This document presents a method for fabric defect detection using wavelet transforms and genetic algorithms. Wavelet transforms are used to extract coefficients from sample fabric images, and a genetic algorithm selects an optimal subset of coefficients that best identify defects. Two separate coefficient sets are determined, one for horizontal defects and one for vertical defects, to improve accuracy. Experimental results on two fabric image databases demonstrate that the technique can effectively detect various defect types and configurations after applying thresholding and denoising post-processing steps to the wavelet-filtered images.
Texture classification of fabric defects using machine learning IJECEIAES
In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.
Computer aided classification of Bascal cell carcinoma using adaptive Neuro-f...Editor IJMTER
For skin lesion detection pathologists examine biopsies to make diagnostic
assessment largely based on cell anatomy and tissue distribution. However in many
instances it is subjective and often leads to considerable variability. Whereas computer
diagnostic tools enable objective judgments by making use of quantitative measures.
Paper presents a diagnosis system based on an adaptive Neuro-fuzzy inference system
for effective classification of Bascal cell carcinoma images from the given set of all types
of skin lesions. System divide in three parts. Image Processing, Feature Extraction, and
classification. First part deals with the noise reduction and artifacts removing from the
set of images. Second part deals with extracting variety of features of Bascal Cell
Carcinoma using the Greedy feature flip algorithm (G-flip), and classification method
using ANFIS algorithm and finally Part three deals with the results that is classification
of BCC images from the variety of pre-cancerous stage images that is Actinic Keratosis
and also other images called psoriasis which looks as cancer images at a first look . The
results confirmed that the proposed ANFIS model has potential in classifying the skin
cancer diagnosis.
USING SPECTRAL ESTIMATION TECHNIQUE AND ROUGH SET CLASSIFIER OF REGULAR PATTE...ijiert bestjournal
Fabric detection has an outstanding importance in inspection of fabric quality and defects. It
works on principle of spectral estimation technique vision. it gets bonded to locate defected
regions accurately. This project represented a most accepted method for patterned fabric defect
detection and classification using spectral estimation technique and rough set theory. To extract
the Regular pattern from the image of the patterned fabric, here use of Estimating Signal
Parameter via Rotational Invariance Technique (ESPRIT) is done. In this technique the defected
region i.e. the shape and location of the flawed areas are detected by comparing the pattern
image and the source image also the rough set classifier is trained and tested to detect the types
of defects in the patterned fabric image. Practically it is observed that this method can
successfully be used to analyze & find the defects in patterned fabrics with nearly 96% success
rate. This method is result oriented and better improving than previous method.
The document presents a study that implemented segmentation and classification techniques for mammogram images to detect breast cancer malignancy. It used Gray Level Difference Method (GLDM) and Gabor texture feature extraction methods with Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) classifiers. The results showed that GLDM features with SVM achieved the best classification accuracy of 95.83%, outperforming the other combinations. The study concluded the GLDM and SVM approach provided the most effective classification of mammogram images.
This document presents a novel technique for detecting the breast boundary (also known as the skin-air interface or skin-line) in mammogram images using entropy estimation. The proposed method applies a logarithmic transform to increase contrast near the skin line, calculates entropy across the image which changes significantly at the boundary, and uses an exponential transform to enhance boundary detection. The algorithm was tested on 103 mammogram images and evaluated by an expert, achieving accurate boundary detection. The method provides a noise resistant way to detect the important but low-contrast breast boundary for use in computer-aided diagnosis systems.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
This document presents a method for classifying silk sarees using fuzzy logic. Texture features like Tamura features (coarseness, contrast, etc.) and Haralick features (contrast, homogeneity, etc.) are extracted from images of silk saree samples. These features are used to frame fuzzy logic rules to classify new saree images as pure silk or not. Over 500 rules are defined using the feature datasets. This fuzzy classification approach achieves higher accuracy than other methods like k-NN and SVM, without requiring large databases due to incorporating features into fuzzy membership functions. The method was tested on 800 silk saree images.
BAYESIAN CLASSIFICATION OF FABRICS USING BINARY CO-OCCURRENCE MATRIXijistjournal
Classification of fabrics is usually performed manually which requires considerable human efforts. The goal of this paper is to recognize and classify the types of fabrics, in order to identify a weave pattern automatically using image processing system. In this paper, fabric texture feature is extracted using Grey Level Co-occurrence Matrices as well as Binary Level Co-occurrence Matrices. The Co-occurrence matrices functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, and then extracting statistical measures from this matrix. The extracted features from GLCM and BLCM are used to classify the texture by Bayesian classifier to compare their effectiveness.
Texture classification of fabric defects using machine learning IJECEIAES
In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.
