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Computer vision

See below for a selection of the latest books from Computer vision category. Presented with a red border are the Computer vision books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Computer vision books and those from many more genres to read that will keep you inspired and entertained. And it's all free!

Feature Extraction and Classification Techniques for Text Recognition

Feature Extraction and Classification Techniques for Text Recognition

Author: Munish Kumar Format: Hardback Release Date: 30/01/2020

In recent years there have been significant developments in the area of text recognition and document analysis. Measuring the relevant features contained in text is an important task in the recognition process, as the performance of the recognition system depends on the quality of features that are being extracted. As the volume of data keeps growing, it has become increasingly challenging to extract useful information from these sets. Feature Extraction and Classification Techniques for Text Recognition is a collection of innovative research on the fusion and hybridization of various features and classifiers for document analysis and recognition. While highlighting topics including adaptive boosting, writer identification, and signature verification, this book is ideally designed for academicians, researchers, industry professionals, developers, analysts, forensics specialists, scholars, and students seeking current research on the advancements and developing methods in document analysis and text recognition.

Challenges and Applications for Implementing Machine Learning in Computer Vision

Challenges and Applications for Implementing Machine Learning in Computer Vision

Author: Ramgopal Kashyap Format: Hardback Release Date: 30/12/2019

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

Machine Vision and Image Recognition

Machine Vision and Image Recognition

Author: Jovan Pehcevski Format: Hardback Release Date: 30/12/2019

Machine Vision and Image Recognition informs the readers about the behavior fusion for visually guided service robots and the approaches and limitation in achieving vision in machine. The readers are informed about the use of beacon tracker for dynamic omnidirectional vision localization and explained the QoE assessment of will transmission. They are explained about the concepts that are there to be learnt in neuromorphic vision systems and told about an automatic assembling system for sealing rings. Also discussed in the book is the design of omnidirectional tilt sensor, modelling and positioning system in sand casting process, raising sow and procreating of processing, augmented reality applications, 3D autonomous navigation line, all of which and even more is based on machine vision.

Deep Learning with PyTorch 1.x Implement deep learning techniques and neural network architecture variants using Python, 2nd Edition

Deep Learning with PyTorch 1.x Implement deep learning techniques and neural network architecture variants using Python, 2nd Edition

Author: Laura Mitchell, Sri. Yogesh K., Vishnu Subramanian Format: Paperback / softback Release Date: 29/11/2019

Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.x Key Features Gain a thorough understanding of the PyTorch framework and learn to implement neural network architectures Understand GPU computing to perform heavy deep learning computations using Python Apply cutting-edge natural language processing (NLP) techniques to solve problems with textual data Book DescriptionPyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts. In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you'll explore GPU computing and how it can be used to perform heavy computations. Finally, you'll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems. By the end of this book, you'll be able to confidently and easily implement deep learning applications in PyTorch. What you will learn Build text classification and language modeling systems using neural networks Implement transfer learning using advanced CNN architectures Use deep reinforcement learning techniques to solve optimization problems in PyTorch Mix multiple models for a powerful ensemble model Build image classifiers by implementing CNN architectures using PyTorch Get up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examples Who this book is forThis book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.

Feature Extraction and Image Processing for Computer Vision

Feature Extraction and Image Processing for Computer Vision

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.

Intelligent Data Processing 11th International Conference, IDP 2016, Barcelona, Spain, October 10-14, 2016, Revised Selected Papers

Intelligent Data Processing 11th International Conference, IDP 2016, Barcelona, Spain, October 10-14, 2016, Revised Selected Papers

Author: Vadim V. Strijov Format: Paperback / softback Release Date: 16/11/2019

This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016. The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life and social sciences; morphological and technological approaches to image analysis.

Advances in Computerized Analysis in Clinical and Medical Imaging

Advances in Computerized Analysis in Clinical and Medical Imaging

Author: J Dinesh Peter Format: Hardback Release Date: 12/11/2019

Advances in Computerized Analysis in Clinical and Medical Imaging book is devoted for spreading of knowledge through the publication of scholarly research, primarily in the fields of clinical & medical imaging. The types of chapters consented include those that cover the development and implementation of algorithms and strategies based on the use of geometrical, statistical, physical, functional to solve the following types of problems, using medical image datasets: visualization, feature extraction, segmentation, image-guided surgery, representation of pictorial data, statistical shape analysis, computational physiology and telemedicine with medical images. This book highlights annotations for all the medical and clinical imaging researchers' a fundamental advances of clinical and medical image analysis techniques. This book will be a good source for all the medical imaging and clinical research professionals, outstanding scientists, and educators from all around the world for network of knowledge sharing. This book will comprise high quality disseminations of new ideas, technology focus, research results and discussions on the evolution of Clinical and Medical image analysis techniques for the benefit of both scientific and industrial developments. Features: Research aspects in clinical and medical image processing Human Computer Interaction and interface in imaging diagnostics Intelligent Imaging Systems for effective analysis using machine learning algorithms Clinical and Scientific Evaluation of Imaging Studies Computer-aided disease detection and diagnosis Clinical evaluations of new technologies Mobility and assistive devices for challenged and elderly people This book serves as a reference book for researchers and doctoral students in the clinical and medical imaging domain including radiologists. Industries that manufacture imaging modality systems and develop optical systems would be especially interested in the challenges and solutions provided in the book. Professionals and practitioners in the medical and clinical imaging may be benefited directly from authors' experiences.

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging Techniques and Applications

Vision Models for High Dynamic Range and Wide Colour Gamut Imaging Techniques and Applications

To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies.

Practical Deep Learning for Cloud and Mobile Real-World AI & Computer Vision Projects Using Python, Keras & TensorFlow

Practical Deep Learning for Cloud and Mobile Real-World AI & Computer Vision Projects Using Python, Keras & TensorFlow

Author: Anirudh Koul, Siddha Ganju, Meher Kasam Format: Paperback / softback Release Date: 01/11/2019

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 do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile 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, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's Not Hotdog, image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology

Spectral Geometry of Shapes Principles and Applications

Spectral Geometry of Shapes Principles and Applications

Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource.