Texture Discrimination Problem Research Articles

Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD

Fatty liver is a prevalent disease and is the major cause for the dysfunction of the liver. If fatty liver is untreated, it may progress into chronic diseases like cirrhosis, hepatocellular carcinoma, liver cancer, etc. Early and accurate detection of fatty liver is crucial to prevent the fatty liver progressing into chronic diseases. Based on the severity of fat, the liver is categorized into four classes, namely Normal, Grade I, Grade II and Grade III respectively. Ultrasound scanning is the widely used imaging modality for diagnosing the fatty liver. The ultrasonic texture of liver parenchyma is specific to the severity of fat present in the liver and hence we formulated the quantification of fatty liver as a texture discrimination problem. In this paper, we propose a novel algorithm to discriminate the texture of fatty liver based on curvelet transform and SVD. Initially, the texture image is decomposed into sub-band images with curvelet transform enhancing gradients and curves in the texture, then an absolute mean of the singular values are extracted from each curvelet decomposed image, and used it as a feature representation for the texture. Finally, a cubic SVM classifier is used to classify the texture based on the extracted features. Tested on a database of 1000 image textures with 250 image textures belonging to each class, the proposed algorithm gave an accuracy of 96.9% in classifying the four grades of fat in the liver.

Surface Defect Inspection for Vehicle Heat Exchangers Based on LBP and SVM

To tackle the problems appeared in traditional surface defect inspection for vehicle heat exchangers which is mainly depended on manual execution, machine vision has been introduced due to its unparalleled characteristics of on-line, fast, high precision and non-contact. In this paper, an automatic surface defect detection algorithm employing machine vision technology has been presented. Treating the surface defect detection as a texture discrimination problem, the proposed method first uses an LBP (Local Binary Pattern) descriptor to represent the image of the heat exchanger, which has efficiently alleviated the effect of uneven illumination. Then the whole texture image has been partitioned into several non-overlapped 32×32 blocks and each block is subject to classification by an SVM (Support Vector Machine) to judge if it is a defect area. The experiments have been performed and the results have demonstrated the effectiveness and the efficiency of the proposed method.

Rough fuzzy set based scale space transforms and their use in image analysis

In this paper we present a multi-scale method based on the hybrid notion of rough fuzzy sets, coming from the combination of two models of uncertainty like vagueness by handling rough sets and coarseness by handling fuzzy sets. Marrying both notions lead to consider, as instance, approximation of sets by means of similarity relations or fuzzy partitions. The most important features are extracted from the scale spaces by unsupervised cluster analysis, to successfully tackle image processing tasks. Here, we report some results achieved by applying the method to multi-class image segmentation and edge detection, but it can be shown to be successfully applied to texture discrimination problem too.

Using multiband correlation models for the invariant recognition of 3-D hyperspectral textures

We develop a method for the recognition of three-dimensional (3-D) textures in hyperspectral images. Textures are modeled using subspaces of multiband correlation functions that represent texture variability over a range of solar angles and atmospheric conditions. These subspace models are used to enable texture recognition that is invariant to these environmental variables. The multiband correlation model captures within and between spectral band spatial characteristics. We present a method that can be used to optimize the selection of the multiband correlation functions for a given texture discrimination problem. We demonstrate the effectiveness of the approach using texture recognition experiments that consider 2016 texture samples from 168 hyperspectral images that were synthesized for a 3-D scene over a range of conditions. The results show that 3-D textures can be accurately recognized over a wide range of conditions using a small number of multiband correlation functions.

Textural analysis of range images

This paper addresses the problem of texture discrimination in range images. The range data is transformed to a common coordinate system, and then resampled to generate a regular grid of points in order to eliminate the effect of different sampling rates. Eight statistical textural features based on co-occurrence matrices are computed on the resampled data, and are used to discriminate between two classes of natural surfaces consisting of pebbles of different sizes lying on a plane. Seven of the eight textural features are found to be useful in discriminating between these two classes. The experiments also confirm the importance of resampling the data before computing the textural features.

A Random Walk Procedure for Texture Discrimination

We consider the problem of texture discrimination. Random walks are performed in a plain domain D bounded by an absorbing boundary ¿ and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Both problems of texture discrimination and edge segment detection can be solved using the same random walk approach. The border distributions and their differences with respect to a homogeneous image can classify two different images as having similar or dissimilar textures. The existence of an edge segment is concluded if the boundary distribution for a given window (subimage) differs significantly from the boundary distribution for a homogeneous (uniform grey level) window. The random walk procedure has been implemented and results of texture discrimination are shown. A comparison is made between results obtained using the random walk approach and the first-or second-order statistics, respectively. The random walk procedure is intended mainly for the texture discrimination problem, and its possible application to the edge detection problem (as shown in this paper) is just a by-product.