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1 November 2011 Color-invariant three-dimensional feature descriptor for color-shift-model-based image processing
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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.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Joohyun Lim and Joonki Paik "Color-invariant three-dimensional feature descriptor for color-shift-model-based image processing," Optical Engineering 50(11), 117005 (1 November 2011).
Published: 1 November 2011

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