Computer aided classification of Bascal cell carcinoma using adaptive Neuro-f...Editor IJMTER
For skin lesion detection pathologists examine biopsies to make diagnostic
assessment largely based on cell anatomy and tissue distribution. However in many
instances it is subjective and often leads to considerable variability. Whereas computer
diagnostic tools enable objective judgments by making use of quantitative measures.
Paper presents a diagnosis system based on an adaptive Neuro-fuzzy inference system
for effective classification of Bascal cell carcinoma images from the given set of all types
of skin lesions. System divide in three parts. Image Processing, Feature Extraction, and
classification. First part deals with the noise reduction and artifacts removing from the
set of images. Second part deals with extracting variety of features of Bascal Cell
Carcinoma using the Greedy feature flip algorithm (G-flip), and classification method
using ANFIS algorithm and finally Part three deals with the results that is classification
of BCC images from the variety of pre-cancerous stage images that is Actinic Keratosis
and also other images called psoriasis which looks as cancer images at a first look . The
results confirmed that the proposed ANFIS model has potential in classifying the skin
cancer diagnosis.
USING SPECTRAL ESTIMATION TECHNIQUE AND ROUGH SET CLASSIFIER OF REGULAR PATTE...ijiert bestjournal
Fabric detection has an outstanding importance in inspection of fabric quality and defects. It
works on principle of spectral estimation technique vision. it gets bonded to locate defected
regions accurately. This project represented a most accepted method for patterned fabric defect
detection and classification using spectral estimation technique and rough set theory. To extract
the Regular pattern from the image of the patterned fabric, here use of Estimating Signal
Parameter via Rotational Invariance Technique (ESPRIT) is done. In this technique the defected
region i.e. the shape and location of the flawed areas are detected by comparing the pattern
image and the source image also the rough set classifier is trained and tested to detect the types
of defects in the patterned fabric image. Practically it is observed that this method can
successfully be used to analyze & find the defects in patterned fabrics with nearly 96% success
rate. This method is result oriented and better improving than previous method.
The document presents a study that implemented segmentation and classification techniques for mammogram images to detect breast cancer malignancy. It used Gray Level Difference Method (GLDM) and Gabor texture feature extraction methods with Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) classifiers. The results showed that GLDM features with SVM achieved the best classification accuracy of 95.83%, outperforming the other combinations. The study concluded the GLDM and SVM approach provided the most effective classification of mammogram images.
This document presents a novel technique for detecting the breast boundary (also known as the skin-air interface or skin-line) in mammogram images using entropy estimation. The proposed method applies a logarithmic transform to increase contrast near the skin line, calculates entropy across the image which changes significantly at the boundary, and uses an exponential transform to enhance boundary detection. The algorithm was tested on 103 mammogram images and evaluated by an expert, achieving accurate boundary detection. The method provides a noise resistant way to detect the important but low-contrast breast boundary for use in computer-aided diagnosis systems.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
This document presents a method for classifying silk sarees using fuzzy logic. Texture features like Tamura features (coarseness, contrast, etc.) and Haralick features (contrast, homogeneity, etc.) are extracted from images of silk saree samples. These features are used to frame fuzzy logic rules to classify new saree images as pure silk or not. Over 500 rules are defined using the feature datasets. This fuzzy classification approach achieves higher accuracy than other methods like k-NN and SVM, without requiring large databases due to incorporating features into fuzzy membership functions. The method was tested on 800 silk saree images.
BAYESIAN CLASSIFICATION OF FABRICS USING BINARY CO-OCCURRENCE MATRIXijistjournal
Classification of fabrics is usually performed manually which requires considerable human efforts. The goal of this paper is to recognize and classify the types of fabrics, in order to identify a weave pattern automatically using image processing system. In this paper, fabric texture feature is extracted using Grey Level Co-occurrence Matrices as well as Binary Level Co-occurrence Matrices. The Co-occurrence matrices functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, and then extracting statistical measures from this matrix. The extracted features from GLCM and BLCM are used to classify the texture by Bayesian classifier to compare their effectiveness.
Dimensionality Reduction Evolution and Validationiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A combined method of fractal and glcm features for mri and ct scan images cla...sipij
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale
complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give ownership of the local image
structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM
Features. This powerful combination has proved good results especially in classification of medical texture
from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical
diagnostics tests for osteoporosis pathologies.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
This paper proposes a new fuzzy similarity measure called Fuzzy Monotonic Inclusion (FMI) to measure similarity between images for image retrieval systems. The FMI approach segments images into regions, extracts features for each region, and maps the features into a fuzzy similarity model based on fuzzy inclusion. Experimental results on the Label Me image dataset show the FMI approach achieves higher precision than other methods like Unified Feature Matching and Fuzzy Histogram in identifying images by semantic class.
