This paper presents necessary and sufficient conditions for uniquely determining the motion and structure of a planar surface from any number of monocular images. The authors first show an uncertain situation for the two-view plane motion problem in which an infinite number of solutions may result. Although this situation rarely occurs in real situations, it does raise visual ambiguities under certain circumstances, e.g., when some images are observed through a mirror. The understanding of this situation therefore helps analyze reflected images which are often seen in daily life. Then, the authors present necessary and sufficient conditions for a unique solution of plane motion and structure from any number of views. These algorithm-independent conditions enhance understanding about the problem of estimating plane motion and structure from image sequences and may be used to determine the uniqueness of the solution of the motion and structure of a planar surface.>

This paper discusses detection and matching of arbitrary image features or patterns. The common characteristics of feature extraction and matching are summarized which show that they can be considered as special cases of a more general problem—signal detection. However, the existing signal detection theories do not solve feature extraction and matching problems readily. Therefore, a general formulation of feature extraction and matching as a problem of signal detection is presented. This formulation unifies feature extraction and matching into a more general framework so that the two can be better integrated to form an automatic system for image matching or object recognition. Following this formulation, guidelines for designing algorithms for detection or matching of arbitrary image features or patterns which can be easily implemented or reconfigurated for many practical applications are derived. Sample algorithms resulting from this formulation and the associated experimental results with real image data are provided which demonstrate the performance and robustness of the methods.

This correspondence presents a matching algorithm for obtaining feature point correspondences across images containing rigid objects undergoing different motions. First point features are detected using newly developed feature detectors. Then a variety of constraints are applied starting with simplest and following with more informed ones. First, an intensity-based matching algorithm is applied to the feature points to obtain unique point correspondences. This is followed by the application of a sequence of newly developed heuristic tests involving geometry, rigidity, and disparity. The geometric tests match two-dimensional geometrical relationships among the feature points, the rigidity test enforces the three dimensional rigidity of the object, and the disparity test ensures that no matched feature point in an image could be rematched with another feature, if reassigned another disparity value associated with another matched pair or an assumed match on the epipolar line. The computational complexity is proportional to the numbers of detected feature points in the two images. Experimental results with indoor and outdoor images are presented, which show that the algorithm yields only correct matches for scenes containing rigid objects.>

Presents a model-based algorithm for estimating motion from monocular image sequences. The authors first present a two-view motion algorithm and then extend it to multiple views. The two-view algorithm requires generally 6 pairs of point correspondences to give unique solution of the motion parameters. However, when the used points lie on a Maybank quadric, the algorithm requires 7 pairs of point correspondences to give double solutions. Object-centered motion representations and a motion model of constant acceleration are used to estimate motion parameters from long image sequences. The algorithm guarantees globally optimal solution. Since the algorithm does not involve structure parameters, it contains the least number of unknowns and is hence more efficient and robust than the existing ones. Experimental results with real image data are presented. The same method can be applied to solve for motions described by second or higher orders of polynomials.>

Several sufficient conditions are presented for a double or unique solution of the problem of motion and structure from two monocular images. It is shown that: five correspondences of points that do not lie on two lines in the image plane suffice to determine a pure rotation uniquely; six correspondences of points that do not lie on two lines in the image plane and do not correspond to space points lying on a specific quadric surface suffice to determine a motion with nonzero translation uniquely; each Maybank quadric can sustain at most two physically acceptable motion solutions and surface interpretations, provided that a sufficient number of correspondences are present; in the plane motion case, six correspondences of points that do not lie on a quadric curve in the image plane will only admit the true motion and structure and their duals as solutions. Several properties of the essential matrix T*R and the plane motion matrix R+TN/sup T/, both of which are frequently used in the motion and structure estimation problem, are listed.>

Some new results for the problem of motion estimation under orthographic projection are presented. Some basic results obtained by previous researchers are refined and more detailed and precise results are provided. It is shown that, in the two-view problem, when the rotation is around the optical axis, the motion (but not the structure) is uniquely determined. In the three-view problem, only under certain conditions are the motion and structure uniquely determined. For any motion problem, if two-view matching cannot determine the motion, only under certain conditions can three-view or multiview matching help.>