Inferred boundary-based approach to object recognition

Anca L. Ralescu; James G. Shanahan

doi:10.1117/12.205254

28 March 1995 Inferred-boundary-based approach to object recognition

Anca L. Ralescu, James G. Shanahan

Proceedings Volume 2424, Nonlinear Image Processing VI; (1995) https://doi.org/10.1117/12.205254
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

We are concerned with object recognition in the framework of a navigation support system (NSS). Unlike a vision based navigation system where the navigating agent must solve obstacle avoidance problems, path planning, etc., different problems must be solved in the NSS: for instance it must inform the user that it has reached a desired location once objects associated with that location have been recognized. In this general context we present a framework to compute perceptual organization. It incorporates a number of concepts from human visual analysis especially the Gestalt laws of organization. Fuzzy techniques are used for the definition and evaluation of the grouping/non-grouping properties as well as for the construction of structures from grouped input tokens. This method takes as input the initially fitted line segments (tokens) and then recursively groups these tokens into higher level structures (tokens) such as lines, u-structures, quadrilaterals, etc. The output high level structures can then be used to compare with object models and thus lead to object recognition. In this paper inference (grouping) of line segments, line symmetry, junctions, closed regions and strands is presented. The approach is supported by experimental results on 2D images of an office scene environment.

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Anca L. Ralescu and James G. Shanahan "Inferred-boundary-based approach to object recognition", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); https://doi.org/10.1117/12.205254

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KEYWORDS

Image segmentation

Fuzzy logic

Navigation systems

Image processing

Object recognition

Visualization

Computer vision technology

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