# UMD CMSC 828 - Embedding Gestalt Laws in Markov Random Fields (34 pages)

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## Embedding Gestalt Laws in Markov Random Fields

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## Embedding Gestalt Laws in Markov Random Fields

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Pages:
34
School:
University of Maryland, College Park
Course:
Cmsc 828 - Advanced Topics in Information Processing
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Unformatted text preview:

Embedding Gestalt Laws in Markov Random Fields by Song Chun Zhu Purpose of the Paper Proposes functions to measure Gestalt features of shapes Adapts Zhu Wu Mumford FRAME method to shapes Exhibits effect of MRF model obtained by putting these together Recall Gestalt Features la Lowe and others Colinearity Cocircularity Proximity Parallelism Symmetry Continuity Closure Familiarity FRAME Zhu Wu Mumford F ilters R andom fields A nd M aximum E ntropy A general procedure for constructing MRF models Three Main Parts Data Learn MRF models from data Test generative power of learned model Elements of Data A set of images representative of the chosen application domain An adequate collection of feature measures or filters The marginal statistics of applying the feature measures or filters to the set of images Data Images Zhu considers 22 animal shapes and their horizontal flips The resulting histograms are symmetric More data can be obtained But are there other effects Sample Animate Images Contour based Feature Measures Goal is to be generic But generic shape features are hard to find 1 s the curvature s 0 implies the linelets on either side of s are colinear 2 s its derivative s 0 implies three sequential linelets are cocircular Other contour based shape filters can be defined in the same way Zhu s Symmetry Function s pairs linelets across medial axes Defined and computed by minimizing an energy functional constructed so that Paired linelets are as close parallel and symmetric as possible and There are as few discontinuities as possible Region based Feature Measures 3 s dist s s Measures proximity of paired linelets across a region 4 s 3 s the derivative 4 s 0 implies paired linelets are parallel 5 s 4 s 3 s 5 s 0 implies paired linelets are symmetric Another Possible Shape Feature 6 s 1 where s is discontinuous 0 otherwise Counts the number of parts a shape has Can Gestalt familiarity be statistically measured The Statistic The histogram of feature over curve is H z k z k s

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