Search results

1 – 10 of over 2000
Article
Publication date: 23 March 2012

Ovidiu Ghita, Dana Ilea, Antonio Fernandez and Paul Whelan

The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary…

Abstract

Purpose

The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters.

Design/methodology/approach

The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.

Findings

The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture.

Practical implications

Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding.

Originality/value

This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

Article
Publication date: 26 June 2009

Yih‐Chih Chiou, Chern‐Sheng Lin and Guan‐Zi Chen

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

Abstract

Purpose

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

Design/methodology/approach

In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.

Findings

The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.

Originality/value

The paper presents a new approach for testing materials. The multipurpose measurement application with unsophisticated and economical equipment can be confirmed in online inspection of papers and cloth manufacturing.

Details

Sensor Review, vol. 29 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 27 March 2009

Manuel Ferreira, Cristina Santos and Joao Monteiro

The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes…

Abstract

Purpose

The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns.

Design/methodology/approach

The global system includes a texture segmentation phase and a classification phase. The approach for image texture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture uses a fuzzy grammar inference system.

Findings

The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning the diversity of industrial textures. The resulted system allows on‐line learning of new textures. This approach avoids the need for a global re‐learning of the all textures each time a new texture is presented to the system.

Practical implications

These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors for quality control operations.

Originality/value

The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested. Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. The segmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization.

Details

Sensor Review, vol. 29 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 4 April 2016

Babar Khan, Fang Han, Zhijie Wang and Rana J. Masood

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color…

Abstract

Purpose

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

Design/methodology/approach

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

Findings

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Originality/value

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

Details

Assembly Automation, vol. 36 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 14 November 2016

Yuye Wang, Guofeng Zhang and Xiaoguang Hu

Infrared simulation plays an important role in small and affordable unmanned aerial vehicles. Its key and main goal is to get the infrared image of a specific target. Infrared…

Abstract

Purpose

Infrared simulation plays an important role in small and affordable unmanned aerial vehicles. Its key and main goal is to get the infrared image of a specific target. Infrared physical model is established through a theoretical research, thus the temperature field is available. Then infrared image of a specific target can be simulated properly while taking atmosphere state and effect of infrared imaging system into account. For recent years, some research has been done in this field. Among them, the infrared simulation for large scale is still a key problem to be solved. In this passage, a method of classification based on texture blending is proposed and this method effectively solves the problem of classification of large number of images and increase the frame rate of large infrared scene rendering. The paper aims to discuss these issues.

Design/methodology/approach

Mosart Atmospheric Tool (MAT) is used first to calculate data of sun radiance, skyshine radiance, path radiance, temperatures of different material which is an offline process. Then, shader in OGRE does final calculation to get simulation result and keeps a high frame rate. Considering this, the authors convert data in MAT file into textures which can be easily handled by shader. In shader responding, radiance can be indexed by information of material, vertex normal, eye and sun. Adding the effect of infrared imaging system, the final radiance distribution is obtained. At last, the authors get infrared scene by converting radiance to grayscale.

Findings

In the fragment shader, fake infrared textures are used to look up temperature which can calculate radiance of itself and related radiance.

Research limitations/implications

The radiance is transferred into grayscale image while considering effect of infrared imaging system.

Originality/value

Simulation results show that a high frame rate can be reached while guaranteeing the fidelity.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 26 January 2010

Padmapriya Nammalwar, Ovidiu Ghita and Paul F. Whelan

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to…

Abstract

Purpose

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

Design/methodology/approach

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

Findings

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

Originality/value

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

Details

Sensor Review, vol. 30 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 17 July 2020

Pulla Rao Chennamsetty, Guruvareddy Avula and Ramarao Chunduri buchhi

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy…

Abstract

Purpose

The purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.

Design/methodology/approach

Camouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.

Findings

There are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.

Research limitations/implications

Even though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.

Practical implications

Camouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.

Social implications

Social problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.

Originality/value

The proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.

Details

International Journal of Intelligent Unmanned Systems, vol. 9 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

Open Access
Article
Publication date: 4 May 2021

Loris Nanni and Sheryl Brahnam

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or…

1313

Abstract

Purpose

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Design/methodology/approach

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

Findings

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Originality/value

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 16 March 2020

Chunlei Li, Chaodie Liu, Zhoufeng Liu, Ruimin Yang and Yun Huang

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile…

Abstract

Purpose

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

Design/methodology/approach

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

Findings

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

Originality/value

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

Details

International Journal of Clothing Science and Technology, vol. 32 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 July 2004

Chengdong Wu, Yong Yue, Mengxin Li and Osei Adjei

This paper presents a comprehensive review of the available literature on applications of the rough set theory. Concepts of the rough set theory are discussed for approximation…

2182

Abstract

This paper presents a comprehensive review of the available literature on applications of the rough set theory. Concepts of the rough set theory are discussed for approximation, dependence and reduction of attributes, decision tables and decision rules. The applications of rough sets are discussed in pattern recognition, information processing, business and finance, industry, environment engineering, medical diagnosis and medical data analysis, system fault diagnosis and monitoring and intelligent control systems. Development trends and future efforts are outlined. An extensive list of references is also provided to encourage interested readers to pursue further investigations.

Details

Engineering Computations, vol. 21 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

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