Finding Correspondences Between Images using Descriptors and Graphs

AbstractThe problem of finding correspondences is considered in the article. The main objective of this method is to reduce the number of false matches by using structural performance. The relevance of the problem is proven. The review of existing methods of finding correspondences is provided. The method presented is finding correspondences based on combined use of graphs and descriptors. Scott and Longuet-Higgins algorithm is used in the first stage. We construct a graph the vertices of which are the features on the two images. Singular value decomposition of the graph matrix is performed. The correspondences based on the descriptor are used. An example of the algorithm is shown. Test images are researched. A comparison of the algorithm with the RANSAC is carried out. The proposed approach allows excluding a significant portion of false correspondences found using the existing descriptors. The algorithm has high speed

Edge Detection in Remote Sensing Images Based on Fuzzy Image Representation

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images. Comparison of result with Sobel, Prewitt, Roberts and Canny operators is presented. Developed method selected more details (edges) rather then Sobel, Prewitt and Roberts operators, but less than Canny operator. This is because the selected convolution kernel (Sobel) has size 3x3. There are also used only simple functions of estimating the real intensities of pixels. Later, to increase quality it is necessary to use more complex masks of size 5x5 and 7x7 or median filters. Developed approach showed its workability in solving imagep rocessing problems. The proposed fuzzy model in the future can be extended to use higher level fuzzy sets (Type-2FS and others)

Development of fuzzy fractal representation of the image

Основная статьяIn article the way of the fuzzy image representation with use fractal models of images is considered. According to it the image can be presented in the form of a tree range blocks which correspond to image blocks. Everyone range block represents the structure describing similarity of this block to other block of the image. As a result it turns out treelike fractal representation of the image describing display of property of self-similarity in the image. The basic possibilities of construction of algorithms of digital processing of the images which are based on given representation are listed