In this paper, we propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.

The dipole-dipole interaction in the quantum mechanical treatment of the matter-radiation dynamics, is shown to give rise to split energy levels reminiscent of the nonlinear coupled spectral features as well as a self-sustained coherent modes. Wiener's theory of nonlinear random processes is applied to the second harmonic generation (SHG), leading also to coupled spectral pulling and dipping features, due to the dual noise sources in the fundamental and the harmonic polarizations. Furthermore, the nonlinear spectral features are suggested to be applied to implement quantum (qubit) gates for computation.

In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.

A method for the recognition of Arabic printed scripts entered from an image scanner is presented. The method uses the Hough transformation (HT) to extract features, Dynamic programming (DP) matching technique, and a topological classifier to recognize the characters. A process of characters recognition is further divided into four parts: preprocessing, segmentation of a word into characters, features extraction, and characters identification. The preprocessing consists of the following steps: smoothing to remove noise, baseline drift correction by using HT, and lines separation by making an horizontal projection profile. After preprocessing, Arabic printed words are segmented into characters by analysing the vertical and the horizontal projection profiles using a threshold. The character or stroke obtained from the segmentation process is normalized in size, then thinned to provide it skeleton from which features are extracted. As in the procedure of straight lines detection, a threshold is applied to every cell and those cells whose count is greater than the threshold are selected. The coordinates (R, θ) of the selected cells are the extracted features. Next, characters are classified in two steps: In the first one, the character main body is classified using DP matching technique, and features selected in the HT space. In the second one, simple topological features extracted from the geometry of the stress marks are used by the topological classifier to completely recognize the characters. The topological features used to classify each type of the stress mark are the width, the height, and the number of black pixels of the stress marks. Knowing both the main group of the character body and the type of the stress mark (if any), the character is completely identified.