9319153 Zhang This research studies an approach to perceptual grouping in computer vision, using as a basis a theory of position-frequency analysis. Perceptual grouping organizes local features of an input image, such as pixels, line-segments, and regions, into perceptually salient structures. Successful perceptual grouping can lead to a drastic reduction of the search space and significant performance improvements for various computer vision and image processing applications, such as object recognition, image analysis, and active vision. The main difficulty in perceptual grouping, however, lies in the lack of understanding of how perceptual grouping should be formulated as a computational problem under a proper data representation. The recent success of time-frequency analysis in signal processing has motivated the use of its extension, position-frequency analysis, in computer vision. Position-frequency representations can be used to model a wide range of image features, local and global. It also provides new insights on how perceptual grouping can be formulated as a computational problem. The objectives of this research project are to: 1.) Develop a general mathematical framework for perceptual grouping based on the theory of position-frequency analysis, 2.) Investigate a number of fundamental problems in perceptual grouping using this framework and developing grouping algorithms, 3.) Test these algorithms in practical (e.g., industrial) applications.
