A structural-description-based vision system for automatic object recognition

This paper presents the results of the integration of a proposed part-segmentation-based vision system. The first stage of this system extracts the contour of the object using a hybrid first- and second-order differential edge detector. The object defined by its contour is then decomposed into its constituent parts using the part segmentation algorithm given by Bennamoun (1994). These parts are then isolated and modeled with 2D superquadrics. The parameters of the models are obtained by the minimization of a best-fit cost function. The object is then represented by its structural description which is a set of data structures whose predicates represent the constituent parts of the object and whose arguments represent the spatial relationship between these parts. This representation allows the recognition of objects independently of their positions, orientations, or sizes. It is also insensitive to objects with partially missing parts. In this paper, examples illustrating the acquired images of objects, the extraction of their contours, the isolation of the parts, and their fitting with 2D superquadrics are reported. The reconstruction of objects from their structural description is illustrated and improvements are suggested.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Mohammed Bennamoun,et al.  A contour-based part segmentation algorithm , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Roland T. Chin,et al.  On the Detection of Dominant Points on Digital Curves , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tomaso A. Poggio,et al.  An Optimal Scale for Edge Detection , 1988, IJCAI.

[5]  Pramod K. Varshney,et al.  An information theoretic approach to the distributed detection problem , 1989, IEEE Trans. Inf. Theory.

[6]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  R. W. Rodieck,et al.  Analysis of receptive fields of cat retinal ganglion cells. , 1965, Journal of neurophysiology.

[8]  Bruce J. Schachter,et al.  Decomposition of Polygons into Convex Sets , 1978, IEEE Transactions on Computers.

[9]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[10]  Theodosios Pavlidis,et al.  Decomposition of Polygons into Simpler Components: Feature Generation for Syntactic Pattern Recognition , 1975, IEEE Transactions on Computers.

[11]  Arthur J. Nevins.  Region Extraction from Complex Shapes , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Soo-Chang Pei,et al.  The detection of dominant points on digital curves by scale-space filtering , 1992, Pattern Recognit..

[13]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[14]  Hong Jeong,et al.  Adaptive Determination of Filter Scales for Edge Detection , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Claude L. Fennema,et al.  Velocity determination in scenes containing several moving objects , 1979 .

[16]  Aggelos K. Katsaggelos,et al.  Edge detection using a neural network , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[17]  Takashi Matsuyama.  Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[18]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[19]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[20]  Paul L. Rosin,et al.  Curve segmentation and representation by superellipses , 1995, IEE Proceedings - Vision, Image, and Signal Processing.

[21]  Martin D. Levine,et al.  Vision in Man and Machine , 1985 .

[22]  Pramod K. Varshney,et al.  Distributed Bayesian signal detection , 1989, IEEE Trans. Inf. Theory.

[23]  Mohammed Bennamoun,et al.  An adaptive vision system for automatic object recognition , 1996 .

[24]  Andrew P. Witkin,et al.  Uniqueness of the Gaussian Kernel for Scale-Space Filtering , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  John K. Tsotsos,et al.  Shape representation and recognition from curvature , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Steven W. Zucker,et al.  The Local Structure of Image Discontinuities in One Dimension , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Boualem Boashash,et al.  Optimal parameters for edge detection , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[28]  James J. Clark.  Authenticating Edges Produced by Zero-Crossing Algorithms , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  William H. Press,et al.  Numerical recipes , 1990 .

[31]  Theodosios Pavlidis,et al.  Algorithms for Shape Analysis of Contours and Waveforms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Boualem Boashash,et al.  A vision system for automatic object recognition , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.