- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
: A Novel Similarity Measure for Matching Local Image Descriptors
摘要： mp-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how mp-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporating (cid:96)p-norm distance and mp-dissimilarity. Each of these three matching strategies is essentially a two-round matching process that utilizes (cid:96)p-norm distance and mp-dissimilarity individually. This paper presents two novel similarity measures for matching local image descriptors. The first similarity measure normalizes and weights the similarities that are calculated using (cid:96)p-norm distance and mp-dissimilarity, respectively. The second similarity measure involves a novel calculation that takes into account both spatial distance and data distribution between descriptors. The proposed similarity measures are extensively evaluated on a few image registration benchmark data sets. Experimental results will demonstrate that the proposed similarity measures achieve higher matching accuracy and are able to attain better recall results when registering multi-modal images compared with the existing matching strategies that combine (cid:96)p-norm distance and mp-dissimilarity.
关键词： local descriptors,accuracy,mp-dissimilarity,image registration,(cid:96)p-norm distance,Similarity measure
更新于2025-09-23 15:23:52
-
Noisy image block matching based on dissimilarity measure in discrete cosine transform domain
摘要： In this paper, the problem of image block similarity measuring in noisy environment is considered. In different practical applications often is necessary to ?nd groups of similar image blocks within an ample search area. In such situation, the full search algorithm is very slow; apart, its accuracy is low due to the presence of noise. New algorithms for similar image block matching in noisy environment are presented. The algorithms are based on the dissimilarity measure calculated as the distance between image patches in the discrete cosine transform domain. The proposed algorithms perform the hierarchical search for the similar image blocks and hereby have a reduced complexity in comparison to the full search algorithm. Adjusting the radius of the distance calculation for spectral coef?cient matching, the characteristics of the block matching algorithm can easily be adjusted to obtain a better accuracy of the matched block group. A higher accuracy is obtained using the local adaptation of the radius for the distance calculation outperforming the existing algorithms used to ?nd groups of similar blocks in different applications, such as image noise ?ltering and image clustering. The performance of the different block matching algorithms were evaluated on the base of the proposed accuracy measure that uses as a reference the list of patches obtained with the full search algorithm in the absence of noise.
关键词： Dissimilarity measure,hierarchical search,local adaptation,noisy image block matching,discrete cosine transform
更新于2025-09-23 15:21:01