Computer Vision: Models, Learning, and Inference (Hardcover)

Dr Simon J. D. Prince

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商品描述

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. - Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry. - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking. - More than 70 algorithms are described in sufficient detail to implement. - More than 350 full-color illustrations amplify the text. - The treatment is self-contained, including all of the background mathematics. - Additional resources at www.computervisionmodels.com.

商品描述(中文翻譯)

這本現代的計算機視覺書籍以學習和推論概率模型作為統一主題。它展示了如何使用訓練數據來學習觀察到的圖像數據與我們希望估計的世界方面之間的關係,例如3D結構或物體類別,以及如何利用這些關係從新的圖像數據中進行新的推論。本書從概率和模型擬合的基礎知識開始,並逐步介紹讀者可以實施和修改以建立有用視覺系統的真實示例。主要面向高年級本科生和研究生,詳細的方法論介紹也對計算機視覺從業人員有用。- 包括最新技術,包括圖割、機器學習和多視圖幾何。- 一種統一的方法顯示了解決重要計算機視覺問題的共同基礎,例如相機校準、人臉識別和物體跟踪。- 描述了超過70種算法,足夠實施。- 超過350張全彩插圖增強了文本。- 本書是自包含的,包括所有背景數學知識。- 更多資源請參考www.computervisionmodels.com。