Computer Vision: Models, Learning, and Inference
by Simon J.D. Prince
Publisher: Cambridge University Press 2012
Number of pages: 665
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.
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by Peng-Yeng Yin - IN-TECH
The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms.
by Xiong Zhihui - InTech
This book presents research trends on computer vision, especially on application of robotics, and on advanced approaches for computer vision. Research on RFID technology integrating stereo vision to localize an indoor mobile robot is included.
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The main ideas in the area of face recognition are security applications and human-computer interaction. The goal of this book is to provide the reader with the most up to date research performed in automatic face recognition.
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These are notes on the method of normalized graph cuts and its applications to graph clustering. I provide a thorough treatment of this deeply original method, including complete proofs. The main thrust of this paper is the method of normalized cuts.