Coarse-to-Fine PatchMatch for Dense Correspondence

Li, Yunsong; Hu, Yinlin; Song, Rui; Rao, Peng; Wang, Yangli

doi:10.1109/tcsvt.2017.2720175

Cited by 24 publications

(36 citation statements)

References 21 publications

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“…The goal of image matching algorithms is to establish as many as possible precise pointwise correspondences or matches between two images, discovering shared visual content within them. Image matching techniques based on local features have been extensively studied in the literature during the past decade [36,38,65,67]. Besides, some image matching approaches are inspired by optical flow methods [35][36][37][38]65,68].…”

Section: Image Matching Based On Optical Flowmentioning

confidence: 99%

“…The image matching approaches can be divided into dense [65,68] and sparse [35][36][37][38] algorithms. The dense algorithms match densely sampled pixel-wise local features by using the information of every pixel of the image, contrary to sparse methods that take advantage of sparse feature points.…”

Section: Image Matching Based On Optical Flowmentioning

confidence: 99%

“…This technique breaks down patches into a hierarchy of sub-patches by using a multi-layer architecture, handling nonrigid deformations and incorporating a multi-scale scoring of the matches. Besides, the Coarse-to-fine PatchMatch algorithm is proposed by Li et al [38]. It computes correspondences between images by combining an efficient random search strategy and a propagation procedure with constrained random search radius between adjacent levels on a hierarchical architecture.…”

Section: Image Matching Based On Optical Flowmentioning

confidence: 99%

“…In the literature, some techniques that interpolate the optical flow from sparse correspondences [35][36][37][38], contrarily to dense correspondences proposed in Meng et al [27]. These methods are known as sparse matching algorithms and they show great success in efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Section 3.1 explains the preprocessing procedure implemented in our approach. • Second, we propose to use the Coarse-to-fine PatchMatch (CPM) algorithm [38] as a sparse-matching technique to reduce the number of comparisons of dense correspondences during the matching process. This method achieves equivalent recognition performance in comparison to the baseline approach proposed by Meng et al [27] and highly outperforms its results in terms of execution time, achieving a speed-up of 9×.…”

Section: Introductionmentioning

confidence: 99%

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Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

et al. 2019

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Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

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