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 David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
by S. Dance, Z.Q. Liu, T.M. Caelli - World Scientific
Explores a method for symbolically intrepreting images based upon a parallel implementation of a network-of-frames to describe intelligent processing. The system has been implemented in an object-oriented environment in the language Parlog++.
by Kresimir Delac, Mislav Grgic - InTech
This book will serve as a handbook for students, researchers and practitioners in the area of automatic (computer) face recognition and inspire some future research ideas by identifying potential research directions within the area.
by Julio Ponce, Adem Karahoca - IN-TECH
Nearest feature classification for face recognition, subspace methods, a multi-stage classifier for face recognition undertaken by coarse-to-fine strategy, PCA-ANN face recognition system based on photometric normalization techniques, etc.