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 overview and standpoint of grammatical ways to computing device imaginative and prescient. In its quest for a stochastic and context delicate grammar of pictures, it truly is meant to function a unified frame-work of illustration, studying, and popularity for quite a few item different types. It starts off out by way of addressing the historical traits within the sector and overviewing the most strategies: equivalent to 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 notion grammar awarded integrates 3 favourite 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 established tools within the imaginative and prescient literature. on the finish of the overview, 3 case reports are awarded to demonstrate the proposed grammar. A Stochastic Grammar of pictures is a crucial contribution to the literature on dependent statistical versions in machine imaginative and prescient.

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Extra info for A Stochastic Grammar of Images

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This implies that the parse tree can terminate immediately at any node because no more detail is visible. 10 shows a human face in three levels from [85]. The left column shows face images at three resolutions, the middle column shows three configurations (graphs) of increasing detail, and the right column shows the dictionaries (terminals) used at each resolution, respectively. At a low resolution, a face is represented by patches as a whole (for example, by principle component analysis), at a middle resolution, it is represented by a number of parts, and at a higher resolution, the face is represented by a sketch graph using smaller image primitives.

The work in this paper belongs to this approach, cf. an attribute grammar to parse images of the man-made world [32], and a context sensitive grammar for representing and recognizing human clothes [9]. These will be reviewed in later sections. Finally, the sparse image coding model can be viewed as an attribute SCFG. In sparse coding [56, 69], an image is made of a number of n independent image bases, and there are a few types of image bases, such as Gabor cosine, Gabor sine, and Laplacian of Gaussian etc.

In the following, we show how these primitives can be used to represent images. 4. 3, the boundaries of the two rectangles are covered by 4 “T”-junctions, 8 “L”-junctions, and 20 step edges. We denote the domain covered by an image primitive Φsk i by Λsk,i , and the pixels covered by these primitives, which are called the “sketchable part” in [31], are denoted by nsk Λsk = Λsk,i . 2) i=1 The image I on Λsk is denoted by Isk and is modeled by the image primitives through their intensity profiles.

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