计算机视觉书籍资源推荐_Computer Vision Principles, Algorithms, Applications, Learning

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一、书籍简介

英国伦敦大学Roy Davies教授关于计算机视觉的经典书《Computer Vision Principles, Algorithms, Applications, Learning》 已经发行至第五版,本书畅销近30年,是全面了解计算机视觉领域的一本宝典。
作者介绍
Roy Davies是英国伦敦大学皇家霍洛威学院机器视觉荣誉教授。他致力于视觉的许多方面,从特征检测和噪声抑制到鲁棒模式匹配和实际视觉任务的实时实现。他的兴趣包括自动视觉检查、监视、车辆引导和犯罪检测。他发表了200多篇论文和三本书“机器视觉:理论、算法、实用性”(1990)、“电子学、噪声和信号恢复”(1993)和“食品工业图像处理”(2000);其中第一本已在国际上广泛使用超过25年,现在出版的第五版是优化加强版。Roy是IOP和IET的fellow,也是IEEE的高级成员。他拥有伦敦大学的DSC学位,2005年被授予bmva杰出研究员,2008年被授予国际模式识别协会Fellow。

二、主要内容

《计算机视觉:原理、算法、应用、学习》(以前称为计算机和机器视觉)清晰而系统地介绍了计算机视觉的基本方法,涵盖了理论的基本要素,同时强调算法和实际设计的约束。新的第五版引入了更多的计算机视觉的概念和应用,使它成为一本非常适合本科生和研究生,研究人员和研发工程师的书籍。

主要内容目录

  1. 视觉挑战

  2. 影像及影像处理

  3. 图像滤波与形态学

  4. 阈值化的作用

  5. 边缘检测

  6. 角点、兴趣点和不变特征检测

  7. 纹理分析

Computer Vision: Principles, Algorithms, Applications, Learning By 作者: E. R. Davies ISBN-10 书号: 012809284X ISBN-13 书号: 9780128092842 Edition 版本: 5 出版日期: 2017-11-29 pages 页数: (900 ) Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications―including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians―give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples―code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)
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