A Stochastic Grammar of Images by Song-Chun Zhu, David Mumford

By Song-Chun Zhu, David Mumford

A Stochastic Grammar of pictures is the 1st publication to supply a foundational assessment and point of view of grammatical methods to machine imaginative and prescient. In its quest for a stochastic and context delicate grammar of pictures, it's meant to function a unified frame-work of illustration, studying, and popularity for a number of item different types. It starts off out by means of addressing the ancient tendencies within the sector and overviewing the most options: resembling the and-or graph, the parse graph, the dictionary and is going directly to studying concerns, semantic gaps among symbols and pixels, dataset for studying and algorithms. The idea grammar awarded integrates 3 fashionable representations within the literature: stochastic grammars for composition, Markov (or graphical) versions for contexts, and sparse coding with primitives (wavelets). It additionally combines the structure-based and visual appeal dependent tools within the imaginative and prescient literature. on the finish of the evaluation, 3 case reviews are offered to demonstrate the proposed grammar. A Stochastic Grammar of pictures is a crucial contribution to the literature on dependent statistical types in machine imaginative and prescient.

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Extra resources for A Stochastic Grammar of Images

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Images not only have very regular and highly structured objects which could be composed by production rules, they also contain very stochastic patterns, such as clutter and texture which are better represented by Markov random field models. In fact, the spectrum is continuous. The structured and textured patterns can transfer from one to the other through continuous scaling [80, 84]. The two categories of models ought to be integrated more intimately and melded into a common model. This raises numerous challenges in modeling and learning at all levels of vision.

Images not only have very regular and highly structured objects which could be composed by production rules, they also contain very stochastic patterns, such as clutter and texture which are better represented by Markov random field models. In fact, the spectrum is continuous. The structured and textured patterns can transfer from one to the other through continuous scaling [80, 84]. The two categories of models ought to be integrated more intimately and melded into a common model. This raises numerous challenges in modeling and learning at all levels of vision.

When image primitives are connected into larger parts, some spatial and functional relations must be found. Besides its open bonds to connect with others, usually its immediate neighbors, a part may be bound with other parts in various ways. The gestalt groupings discussed in the previous section are the best examples: parts can be linked over possibly large distances by being collinear, parallel, or symmetric. To identify this groupings, connections must be created flagging this non-accidental relationship.

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