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Computer Vision: Models, Learning, and Inference

Name: Computer Vision: Models, Learning, and Inference
Price: 62.50 USD
Availability: InStock
Rating: 4.7 (53 reviews)

4.7 of 5 Stars: Graphics & Design - Computer Vision: Models, Learning, and Inference

$62.50

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Book Synopsis: 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.

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Are you ready to unlock the immense potential of computer vision? Look no further than "Computer Vision: Models, Learning, and Inference"! This groundbreaking book takes a modern approach to computer vision, focusing on the power of learning and inference in probabilistic models. With its comprehensive coverage and easy-to-understand explanations, this book is perfect for advanced undergraduates, graduate students, and even practitioners of computer vision.

One of the highlights of this book is its emphasis on using training data to learn the intricate relationships between image data and the aspects of the world we want to estimate. By providing a step-by-step guide to model fitting and probability, the book equips you with the necessary tools to build powerful vision systems. You'll also learn cutting-edge techniques like graph cuts, machine learning, and multiple view geometry, ensuring you stay ahead of the curve in this rapidly evolving field.

What sets "Computer Vision: Models, Learning, and Inference" apart is its practical approach. With over 70 detailed algorithms and more than 350 full-color illustrations, you'll have everything you need to implement and modify vision systems. Whether you're interested in camera calibration, face recognition, or object tracking, this book provides a unified framework for solving these important computer vision problems.

Don't miss out on this self-contained, methodologically-rich book that will take your computer vision skills to the next level. Grab your copy today and join the ranks of successful computer vision practitioners!

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