A Stochastic Grammar of Images
A stochastic Grammar of Image 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, if is intended to serve as a unified frame work of representation leaming and recognition for a large number of object categories.
It starts out by addressing he historic trends in the area and overviewing the main concepts such as the and or graph the parse graphs the dictionary and goes on to learning issues, semantic gaps between symbols and pixels dataset for for learning and algorithms. The proposal grammar presented integrates three prominent representations in the literature stochastic grammar for composition. Markev (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.
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