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Multiple View Geometry in Computer Vision.

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Many algorithms for both identifying and reconstructing a 3-D object are based on the 2-D silhouettes of the object. In general, identifying a nonconvex object using a silhouette-based approach implies neglecting some features of its surface as identification clues. The same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object. This paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette-based image understanding. For this purpose, the geometric concept of visual hull of a 3-D object is introduced. This is the closest approximation of object S that can be obtained with the volume intersection approach; it is the maximal object silhouette-equivalent to S, i.e., which can be substituted for S without affecting any silhouette. Only the parts of the surface of S that also lie on the surface of the visual hull can be reconstructed or identified using silhouette-based algorithms. The visual hull depends not only on the object but also on the region allowed to the viewpoint. Two main viewing regions result in the external and internal visual hull. In the former case the viewing region is related to the convex hull of S, in the latter it is bounded by S. The internal visual hull also admits an interpretation not related to silhouettes. Algorithms for computing visual hulls are presented and their complexity analyzed. In general, the visual hull of a 3-D planar face object turns out to be bounded by planar and curved patches. >

The visual hull concept for silhouette-based image understanding

Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

/pdf/vision-for-mobile-robot-navigation-a-survey-4hb3uw9u9w.pdf

Vision for mobile robot navigation: a survey

A general-purpose computer vision system must be capable of recognizing three-dimensional (3-D) objects. This paper proposes a precise definition of the 3-D object recognition problem, discusses basic concepts associated with this problem, and reviews the relevant literature. Because range images (or depth maps) are often used as sensor input instead of intensity images, techniques for obtaining, processing, and characterizing range data are also surveyed.

Three-dimensional object recognition

This paper describes a computationally inexpensive algorithm for the determination of vanishing points once line segments in an image have been determined. The approach is particularly attractive since it has no computationally degenerate cases and the only operations necessary are vector cross products and arc tangents. The need to know the distance to the focal plane is also eliminated thus avoiding tedious calibration procedures.

Determining vanishing points from perspective images

The perspective view of three noncollinear points whose image-to-object correspondence is known is studied. Such measurements are known to be ambiguous, resulting in as many as four possible solutions to the perspective three-point problem. Although there can be four solutions, it is quite often the case that there are triangle configurations that cause one, two, three, or four solutions. The results also provide a justification for the common wisdom that there are usually two solutions. >

The perspective view of three points

With the advent of devices that can directly sense and determine the coordinates of points in space, the goal of constructing and recognizing descriptors of three-dimensional (3-D) objects is attracting the attention of many researchers in the image processing community. Unfortunately, the time required to fully sense a range image is large relative to the time required to sense an intensity image. Conversely, a single intensity image lacks the depth information required to construct 3-D object descriptors. This paper presents a method of combining the two sensory sources, intensity and range, such that the time required for range sensing is considerably reduced. The approach is to extract potential points of interest from the intensity image and then selectively sense range at these feature points. After the range information is known at these points, a graph structure representing the object in the scene is constructed. This structure is compared to the stored graph models using an algorithm for partial matching. The results of applying the method to both synthetic data and real intensity/range images are presented.

Experiments in Intensity Guided Range Sensing Recognition of Three-Dimensional Objects

Robot guidance using computer vision

Determining motion parameters using intensity guided range sensing

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