Perception:

Pattern and object recognition

Chapter 3

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?

Object recognition

How do we recognize all of these as A‟s

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

Specialized receptors in visual cortex

Simple cells

e.g. Orientation specific

Complex cells

Combination of 2 simple features

Feature detectors

Stimulus

Cell’s

responses

McClelland & Rummelhart (1981)

Interactive Activation Model

Pandemonium (Selfridge, 1959)

Recognition by components

Biederman‟s RBC (recognition by component) theory

36 geons (3D)

basic building blocks

Emphasis on

intersections

Recognition with missing information possible

Geons:

Identify objects

Resistance to

visual noise

View invariant

properties

Discriminability

Biederman‟s Geons

Intersections are important to recognition

Beyond bottom-up processing

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

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!

Beyond bottom-up processing

Depth perception

Depth cues: Relative size

Size constancy

Odor intensity

Controlled for sniff intensity

Perception of language

Speech segmentation

Connectionist models

Bottom-up AND top-down

Bi-directional or connectionist model

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

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?

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

Perceptual problem solving: Illusions

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

Gestalt law/heuristic examples

Which gestalt law/heuristic?

Which gestalt law/heuristic?

Dalmation

http://michaelbach.de/ot/cog_dalmatian/index.html

“Biological motion”

http://michaelbach.de/ot/mot_biomot/index.html

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.

Apparent motion/motion illusions

Pikler-Ternus display:

http://michaelbach.de/ot/mot_Ternus/index.html

“Rotating snake”

http://michaelbach.de/ot/mot_rotsnake/index.html

“Freezing rotation”

http://michaelbach.de/ot/mot_freezeRot/index.html

“Stepping feet”

http://michaelbach.de/ot/mot_feet_lin/index.html

Apparent motion factors

Color, shape, perceived depth, context

Optical illusions and visual

phenomenon

http://michaelbach.de/ot/index.html

Motion aftereffect: http://michaelbach.de/ot/mot_adapt/index.html

Lilac chaser: http://michaelbach.de/ot/col_lilacChaser/index.html

Optical illusions and visual phenomenon

Watercolor illusion: bright inside color spreads into enclosed area

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!

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

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‟?