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1 – 10 of 187
Article
Publication date: 29 September 2021

Swetha Parvatha Reddy Chandrasekhara, Mohan G. Kabadi and Srivinay

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using…

Abstract

Purpose

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

Design/methodology/approach

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

Findings

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

Originality/value

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

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 graylevel image for extracting textures. The graylevel 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: 3 February 2020

Huosong Xia, Yuting Meng, Wuyue An, Zixuan Chen and Zuopeng Zhang

Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the…

Abstract

Purpose

Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.

Design/methodology/approach

This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.

Findings

Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.

Originality/value

The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

Details

Information Discovery and Delivery, vol. 48 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 1 May 2008

Maroš Tunák and Aleš Linka

This paper deals with a procedure that recognizes common defects occurring in woven fabric. Images of woven fabric are considered as having a directional texture due to…

Abstract

This paper deals with a procedure that recognizes common defects occurring in woven fabric. Images of woven fabric are considered as having a directional texture due to their periodical nature. We used a statistical approach based on the analysis of periodicity of texture images in horizontal and vertical directions. These periodicities correspond to the periodicity of second-order grey level statistical features obtained from a grey level co-occurrence matrix. A set of five significant features is extracted from the matrix: energy, correlation, homogeneity, cluster shade and cluster prominence. The presence of a defect over texture causes regular structure changes and consequently, statistical changes. Detection algorithm is based on the sliding window technique; the window is moved over the whole image area. We counted the test statistic for every window and the multivariate control charts are used as a tool for judging the existence of defects. The results show that the statistical approach is suitable for detection of directional defects or changes in regular structure in analysed simulated and real fabrics.

Details

Research Journal of Textile and Apparel, vol. 12 no. 2
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 14 September 2015

Hong Liu, Haijun Wei, Lidui Wei, Jingming Li and Zhiyuan Yang

This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear…

Abstract

Purpose

This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles.

Design/methodology/approach

In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk’s memory size, size of image samples, different inputting vectors and different classifiers were compared.

Findings

The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems.

Originality/value

This paper combined DTW algorithm with wear particle identification problem.

Details

Industrial Lubrication and Tribology, vol. 67 no. 6
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 9 February 2021

Yaolin Zhu, Jiayi Huang, Tong Wu and Xueqin Ren

The purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.

Abstract

Purpose

The purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.

Design/methodology/approach

To increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.

Findings

The improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.

Originality/value

In this paper, a novel identification method which extracts the optimal feature parameter is proposed.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 May 2004

S. Shaikhzadeh Najar, R. Ghazi Saeidi, M. Latifi, A.Ghazi Saeidi and A.H. Rezaei

This paper describes fabric inspection system aided by computer vision to detect and classify defects in circular knitted fabrics using different common…

Abstract

This paper describes fabric inspection system aided by computer vision to detect and classify defects in circular knitted fabrics using different common texture-recognition methods, including co-occurrence matrices, the discrete Fourier transform, wavelets, Gabor, and clustering. The images of the fabrics were broadly classified into six classes: cracks, holes, vertical stripes, horizontal stripes, soil freckles, and defect-free. One hundred and twenty images (256 gray level and 100 dpi) containing 20 images of defect-free fabrics (rib 1x1) as well as 100 images corresponding to five different categories were used. In general, one-half of the images in each category were employed for training and the remaining images were used for testing.

The application of the clustering method applied in this work was found to be highly promising at identifying defects in knitted fabrics. With an overall success rate of 91.6%, the clustering method has a higher efficiency value than all of the other methods. In the case of the wavelet and Gabor methods, the results are acceptable. However, the overall success rates of the co-occurrence matrix and Fourier transform methods in recognizing defects in knitted fabrics are not acceptable.

Article
Publication date: 1 May 2000

Xiaohong Wang and Mu Yao

Traditionally, the grade of fabric’s crease recovery with twisting is decided by comparing the processed sample fabric with standard sample photograph under some…

269

Abstract

Traditionally, the grade of fabric’s crease recovery with twisting is decided by comparing the processed sample fabric with standard sample photograph under some conditions. This method is completely reliant on subjective appraisal, so it is easy to lead to some subjective error and affect the conclusion. In this paper, we use image processing and texture analysis technique to calculate a few of the parameters which describe the fabric’s crease recovery properties. At the same time, we use the Fuzzy priority similarity comparison method to assess the fabric’s crease recovery properties synthetically.

Details

International Journal of Clothing Science and Technology, vol. 12 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 28 November 2018

Ning Zhang, Ruru Pan, Lei Wang, Shanshan Wang, Jun Xiang and Weidong Gao

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using…

Abstract

Purpose

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Design/methodology/approach

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

Findings

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

Originality/value

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 14 August 2017

Padmavati Shrivastava, K.K. Bhoyar and A.S. Zadgaonkar

The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision, in gathering knowledge about the structure, content and…

Abstract

Purpose

The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision, in gathering knowledge about the structure, content and the surrounding environment of a real-world natural scene, at a quick glance accurately. This paper proposes a set of novel features to determine the gist of a given scene based on dominant color, dominant direction, openness and roughness features.

Design/methodology/approach

The classification system is designed at two different levels. At the first level, a set of low level features are extracted for each semantic feature. At the second level the extracted features are subjected to the process of feature evaluation, based on inter-class and intra-class distances. The most discriminating features are retained and used for training the support vector machine (SVM) classifier for two different data sets.

Findings

Accuracy of the proposed system has been evaluated on two data sets: the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes. The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy, using ten-fold cross validation approach. The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.

Originality/value

The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification. The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap. The proposed feature evaluation technique is general and can be applied across any domain.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

1 – 10 of 187