The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Ve...ijaia
In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is
presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to
represent for each user’s signature model based on the prior information collected. Different from the early
works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve
Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better
approximation of the user model is assured. Experiments on a database from the First International
Signature Verification Competition(SVC 2004) confirm that this combined algorithm yields quite a
satisfactory result.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
Character recognition is a new research field in the domain of pattern recognition which deals with the
style of writing. Some of the challengeable problems in character identification are changing in the style of
writing, font and turns of words and etc. In this paper, the goal is Persian character identification using
independent orthogonal moment as the feature extraction technique.The proposed feature extraction
method is the combination of Pseudo-Zernike Moment and Fourier-Mellin Moment called Pseudo-Zernike-
Mellin Moment to extract feature vector from Persian characters. The proposed character identification
system is evaluated on the HODA dataset and obtained 97.76% acceptance rate.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
1) The document presents a novel algorithm to segment overlapped and touching human chromosome images automatically.
2) The algorithm first obtains chromosome contours and calculates a discrete curvature function to identify concave points on the contours.
3) It then detects interesting points like concave and convex points and uses these to plot possible separation lines between chromosomes.
4) The algorithm segments the overlapped chromosomes using a curvature function scheme to separate them, which previous studies have found to be successful.
This document presents a novel fuzzy k-nearest neighbor equality (FK-NNE) algorithm for classifying masses in mammograms as benign or malignant. The algorithm assigns membership values to different classes based on distances to k-nearest neighbors. It achieved 94.46% sensitivity, 96.81% specificity, and 96.52% accuracy, outperforming k-nearest neighbors, fuzzy k-nearest neighbors, and k-nearest neighbor equality algorithms. The algorithm considers relative importance of neighbors and assigns partial membership to classes, addressing issues with insufficient knowledge faced by other techniques. Experimental results demonstrated FK-NNE had the best performance with an area under the ROC curve of 0.9734, indicating high diagnostic accuracy.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
Multimodal authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
framework for providing more secure key in biometric security aspect. Initially the features will be
extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
and then verified with the measure MSE. This model would give better outcome when compared with SVD
factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
Change Detection from Remotely Sensed Images Based on Stationary Wavelet Tran...IJECEIAES
The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.
In this paper person identification is done based on sets of facial images. Each facial image is considered as the scattered point of logistic regression. The vertical distance of scattered point of facial image and the regression line is considered as the parameter to determine whether the image is of same person or not. The ratio of Euclidian distance (in terms of number of pixel of gray scale image based on ‘imtool’ of Matlab 13.0) between nasal and eye points are determined. The variance of the ration is considered another parameter to identify a facial image. The concept is combined with ghost image of Principal Component Analysis; where the mean square error and signal to noise ratio (SNR) in dB is considered as the parameters of detection. The combination of three methods, enhance the degree of accuracy compared to individual one.
IRJET- Proposed System for Animal Recognition using Image ProcessingIRJET Journal
The document proposes an animal recognition system using image processing. It would use PCA algorithms and eigenfaces methods to identify animals from images captured by existing cameras in nature reserves. This would allow authorities to be alerted of the presence of dangerous animals so people in the reserves can live without fear of attack. The system would analyze images to extract features, calculate covariances and eigenvectors to identify animals based on training data from five classes of animals. If implemented, it could automate animal detection compared to relying on human monitoring of video feeds.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
Takashi Murakami is a Japanese contemporary artist known for blending pop art and Japanese otaku culture. He founded the art collective Kaikai Kiki Co., Ltd. in 1995 and has since become one of the most influential contemporary artists working today. Murakami's work often features flat, graphic figures and bright colors inspired by anime, manga, and advertising. He is considered a leading figure in the globalization of Japanese popular culture through his art.
Lecture of textile fiber introduction for studentsBILAL ABDULLAH
The document discusses the results of a study on the effects of exercise on memory and thinking abilities in older adults. The study found that regular exercise can help reduce the decline in thinking abilities that often occurs with age. Older adults who exercised regularly performed better on cognitive tests and brain scans showed they had greater activity in important areas for memory and learning compared to less active peers.
This document discusses fibers as trace evidence in forensic science. It covers Locard's Principle of exchange, and describes different types of fibers including natural plant, animal, and mineral fibers as well as man-made cellulose and petroleum plastic fibers. Specific fiber types are given like cotton, wool, silk, asbestos, rayon, nylon, and polyester. The document notes that fibers can be observed and identified through a microscope.
Although these tests are different depending on buyers’ requirements & it needs a vast discussion. But I’ll discuss on very basic things shortly for only garments probationers. Please keep in mind what I wanted to say in my writings, there are so many ways here to perform a job, through day by day practices, you’ll find the easiest way for you to do your job well.
