Results 211 to 220 of about 44,456 (260)

An optimum feature extraction method for texture classification

Expert Systems With Applications, 2009
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced.
Engin Avci, Abdülkadir Şengür, Davut Hanbay   +2 more
exaly   +2 more sources

Combined Feature Extraction and Selection in Texture Analysis

2016 9th International Symposium on Computational Intelligence and Design (ISCID), 2016
Texture analysis is an important research content in pattern recognition and computer vision, and we can get important information from the image through it. As an important method in feature extraction and classification, texture analysis has a very wide range of applications in the field of scientific research and engineering technology.
Mengmeng Li, Zhigang Shang
exaly   +2 more sources

Role of correlated noise in textural features extraction

Physica Medica, 2021
Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the ...
Carlos, Huerga, Ana, Morcillo, Luis, Alejo, Alberto, Marín, Alba, Obesso, Daniela, Travaglio, Jose, Bayón, David, Rodriguez, Monica, Coronado   +8 more
openaire   +2 more sources

Texture Feature Extraction and Classification

2001
This paper describes a novel technique for texture feature extraction and classification. The proposed feature extraction technique uses an Auto-Associative Neural Network (AANN) and the classification technique uses a Multi-Layer Perceptron (MLP) with a single hidden layer. The two approaches such as AANN-MLP and statistical-MLP were investigated. The
Brijesh K. Verma, Siddhivinayak Kulkarni
openaire   +1 more source

A Method of the Extraction of Texture Feature

2008
In order to understand the emotional information of the color image, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the `semantic gap' between the visual features and the richness of human perception. In this paper, we firstly get the ROI using the Eye tracker and divide every image into
Haifang Li 0001, Lihuan Men, Junjie Chen 0001   +2 more
openaire   +1 more source

Analysis of 3D Textures Based on Features Extraction

2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018
This paper details an efficient method for the analysis of textures in the 3-dimensional space. The Local Binary Patterns (LBP) and the Grey Level Co-occurrence Matrix (GLCM) which are successfully used in various applications and the Decimal Descriptor Patterns (DDP) which is a new promising method are compared.
Samah Yahia, Yassine Ben Salem, Mohamed Naceur Abdelkrim   +2 more
openaire   +1 more source

Texture feature extraction based on primitive analysis

International Conference on Acoustics, Speech, and Signal Processing, 2003
The authors examine feature extraction from textures with weakly coupled primitives. Three simple techniques are presented for primitive extraction. Texture features are defined as means and standard deviations of the first-order distributions of primitive attributes such as area, perimeter, compactness, orientation, eccentricity, color, and contrast ...
T. N. Tan, Anthony G. Constantinides
openaire   +1 more source

Texture description using statistical feature extraction

2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016
Texture description becomes nowadays very important for the understanding of the visual content of the images. Several approaches are proposed in the last decades and are generally categorized into two large families: statistical and Structural.
Marouane Ben Haj Ayech, Hamid Amiri
openaire   +1 more source

ESVC-based extraction and segmentation of texture features

Computers & Geosciences, 2012
Inspired by Krige' variogram and the multi-channel filtering theory for human vision information processing, this paper proposes a novel algorithm for segmenting the textures based on experimental semi-variogram function (ESVF), which can simultaneously describe structural property and statistical property of textures.
Jingan Yang, Yanbin Zhuang, Feng Wu
openaire   +1 more source

Glaucoma detection using texture features extraction

2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017
Glaucoma is a second leading cause of the disease in the world. The World Health Organization has estimated that by 2020, about 80 million people would suffer from glaucoma. As the disease progresses, it leads to structural changes in the Optic Nerve Head (ONH). Optic Nerve Head is the region which consists of Optic Cup and Optic Disc.
N. Kavya, K. V. Padmaja
openaire   +1 more source