Abstract
Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the scale-interaction method is proposed. First, we proposed a new automatic feature detection processing by using Marr wavelets within the scale-interaction method. Second, feature extraction is executed under the mesh division strategy and entropy algorithm, accompanied by the assessment of the distribution criterion. Image matching is achieved by the nearest neighbor search with normalized cross-correlation similarity measurement to perform coarse matching on feature points set. As to the matching points filtering part, the Random Sample Consensus Algorithm (RANSAC) removes outliers correspondences. One the other hand, a second-order NCC based graph matching algorithm is presented. This algorithm is an integer quadratic programming (IQP) graph matching problem, which is implemented in Matlab. It allows developing and comparing many algorithms based on a common evaluation platform, sharing input data, and a customizable affinity matrix and matching list of candidate solution pairs as input data. Experimental results demonstrate the improvements of these algorithms concerning matching recall and accuracy compared with other algorithms.
Highlights
Computer vision is an important research direction in current computer science since it occupies a pivotal position in human perception simulation
We study different declinations of feature correspondence problems by the use of the Matlab platform, in order to reuse and provide state-of-the-art solution methods, as well as experimental protocols and input data necessary with evaluation and comparison tools against existing sequential algorithms, most of the time developed in Matlab framework
In order to vary the feature set size while preserving a reasonable recall rate in graph matching, we have proposed a new combination of filters with an entropy-response based selection method
Summary
Computer vision is an important research direction in current computer science since it occupies a pivotal position in human perception simulation. A sparse feature point method based on entropy selection is proposed as a new filtering step. Filtering ( known as convolution) is a very popular operation in the field of image processing, which can be applied to image encryption by changing pixel values [14] This step combines the local entropy evaluation with the brightness deviation response as a new process for feature selection. The experiments were performed on standard image processing benchmarks They showed how to increase the size of the feature set and matching accuracy with saving computation time. For the first-order graph matching, we have proposed a new combination of feature points detection algorithms among Laplace filter, Marr wavelets, and the entropy-response based selection method.
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