Color-texture based unsupervised segmentation using JSEG with fuzzy connectedness

Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.

Directional EMD and its application to texture segmentation

In this paper we present the definition and framework of Directional Empirical Mode Decomposition (DEMD) and use DEMD to do texture segmentation. As a new technique of time-frequency analysis, EMD decomposes signals by sifting and then analyzes the instantaneous frequency of the obtained components called Intrinsic Mode Functions (IMFs). Compared with Bidimensional EMD (BEMD) which only extracts textures by radial basis function interpolation, the virtues of DEMD include: the directional quality is considered in this framework; four features can be extracted for each point from the decomposition. The technique of selecting directions for DEMD based on texture’s Wold theory is also presented. Experimental results indicate the effectiveness of the method for texture segmentation. In addition, we show the explanation for the DEMD’s ability for texture classification from visual views.

An approach to extracting information of residential areas from Beijing-1 image based on Gabor texture segmentation

According to the features of high-resolution panchromatic imagery of Beijing-1 small satellite,an approach to extracting information of residential areas is proposed in this paper based on Gabor texture segmentation.The algorithm extracts the features in different directions and different scales by building the Gabor filter,uses cluster analysis of multiple features to segment the image,and performs the fusion processing based on morphological scale space.It solves the problems in image processing resulting from low contrast between remote sensing objects and background,the blurring of image edges and high noise.It has the benefits of direction selection and frequency selection with strong self-adaptive ability.Our experiments prove the effectiveness of the approach for extracting information of residential areas from Beijing-1 high-resolution imagery.

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

We present a multi-phase image segmentation method based on the histogram of the Gabor feature space,which consists of a set of Gabor-filter responses with various orientations,scales and frequencies.Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function,which is a metric to measure the distance of two histograms.The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2.We test our model on both simple synthetic texture images and complex natural images with two or more phases.Experimental results are shown and compared to other recent results.

Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

A multiphase texture segmentation method based on local intensity distribution and Potts model

Because texture images cannot be directly processed by the gray level information of individual pixel,we propose a new texture descriptor which reflects the intensity distribution of the patch centered at each pixel.Then the general multiphase image segmentation model of Potts model is extended for texture segmentation by adding the region information of the texture descriptor.A fast numerical scheme based on the split Bregman method is designed to speed up the computational process.The algorithm is efficient,and both the texture descriptor and the characteristic functions can be implemented easily.Experiments using synthetic texture images,real natural scene images and synthetic aperture radar images are presented to give qualitative comparisons between our method and other state-of-the-art techniques.The results show that our method can accurately segment object regions and is competitive compared with other methods especially in segmenting natural images.

Texture Image Segmentation Based on Nonlinear Diffusion

A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is composed of intensity, scale and orientation of texture image, can be achieved by coupled nonlinear diffusion. A multi-channel statistical region active contour is employed to segment this vector image. The method can be seen as a kind of unsupervised segmentation because parameters are not sensitive to different texture images. Experimental results show its high efficiency in the semiautomatic extraction of texture image.

Target Region Location Based on Texture Analysis and Active Contour Model

Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts.Thus,to solve this problem,a two-step new model was proposed.In the first step,the original features extracted by Gabor filters are applied to training a self-organizing map(SOM) neural network and a novel merging scheme is presented to achieve the clustering.A back propagation(BP) network is used as a classifier to locate the target region approximately.In the second step,Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region.In the experiments,the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.

Tectonic boundaries in the South China Sea from aeromagnetic signature

Magnetic data has been widely applied in the tectonic division.High-resolution magnetic data were used to analyze the geotectonic zoning of the South China Sea.Based on the newly compilated magnetic data,the processing results and the distribution of known faults,we consider that the U-shaped line approximately along the South China Sea national boundary of China shown in the magnetic map is a significant geological and geophysical boundary.We first described the linear characteristics of the magnetic data and then applied pseudo-gravity,Euler deconvolution,tilt derivatives,and the texture segmentation method to process the data.Results show that the dividing line between the South China Sea and the surrounding blocks is approximately along this U-shaped line.The dividing line between the South China domain and the South China Sea domain is along with the Dongsha Islands to Xisha Trough,which is different from the previous geophysical zoning results.Our results are almost consistent with those of the gravity data indicating roughly the tectonic zonation along the U-shaped line.

Boundary-aware texture region segmentation from manga

Due to the lack of color in manga(Japanese comics), black-and-white textures are often used to enrich visual experience. With the rising need to digitize manga, segmenting texture regions from manga has become an indispensable basis for almost all manga processing, from vectorization to colorization. Unfortunately, such texture segmentation is not easy since textures in manga are composed of lines and exhibit similar features to structural lines(contour lines). So currently, texture segmentation is still manually performed, which is labor-intensive and time-consuming. To extract a texture region, various texture features have been proposed for measuring texture similarity, but precise boundaries cannot be achieved since boundary pixels exhibit different features from inner pixels. In this paper, we propose a novel method which also adopts texture features to estimate texture regions. Unlike existing methods, the estimated texture region is only regarded an initial, imprecise texture region. We expand the initial texture region to the precise boundary based on local smoothness via a graph-cut formulation. This allows our method to extract texture regions with precise boundaries. We have applied our method to various manga images and satisfactory results were achieved in all cases.

A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field

The texture analysis is often discussed in image processing domain, but most methods are limited within gray-level image or color image, and the present conception of texture is defined mainly based on gray-level image of single band. One of the essential characters of remote sensing image is multidimensional or even high-dimensional, and the traditional texture conception cannot contain enough information for these. Therefore, it is necessary to pursuit a proper texture definition based on remote sensing images, which is the first discussion in this paper. This paper describes the mapping model of spectral vector in two-dimensional image space using Markov random field (MRF), establishes a texture model of multiband remote sensing image based on MRF, and analyzes the calculations of Gibbs potential energy and Gibbs parameters. Further, this paper also analyzes the limitations of the traditional Gibbs model, prefers a new Gibbs model avoiding estimation of parameters, and presents a new texture segmentation algorithm for hy-perspectral remote sensing image later.

An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.

Tumor segmentation in lung CT images based on support vector machine and improved level set

In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.