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Although many depth map estimation methods have been developed, depth map estimation still has some difficulties in low-texture regions and occlusion. This paper presents a novel approach for multi-view depth video estimation which adopts an adaptive matching scheme for high-texture regions and low-texture regions, respectively. For low-texture regions, the proposed method integrates affine invariant features with colour to make the matching more robust. Furthermore, according to the smoothness assumption of depth maps, a refinement is then performed on the estimated depth maps. Experimental results show that the proposed method can achieve better or comparable performances than the state-of-the-art method in the category of local methods even with the less running time. [ABSTRACT FROM PUBLISHER]

Abstract: Assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small, recent image matching algorithms have focused on affine invariant region (IR) detection and its description to increase the robustness in matching. However, the distinctiveness of an intensity-based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern. To address this problem, we investigated the geometry of a local IR cluster (also called a clique) and propose a new clique-based image matching method. In the proposed method, the clique of an IR is estimated by Delaunay triangulation in a local affine frame and the Hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors. We also introduce two adaptively weighted clique distances, where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution. Experimental results show the clique-based matching method produces more tentative correspondences than variants of the SIFT-based method. [Copyright &y& Elsevier]