Download or read book Feature Extraction and Image Processing for Computer Vision written by Mark Nixon and published by Academic Press. This book was released on 2012-12-18 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. 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 exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. - Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews - 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 - The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

Download or read book Feature Extraction and Image Processing written by Mark Nixon and published by Elsevier. This book was released on 2013-10-22 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Texture Feature Extraction Techniques for Image Recognition written by Jyotismita Chaki and published by Springer Nature. This book was released on 2019-10-24 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Biomedical Image Processing written by Thomas Martin Deserno and published by Springer Science & Business Media. This book was released on 2011-03-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: In modern medicine, imaging is the most effective tool for diagnostics, treatment planning and therapy. Almost all modalities have went to directly digital acquisition techniques and processing of this image data have become an important option for health care in future. This book is written by a team of internationally recognized experts from all over the world. It provides a brief but complete overview on medical image processing and analysis highlighting recent advances that have been made in academics. Color figures are used extensively to illustrate the methods and help the reader to understand the complex topics.

Download or read book A Beginner s Guide to Image Shape Feature Extraction Techniques written by Jyotismita Chaki and published by CRC Press. This book was released on 2019-07-25 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Content Based Image Classification written by Rik Das and published by CRC Press. This book was released on 2020-12-17 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB® codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/

Download or read book Image Feature Detectors and Descriptors written by Ali Ismail Awad and published by Springer. This book was released on 2016-02-22 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition.

Download or read book Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition Analysis and Image Processing written by Kamila, Narendra Kumar and published by IGI Global. This book was released on 2015-11-30 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: ###############################################################################################################################################################################################################################################################

Download or read book Feature Extraction Construction and Selection written by Huan Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Swarm Evolutionary and Memetic Computing written by Bijaya Ketan Panigrahi and published by Springer. This book was released on 2011-12-15 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: These two volumes, LNCS 7076 and LNCS 7077, constitute the refereed proceedings of the Second International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2011, held in Visakhapatnam, India, in December 2011. The 124 revised full papers presented in both volumes were carefully reviewed and selected from 422 submissions. The papers explore new application areas, feature new bio-inspired algorithms for solving specific hard optimization problems, and review the latest progresses in the cutting-edge research with swarm, evolutionary, and memetic computing in both theoretical and practical aspects.

Download or read book Biometric Authentication written by David Zhang and published by Springer Science & Business Media. This book was released on 2004-07 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Conference on Biometric Authentication, ICBA 2004, held in Hong Kong, China in July 2004. The 104 revised full papers presented were carefully reviewed and selected from 157 submissions; also included are summaries of 3 biometric competitions on fingerprint verification, face authentication, and signature verification. The papers are organized in topical sections on face, fingerprint, iris, signature, speech, biometric fusion and risk analysis, and other biometric issues.

Download or read book Feature Extraction Image Processing written by Mark Nixon and published by Elsevier. This book was released on 2008-01-08 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Practical Deep Learning for Cloud Mobile and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Download or read book Unsupervised Feature Extraction Applied to Bioinformatics written by Y-h. Taguchi and published by Springer Nature. This book was released on 2019-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.

Download or read book Feature Extraction and Image Processing for Computer Vision written by Mark Nixon and published by Academic Press. This book was released on 2012-09-25 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. 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 exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews 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 The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

Download or read book Deep Learning for Vision Systems written by Mohamed Elgendy and published by Manning Publications. This book was released on 2020-11-10 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

Download or read book Perspectives on Content Based Multimedia Systems written by Jian Kang Wu and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimedia data comprising of images, audio and video is becoming increasingly common. The decreasing costs of consumer electronic devices such as digital cameras and digital camcorders, along with the ease of transportation facilitated by the Internet, has lead to a phenomenal rise in the amount of multimedia data generated and distributed. Given that this trend of increased use of multimedia data is likely to accelerate, there is an urgent need for providing a clear means of capturing, storing, indexing, retrieving, analyzing and summarizing such data. Content-based access to multimedia data is of primary importance since it is the natural way by which human beings interact with such information. To facilitate the content-based access of multimedia information, the first step is to derive feature measures from these data so that a feature space representation of the data content can be formed. This can subsequently allow for mapping the feature space to the symbol space (semantics) either automatically or through human intervention. Thus, signal to symbol mapping, useful for any practical system, can be successfully achieved. Perspectives on Content-Based Multimedia Systems provides a comprehensive set of techniques to tackle these important issues. This book offers detailed solutions to a wide range of practical problems in building real systems by providing specifics of three systems built by the authors. While providing a systems focus, it also equips the reader with a keen understanding of the fundamental issues, including a formalism for content-based multimedia database systems, multimedia feature extraction, object-based techniques, signature-based techniques and fuzzy retrieval techniques. The performance evaluation issues of practical systems is also explained. This book brings together essential elements of building a content-based multimedia database system in a way that makes them accessible to practitioners in computer science and electrical engineering. It can also serve as a textbook for graduate-level courses.