Showing papers by "Sidharta Gautama published in 1996"

Context driven matching in structural pattern recognition

Abstract: Publisher Summary This chapter examines the problem of structural pattern recognition using graph structures. To speed up the correspondence problem, the chapter proposes a histogram technique which characterizes the context of a primitive within a pattern and allows indexing in the model database with polynomial complexity. The chapter presents a new iterative matching technique based on a histogram of structural context information. Experiments show a good noise suppressing ability while retaining adequate recognition results with minimal false alarms. Because scene primitives are structurally mapped onto the model, orientation and scale can be hypothesized from a match, whereas the model can be used to direct a search for missing information, thereby improving or ignoring the match. This will be the subject for further work.

Automatic induction of relational models

Abstract: The problem of shape recognition is studied through the use of relational models based on the hypergraph representation and the context similarity measure Formal definitions are introduced and graph properties are calculated important to the matching process A conflict is shown to exist between the interclass distance and the semantical distance between the vertices within a model The representation is extended with the notion of vertex neighborhood, which increases the semantical distance and makes the processing of complex scenes feasible© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only