Feature Extraction and Image Processing

5.1 Overview High-level feature extraction concerns finding shapes in computer images. To be able to recognise faces automatically, for example, one approach is to extract the component features. This requires extraction of, say, ...

Author: Mark Nixon

Publisher: Elsevier

ISBN: 9780080506258

Category: Computers

Page: 350

View: 692

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Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals. Ideal module text for courses in artificial intelligence, image processing and computer vision Essential reading for engineers and academics working in this cutting-edge field Supported by free software on a companion website

Feature Extraction

0.95 1 0.9 0.85 0.8 0.75 0.861748307 0.964994 0.964377 0.964377 None(15154) KS-test(12) T-test(91) Composite (20) 0.925846 Best Feature (1) 0.9804 Ordered (48) 0.9804 SFS (4) Feature Extraction Method (# of Features) OC 1 0.697436735 ...

Author: Isabelle Guyon

Publisher: Springer Science & Business Media

ISBN: 9783540354871

Category: Computers

Page: 778

View: 239

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This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Feature Extraction Construction and Selection

Before low-dimensional statistical methods are applied, some form of feature extraction should be implemented prior to the analyses. Feature extraction is a mapping of a data vector X into a lower-dimensional feature vector Y (where ...

Author: Huan Liu

Publisher: Springer Science & Business Media

ISBN: 9781461557258

Category: Computers

Page: 410

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There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.

Image Color Feature Extraction Techniques

It is the procedure of retrieving images from a gathering based on automatically extracted features. CBIR methods support in handling of the digital images by means of computers. The common feature extraction techniques used in CBIR may ...

Author: Jyotismita Chaki

Publisher: Springer Nature

ISBN: 9789811557613

Category: Technology & Engineering

Page: 83

View: 696

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This book introduces a range of image color feature extraction techniques. Readers are encouraged to try implementing the techniques discussed here on their own, all of which are presented in a very simple and step-by-step manner. In addition, the book can be used as an introduction to image color feature techniques for those who are new to the research field and software. The techniques are very easy to understand as most of them are described with pictorial examples. Not only the techniques themselves, but also their applications are covered. Accordingly, the book offers a valuable guide to these tools, which are a vital component of content-based image retrieval (CBIR).

Feature Extraction Image Processing

4.1 Overview We shall define low-level features to be those basic features that can be extracted automatically from an image without any ... As such, thresholding is a form of low-level feature extraction performed as a point operation.

Author: Mark Nixon

Publisher: Elsevier

ISBN: 0080556728

Category: Computers

Page: 424

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Whilst other books cover a broad range of topics, Feature Extraction and Image Processing takes one of the prime targets of applied computer vision, feature extraction, and uses it to provide an essential guide to the implementation of image processing and computer vision techniques. Acting as both a source of reference and a student text, the book explains techniques and fundamentals in a clear and concise manner and helps readers to develop working techniques, with usable code provided throughout. The new edition is updated throughout in line with developments in the field, and is revised to focus on mathematical programming in Matlab. Essential reading for engineers and students working in this cutting edge field Ideal module text and background reference for courses in image processing and computer vision

Prominent Feature Extraction for Sentiment Analysis

Feature. Extraction. Methods. Various feature sets are extracted for sentiment analysis to investigate the contribution of each type of feature for sentiment analysis. Initially, four types of basic features ...

Author: Basant Agarwal

Publisher: Springer

ISBN: 9783319253435

Category: Medical

Page: 103

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The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

A Beginner s Guide to Image Shape Feature Extraction Techniques

The examples related to spatial interrelation-based shape feature extraction techniques are illustrated through MATLAB examples. Chapter 6 provides an overview of moment-based shape feature extraction techniques.

Author: Jyotismita Chaki

Publisher: CRC Press

ISBN: 9781000043983

Category: Computers

Page: 152

View: 398

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This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology.

Feature Extraction and Image Processing for Computer Vision

High-level feature extraction concerns finding shapes and objects in computer images. To be able to recognise human faces automatically, for example, one approach is to extract the component features. This requires extraction of, say, ...

Author: Mark Nixon

Publisher: Academic Press

ISBN: 9780128149775

Category: Computers

Page: 650

View: 467

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Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. The only text to concentrate on feature extraction with working implementation and worked through mathematical derivations and algorithmic methods A thorough overview of available feature extraction methods including essential background theory, shape methods, texture and deep learning Up to date coverage of interest point detection, feature extraction and description and image representation (including frequency domain and colour) Good balance between providing a mathematical background and practical implementation Detailed and explanatory of algorithms in MATLAB and Python

Texture Feature Extraction Techniques for Image Recognition

Feature extraction is called the method of transforming the input data into a set of features. Internal representation is chosen if regional characteristics such as color and texture are the main focus. Texture analysis is widely used ...

Author: Jyotismita Chaki

Publisher: Springer Nature

ISBN: 9789811508530

Category: Technology & Engineering

Page: 100

View: 343

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The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based. It also covers applications related to texture features, such as facial imaging. It is a valuable resource for machine vision researchers and practitioners in different application areas.

EEG Signal Processing and Feature Extraction

According to the previous processing, 36 components were extracted for each mode. ... This is a characteristic of the multi-domain feature extraction of ERP, when NCPD is applied on the fourth-order ERP tensor of the TFR that includes ...

Author: Li Hu

Publisher: Springer Nature

ISBN: 9789811391132

Category: Medical

Page: 437

View: 794

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This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.