Multiscale Image Representation and Texture Extraction Using Hierarchical Variational Decomposition

In order to achieve a mutiscale representation and texture extraction for textured image, a hierarchical (BV,Gp,L2) decomposition model is proposed in this paper. We firstly introduce the proposed model which is obtained by replacing the fixed scale parameter of the original (BV,Gp,L2) decomposition with a varying sequence. And then, the existence and convergence of the hierarchical decomposition are proved. Furthermore, we show the nontrivial property of this hierarchical decomposition. Finally, we introduce a simple numerical method for the hierarchical decomposition, which utilizes gradient decent for energy minimization and finite difference for the associated gradient flow equations. Numerical results show that the proposed hierarchical (BV,Gp,L2) decomposition is very appropriate for multiscale representation and texture extraction of textured image

Adaptive Regularized Level Set Method for Weak Boundary Object Segmentation

An adaptive regularized level set method for image segmentation is proposed. A weighted p(x)-Dirichlet integral is presented as a geometric regularization on zero level curve, which is used to diminish the influence of image noise on level set evolution while ensuring the active contours not to pass through weak object boundaries. The idea behind the new energy integral is that the amount of regularization on the zero level curve can be adjusted automatically by the variable exponent p(x) to fit the image data. This energy is then incorporated into a level set formulation with an external energy term that drives the motion of the zero level set toward the desired objects boundaries, and a level set function regularization term that is necessary for maintaining stable level set evolution. The proposed model has been applied to a wide range of both real and synthetic images with promising results

The complete mitochondrial genome of Leiocassis crassilabris (Teleostei, Siluriformes: Bagridae)

The Leiocassis crassilabris is an important economic fish in China, and is widely distributed in south China, e.g. Yangtze River, Pearl River, and Min River, so it is a good model to study population genetics and geological changes of these regions. In this study, the complete mitochondrial genome sequence of L. crassilabris has been obtained with PCR. The gene arrangement and composition L. crassilabris of mitochondrial genome sequence are similar to most of the other vertebrates', which contains 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a non-coding control region with the total length of 16,530 bp. Except for eight tRNA and ND6 genes, other genes are encoded on heavy-strand (H-strand). Similar to most other vertebrates, the bias of G and C have universality in different region (genes). The complete mitochondrial genome sequence of L. crassilabris would contribute to better understand population genetics, conservation, biogeography, evolution of this lineage.The Leiocassis crassilabris is an important economic fish in China, and is widely distributed in south China, e.g. Yangtze River, Pearl River, and Min River, so it is a good model to study population genetics and geological changes of these regions. In this study, the complete mitochondrial genome sequence of L. crassilabris has been obtained with PCR. The gene arrangement and composition L. crassilabris of mitochondrial genome sequence are similar to most of the other vertebrates', which contains 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a non-coding control region with the total length of 16,530 bp. Except for eight tRNA and ND6 genes, other genes are encoded on heavy-strand (H-strand). Similar to most other vertebrates, the bias of G and C have universality in different region (genes). The complete mitochondrial genome sequence of L. crassilabris would contribute to better understand population genetics, conservation, biogeography, evolution of this lineage