Chapter 4a

Question 1

Receptive Fields Summary

Answer 1

Rods and Cones => Small Spots

Retinal Ganglion Cells, LGN => Larger Spots

Primary Visual Cortex => Bars/Edges etc…

Beyond V1 = ????

Question 2

Receptive Fields:

Rods & Cones

Answer 2

Small Spots

Question 3

Receptive Fields:

Retinal Ganglion Cells and LGN

Answer 3

Larger Spots

Question 4

Receptive Fields:

Primary Visual Cortex

Answer 4

Bars/Edges etc…

Question 5

WHAT do you see in this image?

Answer 5

At the level of the retina, you “see” an array of point-lights bouncing off the page and exciting your rods and cones.

In early visual brain areas, you “see” a collection of oriented lines and a collage of red, green, yellow, and blue color patches.

But your response to this question was almost certainly not “light” or “lines” or “colors”; what we all perceive in this scene are “toys.”

The ability to organize visual sensations into coherent objects and then assign meaningful category labels to these objects is in many ways the ultimate accomplishment of vision.

Question 6

Ambiguous figure

Answer 6

A visual stimulus that gives rise to two or more interpretations of its identity or structure.

In a way, all images are inherently ambiguous. Any 2D image can have an infinite number of 3D interpretations.

An “ambiguous figure” is usually obviously ambiguous.

*Black and white spotted ambiguous picture of dalmatian.

Question 7

Adaptation that Is Specific to Spatial Frequency

Answer 7

This previously shown adaptation demo fatigued small groups of neurons thus revealing their specific responsibilities (i.e. orientation & spatial frequency

Question 8

Can we adapt to more complex stimuli?

Answer 8

Dalmatian Adaptation?

No.

“THIS IS A JOKE. YOU CAN NOT HAVE ADAPTATION TO DALMATIONS!”

Question 9

Middle vision

Answer 9

A loosely defined stage of visual processing that comes AFTER basic features have been extracted from the image and BEFORE object recognition and scene understanding.

• Involves the perception of edges and surfaces.
• Determines which regions of an image should be grouped together into objects.

Question 10

High level vision

Answer 10

Loosely defined stage that involves complex image analysis including 3D vision, object recognition, scene understanding and more.

Question 11

Extrastriate cortex

Answer 11

Region of cortex bordering the primary visual cortex (V1) and containing multiple areas involved in visual processing

-V2, V3, V4, etc.

After extrastriate cortex, processing of object information is split into “WHAT” and “WHERE” pathways of High Level Vision.

Question 12

“Where” pathway

Answer 12

concerned with locations of objects, but not their names or functions

Dorsal pathway = “Where/how” system
Object Localization/Manipulation
Parietal Cortex

Question 13

“What” pathway

Answer 13

concerned with names & functions of objects, regardless of location

Ventral pathway
Object Identification
Inferior Temporal (IT) Cortex

Question 14

Middle Vision Receptive Fields

Answer 14

The same visual input occurs in both (a) & (b) and a V1 neuron would respond equally to both.

A V2 neuron might respond more to (a) than (b) because the black edge is owned by the square in (a), but not in (b)

Question 15

Extrastriate Receptive Fields

Answer 15

The receptive fields for cells in extrastriate areas are more sophisticated than those in striate cortex.

They respond to visual properties important for perceiving objects.

For instance, “boundary ownership.”
For a given boundary, which side is part of the object, and which side is part of the background?

Question 16

Lower Vision

Answer 16

The functionally defined primary visual cortex (V1) is approximately equivalent to the anatomically defined striate cortex.

Question 17

Structuralism

Answer 17

A school of thought believing that complex objects or perceptions could be understood by the analysis of the components.

Developed by Wilhelm Wundt and his mentee Edward Titchener.

Question 18

Structuralism:

Lines

Answer 18

Edges are important, they give us: LINES,

How do we know which lines go together? Which belong to the same object and which do not?

We see edges that computer algorithms can’t.

Picture of gradient colored arrow.
Picture of image filtered with Photoshop’s “Find Edges” filter.

But you SEE the edges as continuous…. Ha!!

A better illustration of “dumb” edge detecting algorithms…is next.

Question 19

Kanisza triangle

Answer 19

These are “imaginary” edges.
Nothing is there…. But…

1. Sometimes people actually report seeing different shades of white inside and outside of the house.
2. Brain responds differently to both types of white surface!
3. This image illustrates the point that the visual system knows about physics!! Objects tend to be opaque and obstruct the view of other objects behind them.
4. Monkeys also see this as a house…

Question 20

Finding Edges

Answer 20

We see objects that computer algorithms can’t.

Question 21

Gestalt Psychology

Answer 21

“The whole is greater than the sum of its parts”

Gestalt: In German, “form” or “whole”

Wertheimer, Köhler, Koffka (1920s–1950s)

Gestalt psychology opposed to other schools of thought, such as structuralism.

