The development of sensors capable of obtaining 3D scans of crime scenes is revolutionizing the ways in which crime scenes can be analyzed and at the same time is driving the need for the development of sophisticated tools to represent and store this data. Here we describe the design of a multimedia database suitable for representing and reasoning about crime scene data. The representation is grounded in the physical environment that makes up the crime scene and provides mechanisms for representing both traditional (forms-based) data as well as 3D scan and other complex spatial data.

We propose a new set of highly descriptive local shape descriptors (LSDs) for model-based object recognition and pose determination in input range data. Object recognition is performed in three phases: point matching, where point correspondences are established between range data and the complete model using local shape descriptors; pose recovery, where a computationally robust algorithm generates a rough alignment between the model and its instance in the scene, if such an instance is present; and pose refinement. While previously developed LSDs take a minimalist approach, in that they try to construct low dimensional and compact descriptors, we use high (up to 9) dimensional descriptors as the key to more accurate and robust point correspondence. Our strategy significantly simplifies the computational burden of the pose recovery phase by investing more time in the point matching phase. Experiments with Lidar and dense stereo range data illustrate the effectiveness of the approach by providing a higher percentage of correct matches in the candidate point matches list than a leading minimalist technique. Consequently, the number of RANSAC iterations required for recognition and pose determination is drastically smaller in our approach.

A method is presented for tracking 3D objects as they transform rigidly in space within a sparse range image sequence. The method operates in discrete space and exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. These motion bounds allow the interframe transformation space to be reduced to a reasonable and indeed tiny size, comprising only tens or hundreds of possible states. The tracking problem is in this way cast into a classification framework, effectively trading off localization precision for runtime efficiency and robustness. The method has been implemented and tested extensively on a variety of freeform objects within a sparse range data stream comprising only a few hundred points per image. It has been shown to compare favorably against continuous domain Iterative Closest Point (ICP) tracking methods, performing both more efficiently and more robustly. A hybrid method has also been implemented that executes a small number of ICP iterations following the initial discrete classification phase. This hybrid method is both more efficient than the ICP alone and more robust than either the discrete classification method or the ICP separately.

Photo-realistic 3D modeling is a challenging problem and has been a research topic for many years. Quick generation of photo-realistic three-dimensional calibrated models using a hand-held device is highly desirable for applications ranging from forensic investigation, mining, to mobile robotics. In this paper, we present the instant Scene Modeler (iSM), a 3D imaging system that automatically creates 3D models using an off-the-shelf hand-held stereo camera. The user points the camera at a scene of interest and the system will create a photo-realistic 3D calibrated model automatically within minutes. Field tests in various environments have been carried out with promising results

Guiding the spacecraft docking process requires the use of sensors that estimate the relative position of the two vessels. This task is complicated by the widely variable on-orbit illumination. To combat this, controllable docking cameras are augmented by computer-controlled illuminants. But how should these illumination and capture parameters be controlled and how should the images obtained under different conditions be combined in order to estimate the relative pose of the vessels? We address these issues in the "Lights and Camera" system. Images captured with the same camera and scene geometry but under different lighting conditions are merged, and the resulting edges are used to estimate the target’s pose. A high level controller monitors the imaging process and determines the set of images to capture and use for pose estimation. This paper describes the "Lights and Camera" system architecture and initial results of its operation on mockups of space hardware.

One of the challenges facing computer vision systems used in space is the presence of specular surfaces. Such surfaces lead to several adverse effects such as the creation of reflected "virtual" images of objects due to specular reflections. These effects may lead to incorrect measurements and loss of data in the case of sensor saturation or inadequate intensity of the returned laser beams in the case of an active illuminant. In addition, the instruments inside space structures such as satellites may be extremely sensitive to active illuminants such as laser beams or radar signals, and thus passive vision systems which rely on either natural or low-power projection systems are preferred over active sensing technologies. Here we consider the task of recovering the local surface structure of highly specular surfaces such as satellites using passive stereopsis without resulting to the introduction of additional light source.

Autonomous mobile robots require at least some three dimensional information to navigate in complex and partially unknown environments. Usually it is sufficient to find obstacles in the direction of the planned motion or unobstructed floor space. The distance at which the obstacle detection or floor reconstruction is carried out, determines how far ahead the robot can plan its actions. Covering large space is particularly important in industrial settings and helps in finding quickly right paths and avoiding excessive exploration. In this paper the author presents a novel method of detecting driveable floor regions. The method uses a planar floor model and a vision guided range detection sensor to segment the image into regions and verify which of them belong to the floor. The acceptance criterion uses the sensor error model. The floor detection process requires a relatively small number (in order 25-50) of individual range measurements to plan a safe path for the robot. The author presents experimental results and floor maps created using this method.

In this paper, we track changes in the appearance of the object as it moves from one frame to the next. At a symbolic level, an aspect graph clusters all the views of an object into a set of topologically distinct classes in terms of which surfaces of an object are visible from a given viewpoint (Koenderink and van Doom (1979). Two nodes (or aspects) in the aspect graph are connected by an arc if it is possible to directly move from a viewpoint in which the first aspect is visible to a viewpoint in which the second aspect is visible. Qualitatively, we can envision a tracking strategy which simply tracks an object as it moves from one node to another in the object's aspect graph. Although it does not provide us with accurate pose of the object, it does qualitatively describe the motion of the object without the need for a CAD representation of the object.>

This paper describes research on the ARK (Autonomous Mobile Robot in a Known Environment) project. The technical objective of the project is to build a robot that can navigate and carry out survey/inspection tasks in a complex but known industrial environment. Rather than altering the robots environment by adding easily identifiable beacons the robot relies on naturally occurring objects to use as visual landmarks for navigation. The robot is equipped with various sensors that are used to detect unmapped obstacles, landmarks and objects. This paper describes the robot's industrial environment, it's control architecture, and some results in processing the robot's range and vision sensor data for navigation.>

This paper presents a new method of retinal image registration. The method is based on representing a segmented reference image as an adaptive adjacency graph. The graph consists of a network of active contours, nodes where contours are connected, regions outlined by the contours and their full adjacency relationship. The contours in the graph correspond to retinal vessels or other curvilinear features. The registration is performed by placing the graph on the image to be registered and allowing it to adapt to the image data. The contours move under combined effect of internal and external forces. The internal forces represent contour internal energy. The external forces correspond to image data and to connectivity constraints imposed on the contours. Results of registration obtained for retinal images are presented.>

ARK (Autonomous Robot for a Known environment), is a visually-guided mobile robot which is being constructed as part of the Precarn project in mobile robotics. ARK operates in a previously mapped environment and navigates with respect to visual landmarks that have been previously located. While the robot moves, it utilizes an active vision sensor to register the robot with respect to these landmarks. As the landmarks may be scarce in certain regions of its environment, ARK plans paths which minimize both path length and path uncertainty. The global path planner assumes that the robot will use a Kalman filter to integrate landmark information with odometry data to correct path deviations as the robot moves, and then uses this information to choose a path which reduces the expected path deviation.

An automatic analysis of angiographic retinal images is discussed. A detection method based on textural differences between perfused and nonperfused regions is described. The image is tessellated into a large number of primary regions using the positions of major vessels. For each region the degree of nonperfusion is estimated by measuring textural properties using mathematical morphology. The image is represented as a region adjacency graph. Adjacent regions are subsequently merged using information on the degree of perfusion. Comparison with manual grading is presented. It is shown that the algorithm for tessellating the image into regions conforms well to the manual delineation of nonperfused regions. The only parameter in the algorithm selects what size of vessels are used for region detection. The features used for grading the images are sensitive enough to detect nonperfused regions.>