Bøger i Computer Vision and Pattern Recognition serien

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. Presents the latest advances in spectral geometric processing for 3D shape analysis applications, such as shape classification, shape matching, medical imaging, etc.Provides intuitive links between fundamental geometric theories and real-world applications, thus bridging the gap between theory and practiceDescribes new theoretical breakthroughs in applying spectral methods for non-isometric motion analysisGives insights for developing spectral geometry-based approaches for 3D shape analysis and deep learning of shape geometry

Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts.Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information.Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation.This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection.

Tone and Gamut Mapping for High Dynamic Range and Colour Gamut Imaging: Vision Models, Techniques and Applications explains tone and color gamut mapping in HDR and WCG imaging within a framework of vision science, presenting the underlying principles and latest practical methods. In addition, it highlights how the use of vision models is a key element of all state-of- the-art methods for these emerging technologies. This book provides university researchers and graduate students in computer science, computer engineering, vision science, and R&D engineers insights into the science and methods of tone and color gamut mapping in HDR and WCG.Presents vision science principles and models that are essential to the emerging technologies of HDR and WCGProvides state-of-the-art techniques for tone and gamut mappingCovers current and proposed standards, open challenges and future directions of HDR and WCG research