A linguistic approach to 3-D object recognition

Abstract The principles of a 3-D object recognition system for combined intensity-image and depth-map understanding are discussed. The goal of such system is to be an inversion of image synthesis performed by 3-D computer graphics. A linguistic model for two system elements, the knowledge base and recognition strategy, being an extension of pattern recognition approaches, is outlined. It consists of a powerful object specification language and a simultaneous syntactic-semantic analysis in this language. The syntax is based on a node-controlled parallel structure grammar. Particular attention is paid to elements shared in common by several parts and to hidden line/surface problems. Both are embedded into the grammars derivation. The semantics is well-defined due to the attribution of the grammar.

[1]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Josef Kittler,et al.  Pattern recognition theory and applications : proceedings of the NATO Advanced Study Institute held at St. Anne's College, Oxford, March 29-April 10, 1981 , 1982 .

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

[4]  F. R. A. Hopgood,et al.  Machine Intelligence 2 , 1970, The Mathematical Gazette.

[5]  Manfred Nagl,et al.  Graph-Grammars and Their Application to Computer Science , 1986, Lecture Notes in Computer Science.

[6]  R. Haralick.  Using perspective transformations in scene analysis , 1980 .

[7]  King-Sun Fu,et al.  Syntactic Methods in Pattern Recognition , 1974, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Yoram Yakimovsky,et al.  A system for extracting three-dimensional measurements from a stereo pair of TV cameras , 1976 .

[9]  A. E. Brain,et al.  The measurement and use of registered reflectance and range data in scene analysis , 1977, Proceedings of the IEEE.

[10]  Ray A. Jarvis,et al.  A Perspective on Range Finding Techniques for Computer Vision , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Bernd Neumann,et al.  Knowledge Sources for Understanding and Describing Image Sequences , 1982, GWAI.

[12]  F. R. A. Hopgood,et al.  Machine Intelligence 5 , 1971, The Mathematical Gazette.

[13]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.