Gestalt grouping rules:
A set of rules that describe when elements in an image will appear to group together.
Patterns are spontaneously organized by the brain into the simplest possible configurations.

Question 22

Gestalt grouping rules:

Answer 22

A set of rules that describe when elements in an image will appear to group together.

Patterns are spontaneously organized by the brain into the simplest possible configurations.

Question 23

Good Continuation

Answer 23

A Gestalt grouping rule stating that two elements will tend to group together if they lie on the same contour.

Some contours in an image will group because of good continuation.
Can you find the shape embedded within the field of lines at right?

Question 24

When should we complete edges behind occluders?

Answer 24

When the edges are relatable by an “elbow curve”

S curves = NOT LIKELY THE SAME EDGE
Elbow curve = likely the same edge.

Also, the steeper the curve, the less likely it is the same edge. (PARAMETRIC RULE)

HEURISTIC = mental shortcut (not always right!).

Question 25

Texture segmentation

Answer 25

Carving an image into regions of common texture properties

Question 26

Similarity & Proximity

Answer 26

Gestalt grouping rule

*Absolut Boston beer advertising

Question 27

Parallelism

Answer 27

Gestalt grouping rule

Parallel contours are likely to belong to the same figure

Question 28

Symmetry

Answer 28

Gestalt grouping rule

Symmetrical regions are more likely to be seen as figure

Symmetry & Parallelism help group objects.
More symmetry/parallelism is generally considered “better” (but not always).

Question 29

Common region

Answer 29

Gestalt grouping rule

Two features will group if they appear to be part of the same larger region

Question 30

Connectedness

Answer 30

Gestalt grouping rule

Two items will tend to group if they are connected

Question 31

Dynamic grouping principles

Answer 31

Common fate: Elements that move in the same direction tend to group together

Synchrony: Elements that change at the same time tend to group together

Question 32

Common fate

Answer 32

group elements moving in the same direction together.

They are going to get to the same place at the same time.

Question 33

Synchrony

Answer 33

group elements changing at the same time together

flashing “open” sign, the lights flash at the same time

Question 34

Camouflage

Answer 34

An organism’s attempt at breaking Gestalt rules so that its features are not perceived as an object on their own, but as parts of a larger object.

Question 35

Figure–ground assignment

Answer 35

The process of determining that some regions of an image belong to a foreground object (figure) and other regions are part of the background (ground)

Gestalt figure–ground assignment principles: surroundedness, size, symmetry, parallelism

Question 36

Gestalt figure–ground assignment principles:

Answer 36

1. Surroundedness: The surrounding region is likely to be ground.
2. Size: The smaller region is likely to be figure.
3. Symmetry: A symmetrical region tends to be seen as figure.
4. Parallelism: Regions with parallel contours tend to be seen as figure.

Ambiguous figures are created when these principles compete

Question 37

Surroundedness

Answer 37

The surrounding region is likely to be ground

Question 38

Size

Answer 38

The smaller region is likely to be figure

Question 39

Symmetry

Answer 39

A symmetrical region tends to be seen as figure

Question 40

Parallelism

Answer 40

Regions with parallel contours tend to be seen as figure

Question 41

The Rubin Vase

Answer 41

Edgar Rubin: Danish Psychologist.

Isn’t that powerful (that OR starts before Figure-Ground segregation)??

Flips in ambiguous images: we think perception is as stable as the world! But that is not always the case.

Other Example: M. C. Escher, Encounter and Sky & Water I

Question 42

Figure-Ground in Advertising

Answer 42

Absolut Malta beer advertising images

Question 43

Which circles are “figures” and which are holes?

Answer 43

Context is important.

Both circles are small, surrounded, symmetrical, but one is seen as an object (penny), the other as a hole, based on their Context.

+ Figure/Ground segregation is not a simple process, even if the rules themselves seem easy…

This is an ambiguous image. Either a strip and a dot, or a T behind, or a strip and two dots (Accidental view).

Question 44

• Global superiority effect

Answer 44

Parts and wholes
(Navon, 1977):
“Forest before the trees”

The properties of the whole object take precedence over the properties of parts of the object

Question 45

• Why do gestalt principles work?

Answer 45

The Gestalt grouping processes capitalize on certain regularities that characterize the physical world.

In other words, the grouping principles have ecological validity.

Awkwardly worded summary provided by book:

1. Bring together what should be brought together.
2. Split asunder what should be split asunder.
3. Use what you know.
4. Avoid accidents.
5. Seek consensus and avoid ambiguity.

Prof’s summary:

1. GROUPING MECHANISMS.
2. FIGURE GROUND SEGMENTATION.
3. Use heuristics for findings corners, etc… and divide objects into parts.
4. Do not consider as likely the accidental view point representations