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Image Texture Feature Extraction Using GLCM Approach

TL;DR: An application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images shows that these texture features have high discrimination accuracy, requires less computation time and hence efficiently used for real time Pattern recognition applications.
Abstract: Feature Extraction is a method of capturing visual content of images for indexing & retrieval. Primitive or low level image features can be either general features, such as extraction of color, texture and shape or domain specific features. This paper presents an application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images. The Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment, and Entropy are computed using Xilinx FPGA. The results show that these texture features have high discrimination accuracy, requires less computation time and hence efficiently used for real time Pattern recognition applications.
Citations
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Journal ArticleDOI
TL;DR: This survey provides a comprehensive survey of the texture feature extraction methods and identifies two classes of methods that deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.
Abstract: Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

268 citations


Cites methods from "Image Texture Feature Extraction Us..."

  • ...Furthermore, the GLCM algorithm is easy to implement and has been shown to give very good results in a large fields of applications (see [25])....

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Journal ArticleDOI
Xiaoping Liu1, Jialv He1, Yao Yao1, Jinbao Zhang1, Haolin Liang1, Huan Wang1, Ye Hong1 
TL;DR: This study proposes a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine and demonstrates the effectiveness of the strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model.
Abstract: Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones ...

232 citations


Cites methods from "Image Texture Feature Extraction Us..."

  • ...The grey-level co-occurrence matrix (GLCM) effectively describes the patterns of images and textures (Hua et al. 2006, Mohanaiah et al. 2013)....

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Journal ArticleDOI
TL;DR: A review of the state of art techniques used in computer-aided diagnostic systems for dermoscopy, by giving the domain aspects of melanoma followed by the prominent Techniques used in each of the steps, and presents cognizance to judge the consequentiality of every methodology utilized in the literature.

178 citations

Journal ArticleDOI
TL;DR: Results show that the SVE classifier with proposed multi-types features outperformed regular machine learning-based classifiers for wafer maps defect detection.
Abstract: A wafer map contains a graphical representation of the locations about defect pattern on the semiconductor wafer, which can provide useful information for quality engineers. Various defect patterns occur due to increasing wafer sizes and decreasing features sizes, which makes it very complex and unreliable process to identify them. In this paper, we propose a voting ensemble classifier with multi-types features to identify wafer map defect patterns in semiconductor manufacturing. Our research contents can be summarized as follows. First, three distinctive features such as density-, geometry-, and radon-based features were extracted from raw wafer images. Then, we applied four machine learning classifiers, namely logistic regression (LR), random forests (RFs), gradient boosting machine (GBM), and artificial neural network (ANN), and trained them using extracted features of original data set. Then their results were combined with a soft voting ensemble (SVE) technique which assigns higher weights to the classifiers with respect to their prediction accuracy. Consequently, we got performance measures with accuracy, precision, recall, ${F}$ -measure, and AUC score of 95.8616%, 96.9326%, 96.9326%, 96.7124%, and 99.9114%, respectively. These results show that the SVE classifier with proposed multi-types features outperformed regular machine learning-based classifiers for wafer maps defect detection.

119 citations

Journal ArticleDOI
Jianbo Yu1, Xiaolei Lu1
TL;DR: In this article, a manifold learning-based wafer map defect detection and recognition system is proposed to discover intrinsic manifold information that provides the discriminant characteristics of the defect patterns, and an unsupervised version of JLNDA is further developed to provide a monitoring chart for defect detection of wafer maps.
Abstract: In semiconductor manufacturing processes, defect detection and recognition in wafer maps have received increasing attention from semiconductor industry. The various defect patterns in wafer maps provide crucial information for assisting engineers in recognizing the root causes of the fabrication problems and solving them eventually. This paper develops a manifold learning-based wafer map defect detection and recognition system. In this system, a joint local and nonlocal linear discriminant analysis (JLNDA) is proposed to discover intrinsic manifold information that provides the discriminant characteristics of the defect patterns. An unsupervised version of JLNDA (called local and nonlocal preserving projection) is further developed to provide a monitoring chart for defect detection of wafer maps. A JLNDA-based Fisher discriminant is further put forward for online defect recognition without the modeling procedure of recognizers. Comparisons with other regular methods, principal component analysis, local preserving projection, linear discriminant analysis (LDA) and local LDA, illustrate the superiority of JLNDA in wafer map defect recognition. The effectiveness of the proposed system has been verified by experimental results from a real-world data set of wafer maps (WM-811K).

115 citations


Cites methods from "Image Texture Feature Extraction Us..."

  • ...Gray level co-occurrence matrix (GLCM) [33] is used to extract texture features in wafer maps....

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  • ...The typical statistical features [23], [33], i....

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  • ...GLCM is a tabulation of how often different combinations of pixel grey levels occurring in an image....

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  • ...The typical statistical features [23], [33], i.e., energy (f25), contract (f26), correlations (f27), entropy (f28), and uniformity (f29) are achieved on GLCM. 4) Projection Features: Radon transform-based features from wafer maps are also considered [5] in this study....

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TL;DR: In this paper, the authors presented CoLD (colorectal lesions detector) an innovative detection system to support colorect cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy.

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TL;DR: In this paper, three generic RAM-based architectures are proposed to efficiently construct the corresponding two-dimensional architectures by use of the line-based method for any given hardware architecture of one-dimensional (1-D) wavelet filters, including conventional convolution-based and lifting-based algorithms.
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TL;DR: Digital Design and Modeling with VHDL and Synthesis introduces V HDL with closely related practical design examples, simulation waveforms, and schematics so you can better understand their correspondence and relationship.
Abstract: From the Publisher: Combines VHDL and synthesis in an easy-to-follow step-by-step sequence. This approach addresses common mistakes and hard-to-understand concepts in a way that eases learning. Digital Design and Modeling with VHDL and Synthesis introduces VHDL with closely related practical design examples, simulation waveforms, and schematics so you can better understand their correspondence and relationship. This book is the result of the K.C. Chang's extensive experience in both design and teaching. Many of the design techniques and design considerations, illustrated throughout the chapters, are examples of real designs.

63 citations