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Perceptual Organization & Scene A nalysis. NRS 495 – Neuroscience Seminar Christopher DiMattina , PhD. How do we parse the visual scene?. Novel combinations of objects. Perceptual organization and Gestalt grouping. The problem of grouping.

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perceptual organization scene a nalysis

Perceptual Organization & Scene Analysis

NRS 495 – Neuroscience Seminar

Christopher DiMattina, PhD

how do we parse the visual scene
How do we parse the visual scene?

NRS 495 - Grinnell College - Fall 2012

novel combinations of objects
Novel combinations of objects

NRS 495 - Grinnell College - Fall 2012

perceptual organization and gestalt grouping
Perceptual organization and Gestalt grouping

NRS 495 - Grinnell College - Fall 2012

the problem of grouping
The problem of grouping
  • The visual system goes from pixel intensities to objects and appropriate groupings of objects

NRS 495 - Grinnell College - Fall 2012

schools of early psychology
Schools of early psychology
  • Structuralists believed that perceptions were built up of atoms of sensation
  • Gestalt school argued that the perceptual whole is greater than the sum of its parts (gestalt = ‘form’)
  • Gestalt psychologists proposed rules for how the visual system groups features into perceptual wholes

PSY 295 - Grinnell College - Fall 2012

good continuation
Good continuation
  • Similarly oriented lines are seen as part of the same contour
  • Reflects the structure of the natural sensory environment

PSY 295 - Grinnell College - Fall 2012

similarity
Similarity
  • Different image regions have different statistical properties
  • Group together regions with similar properties

PSY 295 - Grinnell College - Fall 2012

proximity
Proximity
  • Nearby object tend to be grouped together
  • Note horizontal rather than vertical grouping

PSY 295 - Grinnell College - Fall 2012

gestalt grouping principles
Gestalt grouping principles

NRS 495 - Grinnell College - Fall 2012

degrees of grouping
Degrees of grouping

NRS 495 - Grinnell College - Fall 2012

tradeoff between color and proximity
Tradeoff between color and proximity

NRS 495 - Grinnell College - Fall 2012

synchrony
Synchrony

NRS 495 - Grinnell College - Fall 2012

common region
Common region

NRS 495 - Grinnell College - Fall 2012

connectedness
Connectedness

NRS 495 - Grinnell College - Fall 2012

over ruling proximity
Over-ruling proximity

PSY 295 - Grinnell College - Fall 2012

quantitative measurements of grouping
Quantitative measurements of grouping

NRS 495 - Grinnell College - Fall 2012

repetition discrimination task
Repetition discrimination task
  • Are repeated items squares or circles?

NRS 495 - Grinnell College - Fall 2012

effects of size in common region
Effects of size in common region

NRS 495 - Grinnell College - Fall 2012

organization in three dimensions
Organization in three dimensions

NRS 495 - Grinnell College - Fall 2012

grouping and lightness constancy
Grouping and lightness constancy

NRS 495 - Grinnell College - Fall 2012

grouping and visual completion
Grouping and visual completion

NRS 495 - Grinnell College - Fall 2012

uniform connectedness
Uniform connectedness

NRS 495 - Grinnell College - Fall 2012

effects of experience
Effects of experience

NRS 495 - Grinnell College - Fall 2012

effects of experience1
Effects of experience

NRS 495 - Grinnell College - Fall 2012

camouflage
Camouflage
  • The goal of camouflage is to prevent accurate feature grouping so that you cannot perceive animal

PSY 295 - Grinnell College - Fall 2012

web activity
Web Activity
  • http://sites.sinauer.com/wolfe3e/chap4/gestaltF.htm

NRS 495 - Grinnell College - Fall 2012

region and texture segmentation
Region and texture segmentation

NRS 495 - Grinnell College - Fall 2012

need to partition into regions
Need to partition into regions

NRS 495 - Grinnell College - Fall 2012

finding edges
Finding edges
  • One way to detect objects is to find their edges
  • However, not all edges correspond to object boundaries
  • Output of computer vision edge-detector

