Image And Video Processing And Recognition Based On Artificial Intelligence

This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.

This book focuses on challenging issues in the field of AI-based image and video processing and recognition, including the topics of AI-based image processing, understanding, recognition, compression, and reconstruction; AI-based video processing, understanding, recognition, compression, and reconstruction; computer vision based on AI; AI-based biometrics; AI-based object detection and tracking; approaches that combine AI techniques and conventional methods for image and video processing and recognition; explainable AI (XAI) for image and video processing and recognition; generative adversarial network (GAN)-based image and video processing and recognition; and approaches that combine AI techniques and blockchain methods for image and video processing and recognition.

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

This book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others. The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning,dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts. The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision.

This book has brought 24 groups of experts and active researchers around the world together in image processing and analysis, video processing and analysis, and communications related processing, to present their newest research results, exchange latest experiences and insights, and explore future directions in these important and rapidly evolving areas. It aims at increasing the synergy between academic and industry professionals working in the related field. It focuses on the state-of-the-art research in various essential areas related to emerging technologies, standards and applications on analysis, processing, computing, and communication of multimedia information. The target audience of this book is researchers and engineers as well as graduate students working in various disciplines linked to multimedia analysis, processing and communications, e.g., computer vision, pattern recognition, information technology, image processing, and artificial intelligence. The book is also meant to a broader audience including practicing professionals working in image/video applications such as image processing, video surveillance, multimedia indexing and retrieval, and so on. We hope that the researchers, engineers, students and other professionals who read this book would find it informative, useful and inspirational toward their own work in one way or another.

"Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer Vision provides in-depth and detailed knowledge about the latest research in image processing and computer vision techniques. It is a roadmap for the improvement of computer vision and image processing, explaining the machine learning algorithms and models involved. The authors differentiate between the various algorithms available and how to choose which to use for the most precise results for a specific task involving certain constraints. The volume provides real-world examples to illustrate the concepts and methods. The authors discuss machine learning in healthcare systems for detection, diagnosis, classification, and segmentation. They also explore the diverse applications of image and video processing, including image colorization and restoration using deep learning, using machine learning to record the changes in the Amazon rainforest over time with remote sensing, and more. Researchers, teachers, and students working in the field of artificial intelligence, machine learning, and computer vision will benefit from the knowledge presented here on the diverse applications of computer vision and image processing"--

This volume of original papers has been assembled to honor the achievements of Professor Thomas S Huang in the area of image processing and image analysis. Professor Huang's life of inquiry has spanned a number of decades as his work on imaging problems began in 1960's. Over these 40 years, he has made many fundamental and pioneering contributions to nearly every area of this field. Professor Huang has received numerous Awards, including the prestigious Jack Kilby Signal Processing Medal from IEEE. He has been elected to the National Academy of Engineering, and named Fellow of IEEE, Fellow of OSA, Fellow of IAPR, and Fellow of SPIE. Professor Huang has made fundamental contributions to image processing, pattern recognition, and computer vision: including design and stability test of multidimensional digital filters, digital holography; compression techniques for documents and images; 3D motion and modeling, analysis and visualization of the human face, hand and body, multi-modal human-computer interfaces; and multimedia databases. Many of his research ideas have been seminal, opening up new areas of research. Professor Huang is continuing his contribution to the field in the new millennium This book is intended to highlight his contributions by showing the breadth of areas in which his students are working. As such, contributed chapters were written by some of his many former graduate students (some with Professor Huang as a coauthor) and illustrate not only his contributions to imaging science but also his commitment to educational endeavor. The breadth of contributions is an indication of influence of Professor Huang to the field of signal processing, image processing, computer vision and applications; the book includes chapters on learning in image retrieval, facial motion analysis, cloud motion tracking, wavelet coding, robust video transmission, and many other topics. The Appendix contains several reprints of Professor Huang's most influential papers from 1970's to 1990's. This book is directed towards image processing researchers, including academic faculty, graduate students and industry researchers, as well as toward professionals working in application areas.

This cutting-edge volume focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book explains the essential modules that are necessary for comprehending artificial intelligence experiences to provide machines with the power of vision. The volume also presents innovative research developments, applications, and current trends in the field. The chapters cover such topics as visual quality improvement, Parkinson’s disease diagnosis, hypertensive retinopathy detection through retinal fundus, big image data processing, N-grams for image classification, medical brain images, chatbot applications, credit score improvisation, vision-based vehicle lane detection, damaged vehicle parts recognition, partial image encryption of medical images, and image synthesis. The chapter authors show different approaches to computer vision, image processing, and frameworks for machine learning to build automated and stable applications. Deep learning is included for making immersive application-based systems, pattern recognition, and biometric systems. The book also considers efficiency and comparison at various levels of using algorithms for real-time applications, processes, and analysis.

Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern. • Provides step-by-step guidance on implementing deep learning techniques, specifically for video and image processing tasks in real-world scenarios • Emphasizes lightweight and efficient approaches to deep learning, ensuring that readers learn techniques that are suited to resource-constrained environments • Covers a wide range of real-world applications, such as object detection, image segmentation, video classification • Offers a comprehensive understanding of how deep learning can be leveraged across various domains • Encourages hands-on experience that can be applied to the concepts to existing projects.

Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking. Key Features Build a full-fledged image processing application using JuliaImages Perform basic to advanced image and video stream processing with Julia's APIs Understand and optimize various features of OpenCV with easy examples Book Description Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it’s easy to use and lets you write easy-to-compile and efficient machine code. . This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You’ll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you’ll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who this book is for Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.