Computer Vision 1st Edition by Simon Prince ISBN 1139506307 9781139506304

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Product details:

ISBN 10: 1139506307

ISBN 13: 978-1139506304

Author: Simon J. D. Prince

Computer Vision 1st Edition: 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

 

Computer Vision 1st Edition Table of contents:

Chapter 1: Introduction

• Overview of the book’s contents and organization.

• Reference to other related books.

Part I: Probability

• Chapter 2: Introduction to probability

  • Discusses random variables, joint probability, marginalization, conditional probability, Bayes’ rule, independence, and expectation.

• Chapter 3: Common probability distributions

  • Covers various distributions like Bernoulli, Beta, Categorical, Dirichlet, normal distributions (univariate, multivariate), and others.

• Chapter 4: Fitting probability models

  • Focuses on maximum likelihood, maximum a posteriori estimation, and the Bayesian approach with worked examples.

• Chapter 5: The normal distribution

  • Discusses covariance, linear transformations, marginal and conditional distributions, and more related to the normal distribution.

Part II: Machine learning for machine vision

• Chapter 6: Learning and inference in vision

  • Examines computer vision problems, types of models, and applications in regression and binary classification.

• Chapter 7: Modeling complex data densities

  • Discusses normal classification models, hidden variables, mixture models, factor analysis, and expectation maximization.

• Chapter 8: Regression models

  • Covers various regression techniques like linear, Bayesian, Gaussian process, and sparse regression, with real-world applications.

• Chapter 9: Classification models

  • Details logistic regression, kernel methods, random trees, forests, and incremental fitting techniques for classification tasks.

Part III: Connecting local models

• Chapter 10: Graphical models

  • Introduces conditional independence, graphical models (both directed and undirected), inference, and learning in these models.

• Chapter 11: Models for chains and trees

  • Explores MAP inference, marginal posterior inference, and learning methods in chain and tree models.

• Chapter 12: Models for grids

  • Discusses Markov random fields, conditional random fields, and higher-order models used in grid-based applications.

Part IV: Preprocessing

• Chapter 13: Image preprocessing and feature extraction

  • Focuses on pixel transformations, edge detection, interest points, and dimensionality reduction techniques for image data.

Part V: Models for geometry

• Chapter 14: The pinhole camera

  • Explores geometric problems, homogeneous coordinates, camera parameters, and 3D world point inference.

• Chapter 15: Models for transformations

  • Discusses 2D transformations, robust learning, and 3D transformations between images.

• Chapter 16: Multiple cameras

  • Covers two-view geometry, essential and fundamental matrices, and multiview reconstruction.

Part VI: Models for vision

• Chapter 17: Models for shape

  • Introduces shape representation, snakes, statistical shape models, and articulated models.

• Chapter 18: Models for style and identity

  • Discusses identity models, probabilistic discriminant analysis, and bilinear models for style and identity in vision tasks.

• Chapter 19: Temporal models

  • Focuses on temporal estimation techniques like Kalman filters, extended Kalman filters, unscented Kalman filters, and particle filtering.

• Chapter 20: Models for visual words

  • Examines images as collections of visual words, using methods like bag-of-words, latent Dirichlet allocation, and scene models.

Part VII: Appendices

• Likely includes additional resources, proofs, or advanced topics relevant to the material covered in the chapters.

 

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