This book highlights recent advances and emerging technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. It covers all branches of artificial intelligence and machine learning that are based on computation at some level, e.g. artificial neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Exploring recent trends in research and applications, the book offers a valuable resource for professors, researchers, and engineers alike.
Proceedings of the 2019 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada.
Image and signal processing techniques are receiving increasing interest because of their numerous real-world applications. Data is now available in different forms, different wavelengths, and even in different dimensions, creating the need for novel multidisciplinary solutions for automated data processing and analysis. Applied Signal and Image Processing: Multidisciplinary Advancements highlights the growing multidisciplinary nature of signal and image processing by focusing on emerging applications and recent advances in well-established fields. This book covers state-or-the-art applications in both signal and image processing, which include optical communication and sensing, wireless communication management, face recognition and facial imaging, solar imaging and feature detection, fractal analysis, and video processing.
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. - Provides a step-by-step approach to develop deep learning models - Presents case studies showing end-to-end implementation (source codes: available upon request)
Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
The focus of this book is on providing a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics. Throughout, the authors concentrate on describing and analysing the underlying concepts rather than on presenting algorithms or pseudocode. As befits a modern introduction to this topic, a healthy balance is struck between discussing the underlying mathematics of the subject and the main topics covered: signal processing, data discretization, the theory of colour and different colour systems, operations in images, dithering and half-toning, warping and morphing, and image processing.
This book contains interesting findings of some state-of-the-art research in the field of signal and image processing. It contains twenty one chapters covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. Various types of signals including image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals, MRI, PET and CT scan images, THz signals, solar wind speed signals (SWS) and photoplethysmogram (PPG) signals have been dealt with. It demonstrates how new paradigms of intelligent computing like quantum computing can be applied to process and analyze signals in a most precise and effective manner. Processing of high precision signals for real time target recognition by radar and processing of brain images, ECG and EMG signals that feature in this book have significant implications in defense mechanism and medical diagnosis. There are also applications of hybrid methods, algorithms and image filters which are proving to be better than the individual techniques or algorithms. Thus the present volume, enriched in depth and variety of techniques and algorithms concerning processing of various types of signals, is likely to be used as a compact yet handy reference for the young researchers, academicians and scientists working in the domain of signal and image processing and also to the post graduate students of computer science and information technology.
This book highlights recent advances and emerging technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. It covers all branches of artificial intelligence and machine learning that are based on computation at some level, e.g. artificial neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Exploring recent trends in research and applications, the book offers a valuable resource for professors, researchers, and engineers alike.
Digital image processing, originally established to analyze and improve lunar images, is rapidly growing into a wealth of new appli cations, due to the enormous technical progress made in computer engineering. At present, the most important fields of growth appear to emerge in the areas of medical image processing (i. e. tomography, thermography), earth resource inventory (i. e. land usage, minerals), office automation (i. e. document storage, retrieval and reproduction) and industrial production (i. e. computer vision for mechanical ro bots). Currently, emphasis is being shifted from signal-processing re search and design-innovation activities towards cost-efficient system implementations for interactive digital image processing. For the years ahead, trends in computer engineering indicate still further advances in Large Scale Integration (LSI) and Input/Output (I/O) technologies allowing the implementation of powerful parallel and/or distributed processor architectures for real-time processing of high resolution achromatic and color images. In view of the many new developments in the field of digital image processing and recognizing the importance of discussing these developments amongst key scientists that might make use of them, ffiM Germany sponsored an international symposium on 'Advances in Digital Image Processing', held at Bad Neuenahr, Federal Republic of Germany, September 26 - 28, 1978. The interest shown in this symposium encouraged the publi cation of the papers presented in this volume of the ffiM Research Symposium Series.
