In this paper we show that the optical flow, a 2-D field that can be associated with the variation of the image brightness pattern, and the 2-D motion field, the projection on the image plane of the 3-D velocity field of a moving scene, are in general different, unless very special conditions are satisfied. The optical flow, therefore, is ill-suited for computing structure from motion and for reconstructing the 3-D velocity field, problems that require an accurate estimate of the 2-D motion field. We then suggest a different use of the optical flow. We argue that stable qualitative properties of the 2-D motion field give useful information about the 3-D velocity field and the 3-D structure of the scene, and that they can be usually obtained from the optical flow. To support this approach we show how the (smoothed) optical flow and 2-D motion field, interpreted as vector fields tangent to flows of planar dynamical systems, may have the same qualitative properties from the point of view of the theory of structural stability of dynamical systems. () Massachusetts Institute of Technology 1986 This report describes research done within the Artificial Intelligence Laboratory. Support for the A.I. Laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Depart- ment of Defense under Oilice of Naval Research contract N00014-S5-K-0124. Support for this research is also provided by a grant from the Oilice of Naval Research, Engineering Psychology Division and by gift of the Artificial Intelligence Center of Hughes Aircraft Corporation to T. Poggio.

This dissertation presents algorithms for restoring some of the major corruptions observed in archived film or video material. The two principal problems of impulsive distortion (Dirt and Sparkle or Blotches) and noise degradation are considered. There is also an algorithm for suppressing the inter--line jitter common in images decoded from noisy video signals. In the case of noise reduction and Blotch removal the thesis considers image sequences to be three dimensional signals involving evolution of features in time and space. This is necessary if any process presented is to show an improvement over standard two--dimensional techniques. It is important to recognize that consideration of image sequences must involve an appreciation of the problems incurred by the motion of objects in the scene. The most obvious implication is that due to motion, useful three dimensional processing does not necessarily proceed in a direction `orthogonal' to the image frames. Therefore, attention is giv...

Visual processing tasks often require the matching of contours in two images. Examples include determining image motion and matching features for object recognition. We propose a scheme that takes partial constraints on the matching between contours in two images and finds the matches between these contours using local affine transformations.

Through vision, we derive a rich understanding... This article reviews some computational studies of vision, focusing on edge detection, binocular stereo, motion analysis, intermediate vision and object recognition.