Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space

In this report, we propose a novel and efficient approach for active unsurpervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that allows to incorporate these features in a level set based unsupervised segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. Unlike features obtained by Gabor filters, our approach naturally leads to a significantly reduced number of feature channels. Thus, the supervised part of a texture segmentation algorithm, where the choice of good feature channels has to be learned in advance, can be omitted, and we get an efficient solution for unsupervised texture segmentation. The actual segmentation process based on the new features is an active and adaptative contour model that estimates dynamically probability density functions inside and outside a region and produces very convincing results. It is implemented using a fast level set based active contour technique and has been tested on various real textured images. The performance of the approach is favorably compared to recent studies

A Novel Active Contour Model for Texture Segmentation

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial. There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures. In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimlarity between such matrices. We demonstrate performance of our algorithm on both artificial and real texture images

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images

Level set segmentation with multiple regions

The popularity of level sets for segmentation is mainly based on the sound and convenient treatment of regions and their boundaries. Unfortunately, this convenience is so far not known from level set methods when applied to images with more than two regions. This paper introduces a comparatively simple way how to extend active contours to multiple regions keeping the familiar quality of the two-phase case. We further suggest a strategy to determine the optimum number of regions as well as initializations for the contours

Model-based learning of local image features for unsupervised texture segmentation

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images

A Survey of Image Segmentation Based On Multi Region Level Set Method

Abstract−Image segmentation has a long tradition as one of the fundamental problems in computer vision. Level Sets are an important category of modern image segmentation techniques are based on partial differential equations (PDE), i.e. progressive evaluation of the differences among neighboring pixels to find object boundaries. Earlier method used novel level set method (LSM) for image segmentation. This method used edges and region information for segmentation of objects with weak boundaries. This method designed a nonlinear adaptive velocity and a probability-weighted stopping force by using Bayesian rule. However the difficulty of image segmentation methods based on the popular level set framework to handle an arbitrary number of regions. To address this problem the present work proposes Multi Region Level Set Segmentation which handles an arbitrary number of regions. This can be explored with addition of shape prior's considerations. In addition apriori information of these can be incorporated by using Bayesian scheme. While segmenting both known and unknown objects, it allows the evolution of enormous invariant shape priors. The image structures are considered as separate regions, when they are unknown. Then region splitting is used to obtain the number of regions and the initialization of the required level set functions. In the next step, the energy requirement of level set functions is robustly minimized and similar regions are merged in a last step. Experimental result achieves better result when compare with existing system

A TV flow based local scale estimate and its application to texture discrimination

This paper presents a local region based scale measure, which exploits properties of a certain type of nonlinear diffusion, the so-called total variation (TV) flow. During the signal evolution by means of TV flow, pixels change their value with a speed that is inversely proportional to the size of the region they belong to. From this evolution speed one can derive a local scale estimate based on regions instead of derivative filters. Main motivation for such a scale measure is its application to texture discrimination, in particular the construction of an alternative to Gabor filters. When the scale estimate is combined with the components of the structure tensor, which provides orientation information, it yields a texture feature space of only four dimensions. Like Gabor features, this sparse feature space discriminates textures by means of their orientation and scale, yet the representation of orientation and scale is less redundant. The quality of the feature space containing the new scale measure is evaluated in texture segmentation experiments by comparing results to those achieved with Gabor filters. It turns out that one can gain a total speedup of factor 2 without loosing any quality concerning the discrimination of textures

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence

Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered