Colored Local Invariant Features for Distinct Object Description in Vision-Based Intelligent Systems

Colored Local Invariant Features for Distinct Object Description in Vision-Based Intelligent Systems

Alaa E. Abdel-Hakim (University of Louisville, USA) and Aly A. Farag (University of Louisville, USA)
DOI: 10.4018/978-1-59904-249-7.ch010
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Abstract

This chapter addresses the problem of combining color and geometric invariants for object description by proposing a novel colored invariant local feature descriptor. The proposed approach uses scale-space theory to detect the most geometrically robust features in a physical-based color invariant space. Building a geometrical invariant feature descriptor in a color invariant space grants the built descriptor the stability to both geometric and color variations. The comparison between the proposed colored local invariant features and gray-based local invariant features with respect to stability and distinction supports the potential of the proposed approach. The proposed approach is applicable in any vision-based intelligent system that requires object recognition/retrieval. At the end of this chapter, we present a case study of a local features-based camera planning platform for smart vision systems.

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