نتایج جستجو برای: texture features
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The aim of the study described in this Chapter was to develop and implement 3-D texture features that quantitatively describe the nuclear chromatin distribution. Thirty-five features thoughtfully chosen from 4 categories of 3-D texture features (discrete texture features, Markovian features, fractal features, grey value distribution features) were selected and tested for invariance properties (...
Texture can describe a wide variety of surface characteristics and a key component for human visual perception and plays an important role in image-related applications. This paper proposes a scheme for texture image classification using visual perceptual texture features and Gabor wavelet features. Three new texture features which are proved to be in accordance with human visual perceptions ar...
Texture has long been regarded as spatial distributions of gray-level variation, and texture analysis has generally been connned to the 2-D image domain. Introducing the concept of \3-D world texture", this paper considers texture as a function of 3-D structures and proposes a set of \3-D textural features". The proposed 3-D features appear to have a great potential in terrain classiication. Ex...
introduction: the process of discriminating among benign and malignant hyperplasia begun with subjective methods using light microscopy and is now being continued with computerized morphometrical analysis requiring some features. one of the main features called volume percentage of stroma (vps) is obtained by calculating the percentage of stroma texture. currently, this feature is calculated by...
In this paper, analysis of the feature selection for scale invariance texture image retrieval using fuzzy logic classifier and wavelet and co-occurrence matrix based feature is carried out. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Cooccurrence matrix. Energy and Standard Deviation are obtained from each sub-band of DWT coefficients u...
BACKGROUND Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS Endoscopic images contain rich texture and color information. Novel schemes are developed to extract...
In this paper, we investigate a new approach to the cooccurrence matrix currently used to extract textural features: co-occurrence matrices for volumetric data. While traditional texture metrics have concentrated on 2D texture, 3D imaging modalities are becoming more and more prevalent, providing the possibility of examining texture as a volumetric phenomenon. Just as computer graphics have use...
A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM’s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated.
Content-Based Image Retrieval has been a major area of research in recent years. Efficient image retrieval with high precision would require an approach which combines usage of both the color and texture features of the image. In this paper we propose a method for enhancing the capabilities of texture based feature extraction and further demonstrate the use of these enhanced texture features in...
In this paper, we present a theoretically and computationally simple but efficient approach for rotation invariant texture classification. This method is based on new texture signatures extracted from spectrum. Rotation invariant texture features are obtained based on the extension of the derived signatures. The features are tested with 1000 randomly rotated samples of 20 Brodatz texture classe...
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