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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.
This book focuses on the fundamentals and recent advances in RGB-D imaging as well as covering a range of RGB-D applications. The addition of depth data to regular RGB images vastly increases the range of applications, and has resulted in a demand for robust and real-time processing of RGB-D data.
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition.
This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles.
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems.
These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.
This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections.
This revised and updated edition presents the computational and mathematical procedures underlying data collection, image reconstruction, and image display in computerized tomography. New topics include fast calculation of a ray sum in a digitized image and the task-oriented comparison of reconstruction algorithm performance.