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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. In particular, Bayesian methods have grown from a... Læs mere
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This comprehensive reference introduces the intricate science of implementing image modelling and retrieval based on the shape, on the colour and texture, or on spatial arrangement of imaged objects.
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Presenting techniques for describing image textures, this text deduces the Gibbs models from basic image features and tailors the modelling framework to the images.
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Describes a multiscale, or "pyramid", approach to vision, including its theoretical foundations, a set of pyramid-based modules for... Læs mere
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Bemærk: Kan ikke leveres før jul.
The new edition of this popular book introduces the study of attention, focusing on attention modeling, and addressing such themes as saliency models, signal detection, and different types of signals, including real-life applications.
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The rapid progress in the field of large multimodal foundation models, especially vision-language models, has dramatically transformed the landscape of machine learning, computer vision, and natural language processing.
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This book presents a comprehensive exploration of palmprint recognition, synthesizing over a decade of research in contact-based, contactless, 3D, and multispectral systems.
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Unlocking the future of image and video coding with AI-based Image and Video Coding: Methods, Standards, and Applications, we can explore the revolutionary impact of deep learning technologies on image and video compression.
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Describes several approaches to the analysis of three-dimensional dynamic scenes by the use of a stereovision system. The text should be of interest to scientists concerned with the analysis of moving images, particularly those dealing with robots and computer vision.
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Here is a comprehensive, systematic study of finite frame theory and applications. Coverage includes frame constructions, group frames, fusion frames, pseudo-frames, frames and algebraic geometry, and robustness against erasures.