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This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
Hierarchical Texture Segmentation using Wavelet packet decomposition performs an unsupervised classification of Texture features, extracted using wavelet packet decomposition to generate a segmented image. Recursive decomposition of both the approximation and the detail coefficients derived from the original signal provides a wider spectrum for richer feature extraction. This Texture Segmentation...
The Color and texture information have been the primitive image descriptors in content based image retrieval systems. This work describes an image retrieval method which uses color and texture approach for feature extraction. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color and texture. For segmenting images, JSEG (J-Segmentation) algorithm...
In this work the image is segmented effectively based on texture feature by reducing the noise. For effective image segmentation Expectation-Maximization (EM) algorithm based on Gabor filter is used. The EM algorithm is applied on 2D Ultrasonic image of uterus and tested. The Gabor function has been recognized by its multiresolution properties and the precision of locating the texture features in...
The aim of this paper is segmenting a sequence of images containing dynamic textures. The proposed method is based on means of features extracted from spatio-temporal cooccurrence matrices that characterize the textures themselves as well as their movements. Features with the highest discriminating power are selected according to a supervised scheme that permits to represent the dynamic textures in...
This paper proposes a novel framework for color texture segmentation based on Discrete Reduced Biquaternion Fourier Transform (DRBFT) and Discrete Wavelet Transform (DWT). Reduced biquaternions (RB), which are an extension of the complex numbers, are used to characterize the color information. Multi-Channel wavelet filtering is used to extract the texture information at various scales from the LUV...
Doppler imaging allows evaluation of blood flow patterns, direction, and velocity. The color (red, blue, and mosaic) signify the direction of the blood flow. By analyzing this color Doppler, it is possible to detect heart diseases like mitral and aortic stenosis, mitral, tricuspid, and aortic regurgitation, and Left Ventricle (LV) hypertrophy. We present 3 methods to extract low level features namely...
In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the...
This paper presents the study of vocal videostroboscopic videos to detect morphological pathologies using a combination of motion information and segmentation. The motion permits us to obtain the keyframes of total (or minimum) closure and maximum opening and to have the initialization for the segmentation process. The segmentation is made analyzing the image textures applying Gabor filtering. After...
This paper presents a technique for image segmentation. We demonstrate its efficacy for classsifying high-resolution aerial images. The application is peak water flow estimation in a river catchment in the city of Zurich and the data covers a large rural and urban setting. The output of the segmentation process is used as input to a hydrological model. We introduce a combined, probabilistic, segmentation...
In conventional researches, satisfying results cannot be achieved when directly applying Mean Shift segmentation onto high spatial resolution (HR) remote sensing image. The proposed method addresses this problem and extents Mean Shift clustering algorithm into high-dimensional feature space by extracting texture and shape descriptor. The dilemma in image segmentation is that the algorithms with good...
In this paper textured image segmentation of urban areas using spectral and direction features is lionizing. Texture characterization specifically is very complex when the image data composed of several spectral bands at different wavelength. This topic is very important, especially in the case of remotely sensed hyperspectral images of urban areas, in which hundreds of spectral bands are often available...
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