Dimensionality Reduction Evolution and Validationiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A combined method of fractal and glcm features for mri and ct scan images cla...sipij
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale
complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give ownership of the local image
structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM
Features. This powerful combination has proved good results especially in classification of medical texture
from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical
diagnostics tests for osteoporosis pathologies.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
This paper proposes a new fuzzy similarity measure called Fuzzy Monotonic Inclusion (FMI) to measure similarity between images for image retrieval systems. The FMI approach segments images into regions, extracts features for each region, and maps the features into a fuzzy similarity model based on fuzzy inclusion. Experimental results on the Label Me image dataset show the FMI approach achieves higher precision than other methods like Unified Feature Matching and Fuzzy Histogram in identifying images by semantic class.
The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Ve...ijaia
In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is
presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to
represent for each user’s signature model based on the prior information collected. Different from the early
works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve
Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better
approximation of the user model is assured. Experiments on a database from the First International
Signature Verification Competition(SVC 2004) confirm that this combined algorithm yields quite a
satisfactory result.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
Character recognition is a new research field in the domain of pattern recognition which deals with the
style of writing. Some of the challengeable problems in character identification are changing in the style of
writing, font and turns of words and etc. In this paper, the goal is Persian character identification using
independent orthogonal moment as the feature extraction technique.The proposed feature extraction
method is the combination of Pseudo-Zernike Moment and Fourier-Mellin Moment called Pseudo-Zernike-
Mellin Moment to extract feature vector from Persian characters. The proposed character identification
system is evaluated on the HODA dataset and obtained 97.76% acceptance rate.
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
1) The document presents a novel algorithm to segment overlapped and touching human chromosome images automatically.
2) The algorithm first obtains chromosome contours and calculates a discrete curvature function to identify concave points on the contours.
3) It then detects interesting points like concave and convex points and uses these to plot possible separation lines between chromosomes.
4) The algorithm segments the overlapped chromosomes using a curvature function scheme to separate them, which previous studies have found to be successful.
This document presents a novel fuzzy k-nearest neighbor equality (FK-NNE) algorithm for classifying masses in mammograms as benign or malignant. The algorithm assigns membership values to different classes based on distances to k-nearest neighbors. It achieved 94.46% sensitivity, 96.81% specificity, and 96.52% accuracy, outperforming k-nearest neighbors, fuzzy k-nearest neighbors, and k-nearest neighbor equality algorithms. The algorithm considers relative importance of neighbors and assigns partial membership to classes, addressing issues with insufficient knowledge faced by other techniques. Experimental results demonstrated FK-NNE had the best performance with an area under the ROC curve of 0.9734, indicating high diagnostic accuracy.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
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Multimodal authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
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extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
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factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
Change Detection from Remotely Sensed Images Based on Stationary Wavelet Tran...IJECEIAES
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National Flags Recognition Based on Principal Component Analysisijtsrd
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Takashi Murakami is a Japanese contemporary artist known for blending pop art and Japanese otaku culture. He founded the art collective Kaikai Kiki Co., Ltd. in 1995 and has since become one of the most influential contemporary artists working today. Murakami's work often features flat, graphic figures and bright colors inspired by anime, manga, and advertising. He is considered a leading figure in the globalization of Japanese popular culture through his art.
Lecture of textile fiber introduction for studentsBILAL ABDULLAH
The document discusses the results of a study on the effects of exercise on memory and thinking abilities in older adults. The study found that regular exercise can help reduce the decline in thinking abilities that often occurs with age. Older adults who exercised regularly performed better on cognitive tests and brain scans showed they had greater activity in important areas for memory and learning compared to less active peers.
This document discusses fibers as trace evidence in forensic science. It covers Locard's Principle of exchange, and describes different types of fibers including natural plant, animal, and mineral fibers as well as man-made cellulose and petroleum plastic fibers. Specific fiber types are given like cotton, wool, silk, asbestos, rayon, nylon, and polyester. The document notes that fibers can be observed and identified through a microscope.
Although these tests are different depending on buyers’ requirements & it needs a vast discussion. But I’ll discuss on very basic things shortly for only garments probationers. Please keep in mind what I wanted to say in my writings, there are so many ways here to perform a job, through day by day practices, you’ll find the easiest way for you to do your job well.
Vol. I discusses textiles including manufacturing, fiber types, and classification. Vol. II focuses on the Indian textile industry and Surat market. Surat is the largest producer of sarees in the world, with over 4.7 lakh powerlooms. It faces challenges like labor shortages and lack of branding. However, innovations like the new textiles university and Global Fabric Resource Centre aim to develop skills and introduce new markets.
The document discusses several types of fabric innovations:
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2. Chamba Rumal is a traditional embroidery art from Himachal Pradesh being revived through government training programs. Known for intricate designs depicting mythology and nature.
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This document discusses different types of fabrics, including natural fabrics like cotton, silk, linen, jute, and wool that are formed by nature, as well as man-made fabrics like polyester, rayon, denim, vinyl, fiberglass, and polyethylene. It describes fabrics as materials that can be used for garments, shoes, carpets, and more, and notes they come in two forms - woven fabrics or non-woven fabrics. The document also lists some common applications of fabrics, such as for clothing, dust collection, construction sites, industrial uses, and daily items.
