pattern and object recognition · 2012. 2. 15. · chapter 3 perception: pattern or object...
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Chapter 3
Perception:
Pattern or object recognition
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Perception
Sensation vs. perception
What are the mechanisms responsible?
What is the process?
Q: How do we interpret lines and patterns as objects?
Q: How do we program a computer to perceive objects and scenes?
Start simple: How do we recognize these letters as A’s?
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Template approach Stimulus is compared to stored pattern
Examples? Bar code, bank check, scantron, etc.
Problems:
There are an infinite number of templates to remember
Have to learn a template first
Any change in stimuli will not be recognized
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Receptors in retina -> optic nerve -> occipital lobe (visual cortex)
Specialized receptors in visual cortex
Simple cells: feature detectors e.g. Orientation specific
Complex cells Combination of 2 simple features
Perception due to pattern of neural firing (neural code)
Bottom-up processing
Stimulus
Cell’s
responses
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McClelland & Rummelhart (1981)
Interactive Activation Model
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Pandemonium (Selfridge, 1959)
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Visual perception by neurons
Respond to things that occur most often in environment
e.g orientation: horizontal and vertical lines vs. oblique
Experience-dependent plasticity
Animal reared in certain environment – brain changes to more strongly respond to those cues (Blakemore & Cooper, 1970)
Gauthier et al. (1999): “Greebles” study
Measure FFA (fusiform area)
IV: experience with Greebles
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Recognition by components
Biederman’s RBC (recognition by component) theory
36 geons (3D)
Basic building blocks
Emphasis on
intersections
Recognition with missing information possible
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Geons:
Identify objects
Principle of
componential recovery
Resistance to visual
“noise”
‘View invariant’
properties
Discriminability
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Biederman’s Geons
Intersections are important to recognition
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Beyond bottom-up processing
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Pattern or object recognition
Bottom-up processing
Information from sensory receptors
Processing driven by stimulus
Data-driven
Top-down processing
Information from knowledge and expectations
Processing driven by higher level knowledge
Conceptually-driven
Problems with pure bottom-up theories:
How does brain pull all the feature information together?
How do theories deal with complex objects?
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To xllxstxatx, I cxn rxplxce xvexy txirx lextex of x
sextexce xitx an x, anx yox stxll xan xanxge xo rxad xt –
ix wixh sxme xifxicxltx
Context and knowledge fills in the rest!
The redundancy of stimuli provide more features than required
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Oliva & Torralba (2007)
Q: Does perception depend on more
than just stimulation of receptors?
Method:
Use same “blob” in multiple contexts
Result:
Perceived as different objects due to top-down processing
Conclusion:
Signal from object
Signal from context
Feedback signal: influence of knowledge
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Theory of perception
Bottom-up AND top-down
Bi-directional or connectionist model
Depth perception
Relative size
Size constancy
Odor intensity
Controlled sniff intensity
Perception of language
Speech segmentation
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Treisman & Schmidt (1982)
Q: Does knowledge change perception?
Method
Flash display of #s & objects 200 ms
Ask Ss to report #s then objects
IV: Give description of objects (“carrot, lake, tire”) or not
Results
Info significantly improved accuracy
Conclusion
Top-down knowledge changes perception
Able to “bind” features (group information) together more rapidly
Orange & triangle = carrot
1 3
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Hollingworth (2005)
Question
How does knowledge of what objects
belong in a scene influence perception?
Semantic regularities (knowledge of function
of objects)
Method
Study scene 20s
IV: w/ or w/o target object
Test: Place target object in scene
By memory or expectation
Result
Accurate position in both conditions
Prediction based on experience
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Palmer (1975)
Method Present scene
Ss ID flashed pics (a) or (b) or (c)
IV: type of picture
DV: accuracy
Results Appropriate pictures: 83%
Inappropriate pictures: 50%
Misleading pictures 40%
Conclusion Bottom-up perception interacts with prior knowledge (top-down) to
influence response