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22 March 1999 Multiresolution stereo algorithm via wavelet representations for autonomous navigation
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Many autonomous vehicle navigation systems have adopted area-based stereo image processing techniques that use correlation measures to construct disparity maps as a basic obstacle detection and avoidance mechanism. Although the intra-scale area-based techniques perform well in pyramid processing frameworks, significant performance enhancement and reliability improvement may be achievable using wavelet- based inter-scale correlation measures. This paper presents a novel framework, which can be facilitated in unmanned ground vehicles, to recover 3D depth information (disparity maps) from binocular stereo images. We propose a wavelet- based coarse-to-fine incremental scheme to build up refined disparity maps from coarse ones, and demonstrate that usable disparity maps can be generated from sparse (compressed) wavelet coefficients. Our approach is motivated by a biological mechanism of the human visual system where multiresolution is known feature for perceptional visual processing. Among traditional multiresolution approaches, wavelet analysis provides a mathematically coherent and precise definition to the concept of multiresolution. The variation of resolution enables the transform to identify image signatures of objects in scale space. We use these signatures embedded in the wavelet transform domain to construct more detailed disparity maps at finer levels. Inter-scale correlation measures within the framework are used to identify the signature at the next finer level, since wavelet coefficients contain well-characterized evolutionary information.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Minbo Shim, John Jay Kurtz, and Andrew F. Laine "Multiresolution stereo algorithm via wavelet representations for autonomous navigation", Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999);

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