Computer Vision: Models, Learning, and Inference - pdf - 电子书免费下载

内容提要:

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

ISBN:1107011795

年份：2012

总页数：598

语言：English

文件大小：26.88 MB

文件格式：PDF

下载:关注本站公众号（右侧二维码），在 个性化-联系客服 中输入本文链接可获取文件下载链接。