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Perceptual Organization

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... (at least conceptually) is the art of putting partitions together in a ... Note that a static color image (3-vector at each pixel) is still 2D for this list. ... – PowerPoint PPT presentation

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Transcript and Presenter's Notes Title: Perceptual Organization 1Perceptual OrganizationThere is no real distinction among segmentation, grouping, and perceptual organization. Indeed, I consider all of it to be perceptual organization. But (in my mind) segmentation is a subset of perceptual organization in which a partitioning of the image is sought, while the rest of perceptual organization (at least conceptually) is the art of putting partitions together in a meaningful way. Edge detection is, therefore, a segmentation operation. 2About Perceptual Organization

• No broad, overarching theory exists
• Application-dependent methods
• Data reduction (turn data into information)
• Effective use of domain knowledge
• Vast, understudied area of intermediate level vision
• Grand Challenge Problem
• Imparts
• Robustness
• Efficiency
• Qualitative and holistic nature to the process of vision

3Why Perceptual Organization?

• It is the ability to impose organization on sensory information that makes human perception so powerful and versatile.
• Most vision systems organize primary data (edges, regions tokens) into perceptually significant groups and structures
• Before a collection of image features can be recognized, they must be organized into plausible physical entities.

4Why is Vision Hard? (revisited)Understanding images is difficult, but

• Precision is not the issue. Ultimately important to mensuration, but not understanding
• Noise isnt the central issue, either
• The lack of a mathematical model of perception is the issue.
• Such a model will be built (eventually) around the concept of organization.

5Why do these tokens belong together? What scene interpretation do they suggest? 6The Basic Premise

• Regularities are highly unlikely to arise by accident. The more regular an organization (collection of tokens), the more likely it is to have a common underlying physical cause in the scene.

7Figure-Ground Separation? Thinking of segmentation or perceptual organization in this way leads to ambiguities, even for a cartoon image. Is the circle part of the foreground, or the background? 8Muller-Lyer Illusion The perceived length of the horizontal depends on its visual context. Our assessment is a function of the organized whole, not just the properties of the individual line segment we do not evaluate tokens in isolation. 9Gestalt Psychology (for the impatient)

• Gestalt psychologists (early 20th century) observed the importance of organization in vision and other reasoning tasks
• The properties of the whole are not the summation of those of its parts (illusions)
• Gestalt Organized structure, a whole that is orderly, rule-governed, and not random.
• Pragnanz The tendency of a process to a regular, ordered, stable, balanced state. Has proven elusive to capture mathematically.

10Herr Gestalt, meet Mr. Bayes!Let C be causality, the event that a set of image features are part of the same object. Prior PC is high for real scenes. Let O be the event that some organization exists among the features. Prior PO decreases for more complex groupings. Conditional POC should be high because matter is coherent and behaves according to physics Then we want Can infer causality from organizations having low accidental probability (low priors) but high probability of being caused by matter (posterior PC O). 11Standard Gestalt Cues 12Gestalt Cues for Contours 13Occlusion as a Grouping CueHow to begin? 14Occlusion as a Grouping CueEasier to explain groupings in terms of simple figures 15Elevator ButtonsSee text. I encountered the same problem in a hotel in Toronto. And on a web sites radio buttons. 16Illusory ContoursIts far easier to explain (or accept) these figures when hypothesizing an occluder. Its just simpler that way more likely to occur in our experience. 17Necker CubeA simple 3D figure, but a complex arrangement of lines in 2D. The 3D interpretation prevails in perceptual organization. 18(No Transcript) 19Goal and Leverage

• Goal Detect regularities (as given earlier) these groupings will lead to object hypotheses that can be input to a model based recognition system.
• Leverage Can bridge gaps from noise, infer missing parts, and otherwise bring robustness to the system.

20VocabularyTokens Whatever were grouping Edge points, Pixels, Curves, Textels Preattentive Bottom-up search for Gestaltic cues hypothesis generation Voting and graph theoretical methods common Local spatial coherence General rules that are widely applicable Attentive Top-down, test hypotheses created by preattentive process Bayesian networks and other reasoning methods Put tokens together because they fit a model Specific rules that are (somewhat) domain-specific 21PO Classificatory Structure

• Two axes
• Level of organization
• Dimensionality of the signal space
• Levels (example from to)
• Signal level gray levels edge chains
• Primitive level edge chains concurves
• Structural level concurves ribbons
• Assembly level ribbons Euclidean structures
• Assembly level repeats indefinitely

22Signal Space Dimensionality

• 2D typical static images
• 3D range images, magnetic resonance, etc.
• 2Dt image sequences, video
• 3Dt dynamic range image (stereo video)
• Note that a static color image (3-vector at each pixel) is still 2D for this list.

23Example Organizational HierarchyParallel RibbonsStrands of IntersectionsRibbon Strands Strands or CyclesIntersectionsContours Write a Comment User Comments (0) Cancel OK OK Latest Latest Highest Rated Sort by: Latest Highest Rated Page of About PowerShow.com

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