Perceptual organization scene a nalysis
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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

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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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