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Optical Engineering

Color-invariant three-dimensional feature descriptor for color-shift-model-based image processing
Author(s): Joohyun Lim; Joonki Paik
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Paper Abstract

We present a novel color-invariant depth feature descriptor for color-shift-model (CSM)-based image processing. Color images acquired by a single camera equipped with multiple color-filter aperture (MCA) contain depth-dependent color misalignment. The amount and direction of the misalignment provides object's distance from the camera. The CSM-based image processing, which represents the combined image-acquisition and depth-estimation framework, requires a color-invariant feature descriptor that can convey depth information. For improving depth-estimation performance, color boosting is performed on a color image acquired by the MCA camera, and CSM-based channel-shifting descriptor vectors, or channel-shifting vectors (CSVs), are generated by using the feasibility test. Color-invariant features are also extracted in the luminance image. The proposed color-invariant three-dimensional (3-D) feature descriptor is finally obtained by combining the CSVs and luminance features. We present experimental analysis of the proposed feature descriptor and show that the descriptors are proportional to the depth of an object. The proposed descriptor can be used for feature-based image matching in various applications, including 3-D scene modeling, 3-D object recognition, 3-D video tracking, and multifocusing, to name a few.

Paper Details

Date Published: 1 November 2011
PDF: 8 pages
Opt. Eng. 50(11) 117005 doi: 10.1117/1.3657504
Published in: Optical Engineering Volume 50, Issue 11
Show Author Affiliations
Joohyun Lim, Chung-Ang Univ. (Korea, Republic of)
Joonki Paik, Chung-Ang Univ. (Korea, Republic of)


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