Understanding the curve-polygon object

Computers and Graphics (Pergamon)

Volumn 28, Issue 6, 2004, Pages 919-936

  Les, Z ^a   Les, M ^a  

^a Queen Jadwiga Research Institute of Understanding   (Australia)

Author keywords

Convex curve polygon class; Curve polygon model expert; Generating expert; Self correcting reasoning expert; Shape understanding system

Indexed keywords

APPROXIMATION THEORY; BOUNDARY VALUE PROBLEMS; COMPUTATIONAL GEOMETRY; GRAPH THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; PATTERN RECOGNITION; PROBLEM SOLVING; SEMANTICS;

CONVEX CURVE-POLYGON CLASS; CURVE-POLYGON MODEL EXPERT; GENERATING EXPERT; SELF-CORRECTING REASONING EXPERT; SHAPE UNDERSTANDING SYSTEM;

COMPUTER GRAPHICS;

EID: 5444243407     PISSN: 00978493     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cag.2004.08.001     Document Type: Article

References (42)

1. Marr D. Vision. San Francisco: Freeman; 1982.
2. Stiny GN. Pictorial and formal aspects of shape and shape grammars and aesthetic system. PhD thesis, Department of Engineering, University of California, 1975.
3. Bhavnagri B. A method for representing shape based on an equivalence relation of polygon. Pattern Recognition 1994;27(2):247-60.
4. Ghosh PK, Jain P. An algebra of geometric shapes. IEEE Computer Graphics and Applications 1993;11:99-118.
5. Hilaga M, Shinagawa T, Kohmura T, Kuni TL. Topology matching for fully automatic similarity estimation of 3D shapes. In: SIGGRAPH 2001, 2001.
6. Bhanu B, Faugeras OD. Shape matching of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984;6(2):137-56.
7. Deguchi K, Aoki S. Regularized polygonal approximation for analysis and interpretation of planar contour figures. In: Proceedings of the 10th International Conference on Pattern Recognition; 1990.
8. Lu CH, Dunham JG. Shape matching using polygon approximation and dynamic alignment. Pattern Recognition Letters 1993;14:945-9.
9. Mitizias DA, Mertzios BG. Shape recognition with a neural classifier based on a fast polygon approximation technique. Pattern Recognition 1994;5(27):627-36.
10. Binford TO. Survey of model-based image, analysis systems. International Journal of Robotics Research 1982;1(1):18-64.
11. Besl PJ, Jain RC. Three-dimensional object recognition. Computing Surveys 1985;17(1):75-154.
12. Chin RT, Dyer CR. Model-based recognition in robot vision. Computing Surveys 1986;18(1):67-108.
13. Pope AR. Model-based object recognition, a survey of recent research. Department of Computer Science, The University of British Columbia Press; 1994.
14. Grimson WL. Object recognition by computer: the role of geometric constraints. Boston: MIT Press; 1990.
15. Cornell JH, Brady M. Generating and generalizing models of visual objects. Artificial Intelligence 1987;31:159-83.
16. Segen J. Learning structural description of shape. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. New York: Morgan Kaufmann; 1985.
17. Pope AR, Lowe DG. Learning object recognition models from images. In: 4th International Conference on Computer Vision; 1993.
18. Cromwell RL, Kak AC. Automatic generation of object class descriptions using symbolic learning techniques. In: The 9th National Conference on Artificial Intelligence; 1991.
19. Pellegretti P, Roll F, Serplco SB, Vernazza G. Supervised learning of descriptions for image recognition purposes. IEEE Transactions on Pattern Analysis and Machine Intelligence 1994;16(1):92-8.
20. Michalski RS. Pattern recognition as rule-guided inductive inference. IEEE Transactions on Pattern Analysis and Machine Intelligence 1980;2:349-61.
21. Hsu JC, Hwang SY. A machine learning approach for acquiring descriptive classification rules of shape contours. Pattern Recognition 1997;30(2):245-52.
22. Bhanu B, Poggio TA. Introduction to the special section on learning in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 1994;16(9):865-8.
23. Reiter R, Mackworth AK. A logical framework for depiction and image interpretation. Artificial Intelligence 1989;41(2):125-55.
24. Sowmya A, Lee E. Generating symbolic descriptions of two-dimensional blocks world. In: Kawasaki Japan Proceedings of IAPR International Workshop on Machine Vision Applications; 1994.
25. Russ JC. The image processing handbook. Boca Raton: CRC Press; 1991.
26. Les Z, Les M. Shape understanding system: application of fuzzy sets, neural networks and statistical methods in the process of visual thinking. In: Melbourne, Australia The 10th IEEE Conference on Fuzzy Systems; December 2001. p. 679-82.
27. Les Z, Les M. Shape understanding system: the visual reasoning process. International Journal of Pattern Recognition and Artificial Intelligence 2002;17(4):1-21.
28. Les Z, Les M. Visual thinking as a part of understanding process - a new way of problem solving and communication of the intelligent systems in automotive and transportation technology. SAE Transactions - Journal of Passenger Cars: Electronic and Electrical Systems 2003. p. 893-99.
29. Les Z. Les M. Shape understanding as knowledge generation. IEEE Transactions on Data and Knowledge Engineering 2002;16(2):1-11.
30. Les Z, Les M. Understanding as a new paradigm in the design of the intelligent systems in automotive and transportation technology. In: Barcelona Proceedings of the International Conference on Automotive and Transportation Technology (ATT); 2001.
31. Les Z, Les M, Shape understanding system: understanding of the convex objects. The Journal of Electronic Imaging 2002;12(2):1-15.
32. Les Z. Shape understanding system: understanding the thin object. International Journal of Computers and Graphics 2002;26(6):1-20.
33. Les Z. Les M. Understanding as an interpretation in shape understanding system. Journal of Experimental and Theoretical Artificial Intelligence 2003;15(4):433-59.
34. Les Z, Les M. Understanding in shape understanding system. International Journal of Pattern Recognition and Artificial Intelligence 2004;18(4):727-45.
35. Les Z. Shape understanding. Possible classes of shapes. International Journal of Shape Modeling 2001;7(1):75-109.
36. Les Z, Les M. Shape understanding system: the noisy class. In: Proceedings of the International Conference on Information Systems Analysis and Synthesis and World Multiconference on Systenrics, Cybernetics and Informatics, Orlando, July 23-26, 2001.
37. Les Z. The processing method as a set of image transformations in shape understanding. International Journal of Computers and Graphics 2001;25:223-33.
38. Les Z, Les M. Understanding of the concave-complex object. In: Benalmadena, Spain Proceedings IASTED Conference on Visualization, Imaging, and Image Processing (VIIP); September 8-10, 2003.
39. Les Z, Les M. Shape understanding system: understanding of the cyclic object. In: Rhodes, Greece Proceedings IASTED Conference on Signal Processing, Pattern Recognition and Applications (SPPRA); June 30-July 2, 2003.
40. Les Z. Les M. System of experts to perform an epistemological task of shape understanding. In: Las Vegas Proceedings IASTED International Conference on Software Engineering and Applications; 2000.
41. Les Z, Les M. Shape understanding system: system of experts for visual thinking. In Marina Del Rey. USA Proceedings IASTED International Conference on Software Engineering and Applications (SEA); November 3-5, 2003.
42. Les Z, Tadeusiewicz R. Shape understanding system - generating exemplars of the polygon class. In: Las Vegas Proceedings IASTED International Conference on Computer Graphics and Imaging; 2000.