PSY 295 - Grinnell College - Fall 2012

edge detection does not always find region boundaries
Edge detection does not always find region boundaries

NRS 495 - Grinnell College - Fall 2012

region based approaches
Region-based approaches
  • Work in machine vision groups pixels by similarity in gestalt cues like luminance, color, texture, etc…
  • Segments image using graph-theoretic methods
  • Works better than edge detection methods

NRS 495 - Grinnell College - Fall 2012

parsing
Parsing
  • Sharp concave discontinuities provide an important cue for parsing objects into parts

NRS 495 - Grinnell College - Fall 2012

texture segregation
Texture segregation

NRS 495 - Grinnell College - Fall 2012

texture segregation1
Texture segregation

NRS 495 - Grinnell College - Fall 2012

physically different textures don t separate
Physically different textures don’t separate

NRS 495 - Grinnell College - Fall 2012

identical second and third order statistics but they separate
Identical second and third order statistics but they separate

NRS 495 - Grinnell College - Fall 2012

malik and perona model
Malik and Perona model

NRS 495 - Grinnell College - Fall 2012

filters in the model
Filters in the Model

NRS 495 - Grinnell College - Fall 2012

filter outputs
Filter outputs

NRS 495 - Grinnell College - Fall 2012

texture boundaries
Texture boundaries

NRS 495 - Grinnell College - Fall 2012

model and experiment
Model and experiment

NRS 495 - Grinnell College - Fall 2012

figure and g round organization
Figure and ground organization

NRS 495 - Grinnell College - Fall 2012

ambiguous figure ground organization
Ambiguous figure/ground organization

NRS 495 - Grinnell College - Fall 2012

faces or vase
Faces or vase?

NRS 495 - Grinnell College - Fall 2012

border ownership cells in v2
Border ownership cells in V2

PSY 295 - Grinnell College - Fall 2012

meaningfullness
Meaningfullness

NRS 495 - Grinnell College - Fall 2012

visual completion
Visual completion

NRS 495 - Grinnell College - Fall 2012

both familiar and unfamiliar completion
Both familiar and unfamiliar completion

NRS 495 - Grinnell College - Fall 2012

relatability
Relatability
  • Kellman and Shipley outlines rules for when two occluded segments are joined

NRS 495 - Grinnell College - Fall 2012

illusory contours
Illusory contours

NRS 495 - Grinnell College - Fall 2012

conditions for perceiving illusory contours
Conditions for perceiving illusory contours

NRS 495 - Grinnell College - Fall 2012

illusory contours1
Illusory contours

NRS 495 - Grinnell College - Fall 2012

v2 neurons sensitive to illusory contours
V2 neurons sensitive to illusory contours

NRS 495 - Grinnell College - Fall 2012

object recognition neural codes
Object recognition & neural codes

PSY 295 - Grinnell College - Fall 2012

template matching
Template matching

PSY 295 - Grinnell College - Fall 2012

impractical need a lot of templates
Impractical – need a lot of templates!

PSY 295 - Grinnell College - Fall 2012

alphabet of complex features
Alphabet of complex features

PSY 295 - Grinnell College - Fall 2012

structural description
Structural description
  • One way to get around problems with template matching is to use the fact that objects share a common structure
  • Match image to the structural description, i.e. specify in terms of parts and relationships.
  • Biederman’s “recognition-by-components” theory

PSY 295 - Grinnell College - Fall 2012

geons
Geons
  • Alphabet of geometric primitives from which objects build
  • Structural description theory suggests object recognition should be view-point invariant

PSY 295 - Grinnell College - Fall 2012

viewpoint dependence
Viewpoint dependence
  • Experiments show object recognition is viewpoint dependent
  • Faster for familiar viewpoints

PSY 295 - Grinnell College - Fall 2012

monkey experiments
Monkey experiments

PSY 295 - Grinnell College - Fall 2012

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