This modern treatment of computer vision focuses on learning & inference in probabilistic models as a unifying theme It shows how to use training data to learn the relationships between the observed image data & the aspects of the world that we wish to estimate such as the 3D structure or the object class & 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 & model fitting & works up to real examples that the reader can implement & modify to build useful vision systems Primarily meant for advanced undergraduate & graduate students the detailed methodological presentation will also be useful for practitioners of computer vision Covers cutting-edge techniques including graph cuts machine learning & multiple view geometry A unified approach shows the common basis for solutions of important computer vision problems such as camera calibration face recognition & 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 wwwcomputervisionmodelscom