![]() As a first step, we measured the porosity and the elastic properties of a group of carbonate samples with varying micrite content. We developed a multi-scale approach performing segmentation of the rock images and numerical modeling across several scales, accounting for those heterogeneities. This problem is exacerbated in carbonate rocks due to their heterogeneity at all scales. However, computational models created at different scales can yield conflicting results with respect to the physical laboratory. These can complement experimental results, especially when time constraints and sample availability are a problem. Numerical methods based on computational simulations can be an important tool in estimating physical properties of rocks. Multi-scale image segmentation and numerical modeling in carbonate rocks These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. This weight acts as one of the merging constraints in the multi- scale image segmentation. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. ![]() Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. To avoid the problem of over- segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over- segmentation) results are difficult to avoid. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. Multi-scale image segmentation method with visual saliency constraints and its application Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. ![]() Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Liu, Zhongmin Chen, Zhicai Li, Zhanming Hu, Wenjin Multi scales based sparse matrix spectral clustering image segmentation
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