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 Pei-Gee Ho - InTech
The objective of the image segmentation is to simplify the representation of pictures into meaningful information by partitioning into image regions. Image segmentation is a technique to locate certain objects or boundaries within an image.
by Richard Szeliski - Springer
The book emphasizes basic techniques that work under real-world conditions, not the esoteric mathematics without practical applicability. The text is suitable for a senior-level undergraduates in computer science and electrical engineering.
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We propose a new object detection/recognition method, which improves over the existing methods in every stage of the object detection/recognition process. In addition to the usual features, we propose to use geometric shapes as additional features.
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Introductory textbook and a research monograph on modelling the statistical structure of natural images. The statistical structure of natural images is described using a number of statistical models whose parameters are estimated from image samples.