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Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automatic matching for such images. Deep convolutional neural networks, which can express high-level features and their correlation, have received increasing attention for the task of wide-baseline image matching, and learning-based methods have the potential to surpass methods based on handcrafted features. Therefore, we focus on the dynamic study of wide-baseline image matching and review the main approaches of learning-based feature detection, description, and end-to-end image matching. Moreover, we summarize the current representative research using stepwise inspection and dissection. We present the results of comprehensive experiments on actual wide-baseline stereo images, which we use to contrast and discuss the advantages and disadvantages of several state-of-the-art deep-learning algorithms. Finally, we conclude with a description of the state-of-the-art methods and forecast developing trends with unresolved challenges, providing a guide for future work.

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]

In this paper, we propose a new affine shape adaptation scheme for the affine invariant feature detector, in which the convergence stability is still an opening problem. This paper examines the relation between the integration scale matrix of next iteration and the current second moment matrix and finds that the convergence stability of the method can be improved by adjusting the relation between the two matrices instead of keeping them always proportional as proposed by previous methods. By estimating and updating the shape of the integration kernel and differentiation kernel in each iteration based on the anisotropy of the current second moment matrix, we propose a coarse-to-fine affine shape adaptation scheme which is able to adjust the pace of convergence and enable the process to converge smoothly. The feature matching experiments demonstrate that the proposed approach obtains an improvement in convergence ratio and repeatability compared with the current schemes with relatively fixed integration kernel. [ABSTRACT FROM AUTHOR]

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]

Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automatic matching for such images. Deep convolutional neural networks, which can express high-level features and their correlation, have received increasing attention for the task of wide-baseline image matching, and learning-based methods have the potential to surpass methods based on handcrafted features. Therefore, we focus on the dynamic study of wide-baseline image matching and review the main approaches of learning-based feature detection, description, and end-to-end image matching. Moreover, we summarize the current representative research using stepwise inspection and dissection. We present the results of comprehensive experiments on actual wide-baseline stereo images, which we use to contrast and discuss the advantages and disadvantages of several state-of-the-art deep-learning algorithms. Finally, we conclude with a description of the state-of-the-art methods and forecast developing trends with unresolved challenges, providing a guide for future work. [ABSTRACT FROM AUTHOR]