Grouping-based nonadditive verification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 20, Issue 2, 1998, Pages 186-192

  Amir, Arnon ^a,b   Lindenbaum, Michael ^b  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Graph clustering; Hypothesis verification; Kullback leibler distance; Maximum likelihood; Object recognition perceptual grouping

Indexed keywords

FEATURE EXTRACTION; GRAPH THEORY; IMAGE ANALYSIS; PROBABILITY; STATISTICAL METHODS;

GROUPING-BASED NONADDITIVE VERIFICATION; HYPOTHESIS VERIFICATION; KULLBACK-LEIBLER DISTANCE; MAXIMUM LIKELIHOOD; PERCEPTUAL GROUPING;

OBJECT RECOGNITION;

EID: 0032003801     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.659936     Document Type: Article

References (20)

1. A. Amir and M. Lindenbaum A Generic Grouping Algorithm and Its Quantitative Analysis Trans. Pattern Analysis and Machine Intelligence vol. 20 no. 2 pp. 168-185 Feb. 1998.
2. A. Amir and M. Lindenbaum Grouping-Based Non-Additive Verification Technical Report CIS-9518 Technion Israel Oct. 1995.
3. A. Amir and M. Lindenbaum Quantitative Analysis of Grouping Processes ECCV-96 vol. 1 pp. 371-384 Cambridge 1996.
4. T.M. Breuel Higher-Order Statistics in Visual Object Recognition Technical Report 93-02 IDIAP June 1993.
5. T.M. Cover Elements of Information Theory. New York: WileyInterscience 1991.
6. R.O. Duda and P.E. Hart Use of the Hough Transform to Detect Lines and Curves in Pictures Comm. ACM vol. 15 no. 1 pp. 11151972.
7. W.E.L. Crimson and D.P. Huttenlocher On the Verification of Hypothesized Matches in Model-Based Recognition Trans. Pattern Analysis and Machine Intelligence vol. 13 no. 12 pp. 12011213 Dec. 1991.
8. W.E.L. Crimson and D.P. Huttenlocher eds. Special issues on the interpretation of 3D scenes Trans. Pattern Analysis and Machine Intelligence vol. 13 no. 10 Oct. 1990 and vol. 14 no. 2 Feb. 1991.
9. D. Huttenlocher and S. Ullman Recognizing Solid Objects by Alignment With an Image IJCV vol. 5 no. 2 pp. 195-2121990.
10. M. Lindenbaum An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition Trans. Pattern Analysis and Machine Intelligence vol. 19 no. 11 pp. 12511264 Nov. 1997.
11. [II] M. Lindenbaum Bounds on Shape Recognition Performance Trans. Pattern Analysis and Machine Intelligence vol. 17 no. 7 pp. 666-680 July 1995.
12. D.G. Lowe Perceptual Organization and Visual Recognition. Kluwer Academic Publishers 1985.
13. J. Mundy C. Huang J. Liu W. Huffman D. Forsyth C. Rothwell A. Zisserman S. Utcke and O. Bournez Object Recognition System Based on Geometric Invariants IUW-94 pp. 1393-14021994.
14. J.L. Mundy and A. Zisserman Geometric Invariance in Computer Vision. Cambridge Mass.: MIT Press 1992.
15. A. Papoulis Probability Random Variables and Stochastic Processes. New York: McGraw-Hill 1991.
16. C. Rothwell Reasoning About Occlusions During Hypothesis Verification ECCV-96 vol. 1 pp. 599-609 Cambridge 1996.
17. K.B. Sarachik and W. Crimson Gaussian Error Models for Object Recognition CVPR pp. 400-4061993.
18. S. Sarkar and K.L. Boyer Perceptual Organization in Computer Vision: A Review and Proposal for a Classifactory Structure IEEE Trans. Systems Man and Cybernetics vol. 23 no. 2 pp. 382399 Mar./Apr. 1993.
19. A.P. Witkin and J.M. Tenenbaum On the Role of Structure in Vision J. Beck B. Hope and A. Rosenfeld eds. Human and Machine Vision pp. 481-543. New York: Academic Press 1983.
20. H. Wolfson Model-Based Object Recognition by Geometric Hashing ECCV-90 pp. 526-5361990.