This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: [l) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrax, Laws’ texture energy and Gabor multi...

Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure.  The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There  are  some  approaches  to  develop  a  reliable  noninvasive  method  of  evaluating  histological  changes  in  sonograms. The main characteristic used to distinguish between the normal...

In this paper, a new fusion texture feature with orthogonal polynomials based multiresolution subband and the Gray Level Co-occurrence Matrix (GLCM) is presented. The proposed orthogonal polynomials based multiresolution subband coefficients posses the localized frequency information and the GLCM matrices capture the structural and statistical properties from the subband coefficients for charac...

In this work, we propose a method which can extract critical points on a face using both location and texture information. This new approach can automatically learn feature information from training data. It finds the best facial feature locations by maximizing the joint distribution of location and texture parameters. We first introduce an independence assumption. Then, we improve upon this mo...

Random and natural textures classification is still one of the biggest challenges in the field of image processing and pattern recognition. In this paper, texture feature extraction using Slant Hadamard Transform was studied and compared to other signal processing-based texture classification schemes. A parametric SHT was also introduced and employed for natural textures feature extraction. We ...

In this paper, we present content based image retrieval using two features color and texture. Humans tend to differentiate images based on color, therefore color features are mostly used in CBIR. Color histogram is mostly used to represent color features but it cannot entirely characterize the image. Color Histogram is also rotation invariant about the view axis. Regularity, directionality, smo...

A detailed evaluation of the use of texture features in a query-by-example approach to image retrieval is presented. Three radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view are used. The features were evaluated and tuned on retrieval tasks from the Corel collection and then evaluated and tested on the TRECVID 2003 ...

the peak frequency [2]. When the textures are unstable, however, e.g, when peak frequencies have fluctuations, A novel neural network architecture for image simple comparison of the peak outputs may be ineffective. texture classification is introduced. The proposed Kernel The shortcomings of using the peak frequencies of each Modifying Neural Network (KM Net) which incorporates texture classes ...

The focus of this project was to modify the image feature extraction subsystem of the ImageRover system[11]. The subsystem was extended to use color and texture measures which more closely correspond to the human perception. The feature implemented for color is the color histogram in L u v color space. The texture measure implemented is 2D Wold decomposition, which incorporates the three most i...

A detailed evaluation of the use of texture features in a query-by-example approach to image retrieval is presented. Three radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view are used. The features were evaluated and tuned on retrieval tasks from the Corel collection and then evaluated and tested on the TRECVID 2003 ...