Generalized Shape Autocorrelation

This paper presents an efficient and homogeneous paradigm for automatic acquisition and recognition of nonparametric shapes. Acquisition time varies from linear to cubic in the number of object features. Recognition time is linear to cubic in the number of features in the image and grows slowly with the number of stored models. Nonparametric shape representation is achieved by spatial autocorrelation transforms. Both acquisition and recognition are two-step processes. In the first phase, spatialautocorrelationoperators are applied to the image data to perform local shape analysis. Then, spatial autocorrelation operators are applied to the local shape descriptors to either create entries (acquisition) or index (recognition) into a table containing the distributed shape information. The output of the table is used to generate a density function on the space of possible shapes with peaks corresponding to high confidence in the presence of a particular shape instance. The behavior of the system on a set of complex shapes is shown with respect to occlusion, geometric transformation, and cluttered scenes.

[1]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[2]  Thomas C. Henderson,et al.  CAGD-Based Computer Vision , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Gabriel Taubin,et al.  Nonplanar curve and surface estimation in 3-space , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[4]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[5]  Herbert Freeman,et al.  Characteristic Views As A Basis For Three-Dimensional Object Recognition , 1982, Other Conferences.

[6]  Ruud M. Bolle,et al.  Generalized neighborhoods: a new approach to complex parameter feature extraction , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Peter J. Burt,et al.  Smart sensing within a pyramid vision machine , 1988, Proc. IEEE.

[8]  Andrea Califano,et al.  Feature Recognition Using Correlated Information Contained in Multiple Neighborboods , 1988, AAAI.

[9]  Rodney A. Brooks,et al.  Model-Based Three-Dimensional Interpretations of Two-Dimensional Images , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Thomas C. Henderson,et al.  CAGD-Based Computer Vision , 1988, Defense, Security, and Sensing.

[11]  Dana H. Ballard,et al.  Parameter Networks: Towards a Theory of Low-Level Vision , 1981, IJCAI.

[12]  Ruud M. Bolle,et al.  Visual recognition using concurrent and layered parameter networks , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Takeo Kanade,et al.  Automatic generation of object recognition programs , 1988, Proc. IEEE.

[14]  Robert C. Bolles,et al.  3DPO: A Three- Dimensional Part Orientation System , 1986, IJCAI.

[15]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[16]  Jitendra Malik,et al.  Computing the aspect graph for line drawings of polyhedral objects , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[17]  Michael Brady,et al.  The Curvature Primal Sketch , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Edith Schonberg,et al.  Two-Dimensional, Model-Based, Boundary Matching Using Footprints , 1986 .

[19]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[20]  J. Canny.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[22]  Charles R. Dyer,et al.  Model-based recognition in robot vision , 1986, CSUR.

[23]  Chris Goad,et al.  Special purpose automatic programming for 3D model-based vision , 1987 .