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This document discusses various methods for identifying textile fibers, including non-technical and technical tests. Non-technical tests include feeling, burning, and microscopic analysis. The burning test observes each fiber's reaction to heat, including flame characteristics, odor, and ash properties. Technical tests like microscopic examination and chemical analysis require specialized equipment and knowledge but can more precisely identify fiber blends and properties. Microscopy reveals unique structures of natural and man-made fibers. Chemical tests use solvents and reagents to distinguish fibers based on their different solubility properties.
An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
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minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
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An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
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iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
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Comparative Study of Morphological, Correlation, Hybrid and DCSFPSS based Mor...IDES Editor
This paper proposes comparative study of two basic
approaches such as Morphological Approach (MA) and
Correlation Approach (CA) and three modified algorithms
over the basic approaches for detection of micronatured defects
occurring in plain weave fabrics. A Hybrid of CA followed by
MA was developed and has shown to overcome the drawbacks
of the basic methods. As automation of MA using DC
Suppressed Fourier Power Spectrum Sum (DCSFPSS),
DCSFPSSMA could not yield improvement in Overall
Detection Accuracy (ODA) for micronatured defects,
automation of modified Hybrid Approach (HA) was proposed
leading to the development of Tribrid Approach (TA). Modified
Hybrid approach involves cascade operation of CA and MA
both automated using DCSFPSS. Texture periodicity of defect
free fabric was obtained using DCSFPSS which was extended
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for CA and for the design of the size of structuring element
for morphological filtering process. Overall Detection
Accuracy was used by adopting simple binary based defect
search algorithm as the last step in the experimentation to
detect the defects. Overall Detection Accuracy was found to be
~100%/97.41%/ 98.7 % for 247 samples of warp break defect/
double pick/ normal samples and 96.1% /99% for 205 thick
place defect samples/normal samples belonging to two
different plain grey fabric classes. Robustness of the
performance of TA scheme was tested by comparing TA with
two traditional algorithms viz., CA and MA and our previously
proposed hybrid algorithm and DCSFPSSMA. This TA
algorithm outperformed when compared to CA-only, MA-only,
HA and DCSFPSSMA by yielding an overall ODA of more
than 98% for the defect and defect free samples of different
fabric classes. Secondly, the recognition of defect area less
than 1 mm2 which has not been reported in the literature yet,
was possible using this algorithm. We propose to use this
method as a means to grade the grey fabric similar to the
standard fabric grading system.
Detection of Damage in Beam from Measured Natural Frequencies Using Support V...Subhajit Mondal
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Bayesian-Network-Based Algorithm Selection with High Level Representation Fee...ITIIIndustries
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Constructing a classification model is important in machine learning for a particular task. A
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Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
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medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
This document presents a new approach for segmenting skin lesions in dermoscopic images using a fixed-grid wavelet network (FGWN). The FGWN takes R, G, and B color values as inputs and determines the network structure without training. The image is then segmented and the exact lesion boundary is extracted. Experimental results on 30 images showed the FGWN approach achieved better segmentation accuracy than other methods according to 11 evaluation metrics, extracting lesion boundaries more precisely. In conclusion, the FGWN provides an effective tool for automated skin lesion segmentation in dermoscopy images.
A comparative study of dimension reduction methods combined with wavelet tran...ijcsit
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transform. This study is carried out for mammographic image classification. It is performed in three stages:
extraction of features characterizing the tissue areas then a dimension reduction was achieved by four
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THE APPLICATION OF BAYES YING-YANG HARMONY BASED GMMS IN ON-LINE SIGNATURE VE...ijaia
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Fabric Textile Defect Detection, By Selection A Suitable Subset Of Wavelet Coefficients, Through Genetic Algorithm
1. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 25
Fabric Textile Defect Detection, By Selecting A Suitable Subset
Of Wavelet Coefficients, Through Genetic Algorithm
Narges Heidari narges_hi@yahoo.com
Department Of Computer
Islamic Azad University, ilam branch
Ilam, Iran
Reza Azmi azmi@alzahra.ac.ir
Department Of Computer
Alzahra University
Tehran, Iran
Boshra Pishgoo boshra.pishgoo@yahoo.com
Department Of Computer
Alzahra University
Tehran, Iran
Abstract
This paper presents a novel approach for defect detection of fabric textile. For this purpose, First,
all wavelet coefficients were extracted from an perfect fabric. But an optimal subset of These
coefficients can delete main fabric of image and indicate defects of fabric textile. So we used
Genetic Algorithm for finding a suitable subset. The evaluation function in GA was Shannon
entropy. Finally, it was shown that we can gain better results for defect detection, by using two
separable sets of wavelet coefficients for horizontal and vertical defects. This approach, not only
increases accuracy of fabric defect detection, but also, decreases computation time.
