analyzed. The detection error rate is reduced by merging the different moments into a single metric. Experiments validate the analyses and the applicability of the method.

This paper analyzes the use of text halftone modulation (THM) as a text hardcopy watermarking method. Using THM, text characters in a document have their luminances modified from the standard black to a gray level generated with a given halftone screen, according to a message to be transmitted. The application of THM has been discussed in ((R. Villan et al., 2006), (K. Matsuit and K. Tanaka, 1994)). In this paper, a spectral metric is proposed to detect the embedded message. Based on this metric, an error rate analysis of halftone modulation is presented considering the effects of the print and scan channel. Experiments validate the analysis and the applicability of the method.

This work presents an efficient post-segmentation method for separating text from the background in document images. For this task, this paper proposes the use of textured patterns to represent text in documents, instead of the standard black. It is shown that, in poor quality documents, text segmentation is more efficient when the characters in the document are represented in a halftoned gray level prior to printing. This occurs because the halftoning process induces statistical characteristics that help the text to be distinguished from noise or background. A typical case are noisy printed and scanned documents. Experiments validate the analysis and the applicability of the segmentation method. An important application for the method is in the postal service, where letters have their addresses segmented for automatic sorting.

In this paper we propose a new method to embed hidden data in office-like documents, presenting robustness to the print-scan channel. The method slightly modulates the luminance of the documents' characters and symbols, being this change unnoticeable to the human eye, yet detectable by a scanner. It is based on pulse amplitude modulation communications, using "character shaped" pulses instead of traditional pulses, using text spacing to avoid intersymbol interference. We present an error analysis for the method, along with a new print-scan channel model for text documents. The analyses and applicability of the method are validated by experiments

In this work, we propose an informed positional coding scheme by extending the position based watermarking approach (Borges et al. (2003)). In contrast to the traditional watermarking approaches where the information is carried by the watermark itself, in the proposed positional approach, the information is related to the position of the embedded watermarks in the image. Moreover, we propose an optimization criterion to select the best among a few available positions to embed a watermark to represent information and also the best watermark from a codebook of watermarks. Results illustrate the better robustness and transparency performance when compared to the traditional spread spectrum techniques. Moreover, we show that the method presents better robustness to collusion attacks, as illustrated by experiments.