Perception: Pattern and object recognition. Chapter 3
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1 Perception: Pattern and object recognition Chapter 3
2 Pattern recognition theories How do we interpret lines and patterns as objects? Why is object perception so difficult for computers? Start simple: How do we recognize letters or other simple objects?
3 Object recognition How do we recognize all of these as A s
4 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
5 Feature detectors Cell s responses Stimulus Specialized receptors in visual cortex Simple cells e.g. Orientation specific Complex cells Combination of 2 simple features
6 McClelland & Rummelhart (1981) Interactive Activation Model
7 Pandemonium (Selfridge, 1959)
8 Recognition by components Biederman s RBC (recognition by component) theory 36 geons (3D) basic building blocks Emphasis on intersections Recognition with missing information possible
9 Geons: Identify objects Resistance to visual noise View invariant properties Discriminability
10 Biederman s Geons Intersections are important to recognition
11 Beyond bottom-up processing
12 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 Examples
13 Tox-Doxn Pxocxssxng 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 The redundancy of stimuli provide more features than required Context and knowledge fills in the rest!
14 Beyond bottom-up processing Depth perception Depth cues: Relative size Size constancy Odor intensity Controlled for sniff intensity Perception of language Speech segmentation
15 Connectionist models Bottom-up AND top-down Bi-directional or connectionist model
16 Word recognition Flash stimulus Word condition: FORK Letter condition: K Nonword condition: RFOK Choose letter that was presented K or M Result: Faster and more accurate when letter part of original stimulus (word condition) Word superiority effect
17 Treisman & Schmidt (1982) Does prior knowledge change perception? Method Give Ss description of objects ( carrot, lake, tire ) Flash display of #s/objects 200 ms; mask Ask to report #s then objects Results Info significantly improves accuracy Conclusion Top-down knowledge changes perception Able to bind features together more rapidly?
18 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
19 Perceptual problem solving: Illusions
20 Gestalt principles of organization Integrate info into meaningful whole Heuristics: best-guess predictions Laws of perceptual organization Pragnanz: Good figure or simplicity Similarity Good continuation Proximity Common fate Familiarity Other heuristics Occlusion heuristic Light-from-above heuristic
21 Gestalt law/heuristic examples
22 Which gestalt law/heuristic?
23 Which gestalt law/heuristic? Dalmation Biological motion
24 CogLab: Apparent motion Data from Spring 09 (N = 8) Expected result: For larger separations, the stimulus must "move" a farther distance, which presumably requires a greater length of time.
25 Apparent motion/motion illusions Pikler-Ternus display: Rotating snake Freezing rotation Stepping feet Apparent motion factors Color, shape, perceived depth, context
26 Optical illusions and visual phenomenon Motion aftereffect: Lilac chaser:
27 Optical illusions and visual phenomenon Watercolor illusion: bright inside color spreads into enclosed area
28 Problems for computers Stimulus on receptors is ambiguous Inverse projection problem Segmentation Visual separation/overlap Speech segmentation Visual or verbal noise Occlusions or obscured Blurred or degraded Changes in shadowing (lightness/darkness) Human perception is different due to bottom-up AND top-down processing!
29 Chapter 3: Perception Research questions What are the processes responsible for perception? How do we recognize objects or words? Why is perception difficult for computers? Methods Name objects in pictures or read words (or letters) With or without noise With or without prior information (context) Indicate what you see with an illusion figure Results Requires combination of bottom-up and top-down processing Use (gestalt) rules of perceptual organization Experience-dependent plasticity: depends on experiences Future directions
30 Quiz: Chapter 3 Describe how the following example shows that perception involves taking into account information in addition to what is on the receptors: 1) perceiving size (like railroad track or quarter examples). Describe the gestalt laws of organization. Why are they called heuristics?
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