Keywords: Fabric Textile Defect Detection, Genetic Algorithm, Wavelet Coefficients.
1. INTRODUCTION
In loom industry, it's very important to control quality of textile generation. Though, loom
machines improvement, decreases defects in fabric textile, but this process can't perform quite
perfect [1]. In the past, human resource performed defect detection process in factories but this
tedious operation, depended on their attention and experiment. So we couldn't expect high
accuracy from them. In other words, according to experimental results that were shown in Ref [1],
human eyesight system can recognize just 50-70% of all defects in fabric textile. In the other
hand, Ref [2] claimed that human's accuracy in fabric defect detection isn't higher than 80%. So,
it seems that automatic evaluation of fabric defects is very necessary.
Because of improvement in image processing and pattern recognition, automatic fabric defect
detection can presents an accurate, quick and reliable evaluation of textile productions. since
fabric evaluation is very important in industrial applications, Different researches were done on
fabric textile, surface of wood, tile, aluminum and etc [2,3,4,5,6]. [3] presented a method for fabric
defect detection based on distance difference. In this method, user can adjust some parameters,
according to the type of textile. [7] evaluated effects of using regularity and local directions as two
features for fabric textile defect detection. [8] used Gabor filters for feature extraction and
Gaussian Mixture model for detection and classification of fabric defects. [9] used structure
characters of fabrics for defect detection and [10] was done this process by using adaptive local
binary patterns.
2. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 26
Many defect detection systems that were presented up now, work on offline mode. In this mode, if
occur some defects in a part of the fabric textile because of machine's problem, all parts of the
fabric will be defective. But in online mode, since defect detection operation perform on textile
generation time, we can stop generation process as soon as occurs a defection. The goal of this
paper is to presents a method for online processing with acceptable accuracy and speed in defect
detection.
Here, we used wavelet filter for fabric defect detection. This filter puts special information in each
quarter of filtered image. In two dimensional wavelet transform, initial image divides to four parts.
The first part is an approximation of initial image and the other parts include components with
high frequencies or details of image, so vertical, horizontal and diagonal defects can be found in
part 2, 3 and 4 respectively. There is a problem for using wavelet transform in fabric defect
detection. The wavelet coefficients that are suitable for a special fabric, may aren't suitable for the
other one. So in this paper, we used separable wavelet coefficients sets for each fabric.
After extracting all wavelet coefficients from a perfect fabric, we found a suitable subset of these
coefficients using genetic algorithm. Then applied this suitable subset to other images and gain
the available defects in each of them. In fact, our final goal is to design a system that can
recognize defective and perfect fabrics. This paper is organized as follows: in Section 2 and 3, we
briefly introduce Wavelet transform and Genetic algorithm. Section 4 describes the proposed
method for fabric defect detection and Our experimental results is shown in Section 5.
2. WAVELET TRANSFORM
Wavelet transform analyses the signal by a group of orthogonal functions in the form of Ψm,n(x),
that computed from translation and dilation of the mother wavelet Ψ. These orthogonal functions
are shown in Equation 1.
(1)
m and n are real numbers in this equation. Analysis and synthesis formulas are shown in
Equation 2 and 3 respectively.
(2)
(3)
Coefficients of this transform are obtained recursively. A synthesis of level j are shown in
Equation 4.
(4)
In two dimensional wavelet transform, initial image in each level, divides to four parts. The first
part is an approximation of initial image and the other parts include components with high
frequencies of image and show edge points in vertical, horizontal and diagonal directions.
Figure1-(a) and (b) show this partitioning for the first and the second order of DWT on a 256 X
256 pixels image. After applying the first order of DWT on image, it is divided to four images IHH,
ILL, ILH and IHL where H and L are high and low bounds respectively [11,12]. This operation can be
performed For each of these four images repeatedly that the result is shown in part (b). Figure 2
shows a real example and the result of applying the first order of DWT on it in part (a) and (b)
respectively.
3. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 27
ILL ILH
ILLLL ILLLH ILHLL ILHLH
ILLHL ILLHH ILHHL ILHHH
IHL IHH
IHLLL IHLLH IHHLL IHHLH
IHLHL IHLHH IHHHL IHHHH
(a) (b)
FIGURE 1: decomposition of an 256 x 256 pixels Image for the (a) first order, (b) second order of DWT
(a) (b)
FIGURE 2: (a): an image, (b): the result of applying the first order of DWT on (a)
Wavelet transform evaluates the difference of information in two different resolutions and creates
a multi-resolution view of image's signal [11], so these transformation are useful for fabric defect
detection.
