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Proceedings Paper

Invariants for motion-based classification
Author(s): Wassim Hafez
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Paper Abstract

Shape geometric invariant play an important role in model- based vision (MBV). However, in many MBV scenarios, shape information may not be sufficiently reliable and hence other types of invariant need to be considered. This paper addresses motion-based classification of objects based on unique motion or activity characteristics in long-sequence of images. To date, the techniques developed in motion-based recognition are inherently sensitive to (a) object's shape, (b) Euclidean group actions and (c) time scale, i.e., velocity and acceleration of motion. We propose the development of a set of motion-based invariant that capture geometric aspects of object's kinematic constraints during distinctive motions and activities. Algebraic and differential invariant of curves and surfaces in a projective space, the kinematic image space, are proposed for motion and activity classification. The proposed approach established parallelism between space and motion geometric invariance.

Paper Details

Date Published: 1 March 1998
PDF: 10 pages
Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); doi: 10.1117/12.300072
Show Author Affiliations
Wassim Hafez, IntelliSys, Inc. (United States)

Published in SPIE Proceedings Vol. 3240:
26th AIPR Workshop: Exploiting New Image Sources and Sensors
J. Michael Selander, Editor(s)

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