A robust boundary-based object recognition in occlusion environment by hybrid Hopfield neural networks

Pattern Recognition

Volumn 29, Issue 12, 1996, Pages 2047-2060

  Kim, Jung Hyoun ^a   Yoon, Sung Ho ^b,d   Sohn, Kwanghoon ^c  

^a NORTH CAROLINA A AND T STATE UNIVERSITY   (United States)

^b North Carolina State University   (United States)

^c Yonsei University   (South Korea)

^d NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Boundary representation; Curvature function; Hopfield network; Machine vision; Matching algorithm; Neural network; Object recognition; Occluded object

Indexed keywords

BOUNDARY CONDITIONS; COMPUTATIONAL METHODS; COMPUTER VISION; CURVE FITTING; NEURAL NETWORKS;

BOUNDARY REPRESENTATION; CURVATURE FUNCTION; HOP FIELD NETWORK; MACHINE VISION; MATCHING ALGORITHM; OCCLUDED OBJECT;

OBJECT RECOGNITION;

EID: 0030398036     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(96)00043-X     Document Type: Article

References (27)

1. B. Bhanu and O. D. Faugeras, Shape matching of two-dimensional objects, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 137-155 (1984).
2. W. K. Chow and J. K. Aggarwal, Computer analysis of planar curvilinear moving images, IEEE Trans. Comput. C-26, 179-185 (1987).
3. J. L. Turney, T. N. Mudge and R. A. Volz, Recognizing partially occluded parts, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7, 410-421 (1985).
4. R. C. Bolles and R. A. Cain, Recognizing and locating partially visible objects: The local-feature-focus method, Int. J. Robot. Res. 1, 57-82 (1982).
5. Mark W. Koch and Rangasami L. Kashyap, Using polygon to recognize and locate partially occluded objects, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9, 483-494 (1987).
6. J. H. Kim, S. H. Yoon and K. H. Sohn, Significant point detection and boundary representation with lines and circular arcs, The Fourth Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-91), 694-701 (June 1991).
7. K. E. Price, Matching closed contours, Proc. 7th Int. Conf. Pattern Recognition, 990-992 (July 1984-August 1984).
8. B. Bhanu and John C. Ming, Recognition of occluded objects: A cluster-structure algorithm, IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 199-211 (1987).
9. F. Attneave, Some informational aspects of visual perception, Psychol. Rev. 61(3), 183-193 (1954).
10. KwangHoon Sohn, Jung H. Kim, Sung H. Yoon, Yonghoon Kim, Eui H. Park, C. A. Ntuen and Winser E. Alexander, Optimal boundary smoothing for curvature estimation, Proc. 25th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, 1220-1224 (4-6 November 1991).
11. W. S. Rutkowski, Recognition of occluded shapes using relaxation, Comput. Graphics Image Process. 19, 111-128 (1982).
12. B. Bhanu, Shape matching and image segmentation using stochastic labeling, Image Processing Institute, University of Southern California, (August 1981).
13. R. P. Lippman, An introduction to computing with neural networks, IEEE ASSP 000, 4-22 (1987).
14. M. Takeda and J. W. Goodman, Neural networks for computation: Number representations and programming complexity, Appl. Opt. 25(18), (1986).
15. J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proc. Natl. Acad. Sci. USA, 81, 3088-3092 (May 1984).
16. J. J. Hopfield, Neural networks and physical systems with emergency collective computational abilities, Proc. Nat. Acad. Sci. USA, 79, 2554-2558 (1982).
17. J. J. Hopfield and D. W. Tank, Computing with neural circuits: A model, Science 233, 625-633 (1986).
18. Robert J. Mceliece et al., The capacity of the Hopfield associative memory, IEEE Trans. Inf. Theory, IT-33(4), 461-482 (1987).
19. D. W. Tank and J. J. Hopfield, Simple neural optimization networks: An A/D converter, single decision circuit, and a linear programming circuit, IEEE Trans. Circuits and Systems 33(5), 533-541 (1986).
20. Bang W. Lee and Bing J. Sheu, Modified Hopfield neural networks for retrieving the optimal solution, IEEE Trans. Neural Net. 2(1), 137-142 (1991).
21. J. J. Hopfield and D. W. Tank, Neural computation of decisions in optimization problems, Biolog. Cybernet. 52, 141-152 (1985).
22. W. Lin, F. Liao, C. Taso and T. Lingutle, A hierarchical multiple-view approach to three-dimensional object recognition, IEEE Trans. on Neural Net. 2, 84-92 (1991).
23. W. Li and M. Nasrabadi, Object recognition based on graph matching implemented by a Hopfield-style neural network, Int. J. Conf. Neural Networks, II, 287-290 (18-22 June 1989).
24. F. C. Groan and P. W. Verbeek, Freeman-code probabilities of object boundary quantized contours, Comput. Vis. Graphics Image Process. 7, 391-402 (1978).
25. F. Mokhtarian and A. Mackworth, Scale-based description and recognition of planar curves and two-dimensional shapes, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(1), 000 (1986).
26. T. Pavlidis, Algorithms for Graphics and Image Processing. Computer Science Press, Murray Hill, NJ (1981).
27. K. Sohn, W. Alexander, J. Kim and W. Snyder, A constrained regularization approach to robust corner point detection, IEEE Trans. Syst. Man Cybernet. SMC-24(5), 820-828 (1994).