3. GENETIC ALGORITHM
Genetic Algorithm is a stochastic optimization method. One of the main characteristic of this
algorithm is that it leaves a set of best solutions in population. This algorithm has some operators
for selecting the best solutions, crossing over and mutation in each generation [13]. GA starts
with an initial and random population of solutions. Then a function that called fitness function, are
calculated for each solution. Among initial population, solutions that their fitness values are higher
than the others, are selected for next generation. In the other hand, Crossover and mutation
operators add new solutions to next generation. this operation performs repeatedly to at the final,
the algorithm converges to a good solution. In this paper, we used genetic algorithm for finding a
suitable subset of wavelet coefficients of a special fabric textile.
The simplest form of GA involves three types of operators: [14]
• Selection: This operator selects chromosomes in the population for reproduction. The
fitter the chromosome, the more times it is likely to be selected to reproduce.
256
128
4. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 28
• Crossover: This operator exchanges subsequences of two chromosomes to create two
offspring. For example, the strings 10000100 and 11111111 could be crossed over after
the third locus in each to produce the two offspring 10011111 and 11100100. This
operator roughly mimics biological recombination between two single-chromosome
(haploid) organisms.
• Mutation: This operator randomly flips some bits in a chromosome. For example, the
string 00000100 might be mutated in its second position to yield 01000100. Mutation can
occur at each bit position in a string with some probability, usually very small (e.g.,
0.001).
the simple GA works as follows: [14]
1. Start with a randomly generated population of N L-bit chromosomes (candidate solutions
to a problem).
2. Calculate the fitness F(x) of each chromosome x in the population.
3. Repeat the following steps (a)-(c) until N offspring have been created:
a. Select a pair of parent chromosomes from the current population, with the
probability of selection being an increasing function of fitness. Selection is done
with replacement," meaning that the same chromosome can be selected more
than once to become a parent.
b. With probability Pc (the crossover probability), cross over the pair at a randomly
chosen point (chosen with uniform probability) to form two offspring. If no
crossover takes place, form two offspring that are exact copies of their respective
parents.
c. Mutate the two offspring at each locus with probability Pm (the mutation
probability), and place the resulting chromosomes in the new population.
4. Replace the current population with the new population.
5. If the stopping condition has not satisfied, go to step 2.
4. FABRIC DEFECT DETECTION
Our proposed method has two phases. the First phase includes extracting all wavelet coefficients
from a perfect fabric image and the second one includes finding a suitable subset of all
coefficients using genetic algorithm. This subset can delete main fabric of image and indicate
defects of fabric textile. So after these two phases, we can apply the suitable subset to the other
images and gain the available defects in each of them.
Jasper in [15] showed that if the optimization problem in Eq. 5 can be solved, we can find a
suitable subset of wavelet coefficients.
Where
(5)
In this equation, J function is the normal form of high frequency components of image, n is the
number of coefficients, c is a subset of wavelet coefficients as a solution and p is an array of
image's pixel values. Our goal is to find a c subset that minimize J function. For this purpose, we
can use genetic algorithm. In this algorithm, each solution c is a chromosome and different fitness
functions can be used. In this paper we evaluated some fitness functions but sum of squares and
entropy functions had the best results.
Using a wavelet coefficients subset as a filter that filters images in both horizontal and vertical
directions, has not good results for real fabrics that aren't symmetric. So by using two separable
5. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 29
filters for horizontal and vertical directions, we can obtain the better results. If W1 and W2 be
horizontal and vertical filters respectively and U be a perfect image, then the filtered image F, can
be shown as Equation 6.
(6)
Now we must calculate a fitness function for filtered image F. as mentioned previously, in this
paper, we used sum of squares and entropy functions. These functions can be shown in Eq. 7
and 8 respectively.
(7)
(8)
In Eq. 7, n and m are the number of lines and columns in image and F(i , j) is the pixel value of
image, in line i and column j. in Eq. 8, n is the number of gray levels that here is 256 and d(i) is
probability of gray level i in filtered image F. this probability is a real number between 0 and 1. For
example if 1024 pixels among all pixels have the same gray level value like 100, then can be
written: d(100) = 1/64.
With these fitness functions in Eq. 7 and 8, the optimization problem that were shown in Eq. 5,
can be written in form of Eq. 9 and 10 respectively. As mentioned previously, our purpose is to
minimize J function.
(9)
(10)
The entropy function optimizes wavelet coefficients based on monotony of background. So if the
entropy function value be small, the image will be more monotonic. But the sum of squares
function optimizes image by minimizing its pixel values. furthermore entropy function has simpler
computation in comparison with sum of squares. So we continued our experiments with entropy
function as fitness function of GA.
5. EXPERIMENTAL RESULTS
In this paper, for evaluating the proposed technique, we used two image databases that are
called Tilda [16] and Brodatz [17]. Tilda includes 8 groups of different fabric textile types and
Each group has 50 images with 512 x 768 pixels. Brodatz includes 112 types of fabric images
with 640 x 640 pixels.
