Feature Extraction, First Edition (23 results)

Condition: Good. 1st. Used book that is in clean, average condition without any missing pages.

Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: Very Good. 1st Edition. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Paperback. Condition: Like New. First Edition. A nearly new like copy bound in the publisher's stiff card covers, not showing any creases, tears or chips. Contents fresh and clean. Not from a library so no such stamps or labels. 187 pages. Includes illustrations and diagrams. Scarce.

Paperback. Condition: new. Paperback. 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). This book introduces a range of image color feature extraction techniques. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: New. 1st ed. Special order direct from the distributor.

Hardcover. Condition: new. Hardcover. 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 thetext 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: New. 1st Edition. Special order direct from the distributor.

Hardcover. Condition: new. Hardcover. Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in creation of image databases. These repositories contain images from a diverse range of modalities, multidimensional as well as co-aligned multimodality images. These image collections offer opportunity for evidence-based diagnosis, teaching, and research. Advances in medical image analysis over last two decades shows there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. This book emphasizes the design of wavelet filter-banks as efficient and effective feature descriptors for medical image retrieval.Firstly, a generalized novel design of a family of multiplier-free orthogonal wavelet filter-banks is presented. In this, the dyadic filter coefficients are obtained based on double-shifting orthogonality property with allowable deviation from original filter coefficients. Next, a low complex symmetric Daub-4 orthogonal wavelet filter-bank is presented. This is achieved by slightly altering the perfect reconstruction condition to make designed filter-bank symmetric and to obtain dyadic filter coefficients. In third contribution, the first dyadic Gabor wavelet filter-bank is presented based on slight alteration in orientation parameter without disturbing remaining Gabor wavelet parameters. In addition, a novel feature descriptor based on the design of adaptive Gabor wavelet filter-bank is presented. The use of Maximum likelihood estimation is suggested to measure the similarity between the feature vectors of heterogeneous medical images. The performance of the suggested methods is evaluated on three different publicly available databases namely NEMA, OASIS and EXACT09. The performance in terms of average retrieval precision, average retrieval recall and computational time are compared with well-known existing methods. Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in creation of image databases. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. This book reports on an outstanding thesis thathas significantly advanced the state-of-the-art in the automated analysis andclassification of speech and music. Itdefines several standard acoustic parameter sets and describes theirimplementation in a novel, open-source, audio analysis framework calledopenSMILE, which has been accepted and intensively used worldwide. The bookoffers extensive descriptions of key methods for the automatic classificationof speech and music signals in real-life conditions and reports on theevaluation of the framework developed and the acoustic parameter sets that wereselected. It is not only intended as a manual for openSMILE users, but also andprimarily as a guide and source of inspiration for students and scientists involvedin the design of speech and music analysis methods that can robustly handlereal-life conditions. This book reports on an outstanding thesis thathas significantly advanced the state-of-the-art in the automated analysis andclassification of speech and music. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. 2019. 1st Edition. Hardcover. . . . . .

Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Softcover. Condition: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.

Hardcover. Condition: Very Good. 1. Auflage. Unread, with some shelfwear. Immediately dispatched from Germany.