access icon free Estimation of disparity map of stereo image pairs using spatial domain local Gabor wavelet

The stereo matching problem takes two images captured by nearby cameras and attempts to recover quantitative disparity information. Most of the existing stereo matching algorithms find it difficult to estimate disparity in the occlusion, discontinuities and textureless regions in the images. In the last few decades, a number of stereo matching methods have been proposed to overcome some of these problems. In the same line of thought, the authors propose a new feature-based stereo matching method, which consists of four basic steps – feature-based stereo correspondence, two-pass cost aggregation, disparity computation using winner-takes-all selection and finally, the disparity refinement. In the proposed method, local features of Gabor wavelet in spatial domain are used for matching cost computation and subsequently a cost aggregation step is implemented by combined use of the Kuwahara filter and the median filter. Experimental results on the Middlebury benchmark database shows that the proposed method outperforms many existing local stereo matching methods.

Inspec keywords: image matching; Gabor filters; image capture; stereo image processing; median filters; cameras; image filtering

Other keywords: image capturing; winner-takes-all selection; texture-less image; spatial domain local Gabor wavelet; quantitative disparity information recovery; disparity refinement step; cameras; Kuwahara filter; middlebury benchmark database; image discontinuity; disparity computation step; feature-based stereo correspondence; median filter; stereo image pair disparity map estimation; feature-based stereo matching method; two-pass cost aggregation step; image occlusion

Subjects: Filtering methods in signal processing; Image recognition; Image sensors; Computer vision and image processing techniques

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