Figure 3 shows a defective fabric textile of Tilda database and the filtered image in part (a) and
(b) respectively. The wavelet coefficients of each quarter was optimized by GA while the fitness
function of GA was entropy. This Figure shows that the entropy function has good quality,
because the background is monotonic and defects are appeared clearly.
6. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 30
(a) (b)
FIGURE 3 : (a) a defective fabric textile, ( b) filtered image by suitable coefficients for each quarter
In this experiment, first a perfect image from each fabric used for training phase and a suitable
subset of its wavelet coefficients for each quarter was obtained. Figure 4 shows the histogram of
quarter 4 of Figure 3-(b). this Figure shows a normal distribution. Most of pixels that are related to
background are placed in the middle of distribution but defects are shown in the end of each
littoral. So by selecting two thresholds in proximity of these two littoral, we can perform a
thresholding process on the image and obtain a clear image for place detection of defects. The
image can be more clear by applying some denoising and normalization processes on it.
FIGURE 4: histogram of quarter 4 of Figure 3-(b)
Figure 5-(a) is a fabric textile with diagonal defects. Part (b) of this Figure shows quarter 4 of the
filtered image that diagonal defects was appeared in it. Part (c) and (d) show the result of
applying thresholding and denoising processes on part (b) and (c) respectively. Now part (d) of
Figure 5 has suitable quality for defect detection.
7. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 31
(a) (b)
(c) (d)
FIGURE 5: (a) a fabric with diagonal defects, (b) the quarter 4 of filtered image, (c) the result of applying
thresholding process on part (b), (d) the result of applying denoising process on part (c)
Figure 6-(a) is a fabric textile with horizontal defects. Part (b) of this Figure shows quarter 3 of the
filtered image that horizontal defects was appeared in it. Part (c) shows the result of applying
thresholding on part (b), but this result is not suitable for denoising because the size of noise and
defect areas are same nearly. So in this example we applied sliding window method on image in
part (b) of Figure 6. This method builds 2 windows with size and for each pixel of
image, Then calculates the standard deviation of each window and assignes the result of
Equation (11) to central pixel as new value of it. In this Equation T is a suitable threshold.
New value of Central pixel = (11)
Part (d) of Figure 6 shows this result. In this image, size of defect areas are very larger than size
of noise areas. So denoising process can be done on part (d). the result of denoising process is
shown in part (e). Now part (e) of Figure 6 has suitable quality for defect detection.
8. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 32
(a) (b)
(c) (d)
(e)
FIGURE 6: (a) a fabric with horizontal defects, (b) the quarter 3 of filtered image, (c) the result of applying
thresholding process on part (b), (d) the result of applying sliding window method on part (b), (e) the result
of applying denoising process on part (d)
In this experiment, first a perfect image from each fabric used for training phase and a suitable
subset of its wavelet coefficients for each quarter, was obtained. Then in the test phase, the
other perfect or defective images evaluated by our proposed technique. Table 1 and 2 show
these results for Tilda and Brodatz databases, respectively. Since genetic algorithm starts with a
9. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 33
random initial population, solutions that are produced by each population, must be same or close
to each other. This characteristic is called repetition's capability. We tested this capability and its
results were shown in Table3. In this table, for each quarter of two different fabric types, mean
and standard deviation of ten runs with different initial populations were shown.
Fabric
Type
Image
number
Defective
image
number
Perfect
image
number
Detected
defective
image
number
Detected
perfect
image
number
Accuracy
of correct
detection
C1r1 18 12 6 11 6 94.4%
C1r2 22 14 8 14 7 95.4%
C2r3 20 16 4 15 4 95%
TABLE 1 : Fabric defect detection results for Tilda database
Fabric
Type
Image
number
Defective
image
number
Perfect
image
number
Detected
defective
image
number
Detected
perfect
image
number
Accuracy
of correct
detection
D77 100 23 77 19 76 95%
D79 100 27 73 23 70 93%
D105 100 56 44 51 39 90%
TABLE 2 : Fabric defect detection results for Brodatz database
Fabric type Quarter number Mean Standard deviation
C1r1e3 2 5.9208 0.1092
C1r1e3 3 6.0158 0.1286
C1r1e3 4 5.9466 0.1297
C2r2e2 2 5.7401 0.1607
C2r2e2 3 5.8144 0.1707
C2r2e2 4 5.7530 0.1356
TABLE 3 : results of repetition's capability
6. CONCLUSION
As mentioned previously, in this paper we used a novel method for fabric textile defect detection.
This process had two phases. the First phase includes extracting all wavelet coefficients from a
perfect fabric image and the second one includes finding a suitable subset of all coefficients using
genetic algorithm. The experimental results showed that our method have good accuracy for
defect detection.
10. Narges Heidari, Reza Azmi & Boshra Pishgoo
International Journal of Image Processing (IJIP), Volume (5) : Issue (1) : 2011 34
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