Computer Vision: Models, Learning, and Inference

Computer Vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to examine relationships between observed image data and the aspects of the world that we wish to estimate (such as 3D structure or object class). It also shows users 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.