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1 / 23 Actions Remove this presentation Flag as Inappropriate I Don't Like This I like this Remember as a Favorite Share About This Presentation 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
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