经典计算机视觉:2011年算法与应用解析

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"Computer Vision: Algorithms and Applications 2011" 《Computer Vision: Algorithms and Applications》是2011年由Richard Szeliski博士编著的一本计算机视觉领域的经典著作。这本书深入探讨了计算机视觉的基本原理、算法和实际应用,对于学习和研究计算机视觉的读者来说具有极高的价值。它不同于网络上常见的简版PDF,提供了全面且详尽的内容。 计算机视觉是一门多学科交叉的领域,涉及图像处理、机器学习、数学、光学等多个方面。本书首先介绍了计算机视觉的基础概念,包括图像获取、几何模型、颜色和光照理论等。在这些基础之上,作者详细讲解了一系列关键算法,如特征检测(如角点检测、边缘检测)、图像匹配、立体视觉、运动估计、物体识别和跟踪等。 书中不仅涵盖了传统的计算机视觉技术,还讨论了深度学习和神经网络在视觉任务中的应用,尽管2011年时深度学习尚未像现在这样普及,但作者已经预见到其在该领域的潜力。此外,书中还包含了大量实例和实际应用,如遥感、医学成像、自动驾驶和娱乐产业中的视觉效果,帮助读者理解如何将理论知识应用于实际问题中。 本书的结构清晰,理论与实践相结合,适合计算机科学、电子工程、人工智能等相关专业的学生和研究人员阅读。同时,对于行业从业者来说,它也是一本重要的参考书,能够提供解决问题的思路和方法。每个章节都配有丰富的参考文献,方便读者进一步探索相关主题。 《Computer Vision: Algorithms and Applications》不仅是一本教程,也是一本研究指南,它激发了对计算机视觉的兴趣,推动了这个领域的发展。通过阅读此书,读者可以系统地学习计算机视觉的核心算法,提升分析和解决复杂视觉问题的能力。此外,书中强调了版权和许可信息,提醒读者在使用内容时要遵循相应的法律法规,尊重知识产权。 这是一本全面而深入的计算机视觉教材,无论你是初学者还是专业人士,都能从中受益匪浅,理解并掌握这一前沿技术的关键要素。
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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++)