Foundations and TrendsŪ in Computer Graphics and Vision
A STOCHASTIC GRAMMAR OF IMAGES
by Song-Chun Zhu (University of California, Los Angeles, USA) & David Mumford (Brown University, USA)
A Stochastic Grammar of Images is the first book to provide a foundational review and perspective of grammatical approaches to computer vision. In its quest for a stochastic and context sensitive grammar of images, it is intended to serve as a unified frame-work of representation, learning, and recognition for a large number of object categories.
It starts out by addressing the historic trends in the area and overviewing the main concepts: such as the and-or graph, the parse graph, the dictionary and goes on to learning issues, semantic gaps between symbols and pixels, dataset for learning and algorithms. The proposal grammar presented integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. At the end of the review, three case studies are presented to illustrate the proposed grammar.
A Stochastic Grammar of Images is an important contribution to the literature on structured statistical models in computer vision.
Published by Now Publishers and marketed by World Scientific
Contents:
- Introduction
- Background
- Visual Vocabulary
- Relations and
Configurations
- Parse Graph for Objects and Scenes
- Knowledge Representation with And-Or Graph
- Learning and Estimation with And-Or Graph
- Recursive Top-Down / Bottom-Up Algorithm for Image Parsing
- Three Case Studies of Image Grammar
- Summary and Discussion
- Acknowledgements
- References
Readership: Postgraduates, researchers and professionals.
| 116pp |
Pub. date: Aug 2007 |
|
