1Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Computational Architectures in Biological Vision, USC, Spring 2001

Lecture 11: Visual Illusions.

Reading Assignments:

None

2Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

What Can Illusions Teach Us?

They exacerbate the failure modes of our visual system, and showartifacts/byproducts of normal visual processing.

Hence, they can help us constrain computational models of visualprocessing.

How relevant is this to computer vision? It can be if we believe that the closer to biological systems our algorithm will be, the best it will perform in real-life situations.

3Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

The Story so Far

Intricate hierarchyof visual areas.

Neurons respond toincreasingly complexstimuli as we ascentthe hierarchy.

Interactions are:- feedforward- intra-area- feedback

and non-linear.

4Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Early Processing

Center-surroundand oriented filters.

Columnar organizationand topographic maps.

5Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Columns

6Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

7Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Non-Linear Interactions

Feedforward model doesnot explain many properties ofearly visual processing.

Some of the non-linearinteractions are well studied.

8Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

9Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Complex Non-linear Interactions

10Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Higher-Level Vision

Similar to lower-level vision,

we can derive several general principles for higher-level processing.

11Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Some Dedicated Circuitry

e.g., looming neurons in insects

directly compute time-to-contact

from a wide array of simple inputs.

12Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Convergence of CuesA given aspect of vision (e.g., depth perception) rarely relies on only one set of cues (e.g., stereo disparity). Rather,

evidence is accumulated about the visual world in several parallel processing streams, which partially overlap in their selectivity.

E.g., distance to an objectcan also be estimated frommotion parallax, vergence,shape from shading, sizeconstancy, occlusions, etc.

In normal vision, all these mechanisms contribute to our percept.

13Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt Psychology

Founded by Max Wertheimer in the early 1900s as a revolt against Wundt�s �molecular� program for psychology.

Gestalt = �unified� or �meaningful whole�

Basic observation: often our experience is richer than our simple sensations.

E.g., rather than perceiving a succession of static frames, we see motion in a movie. So, a rapid sequence of elementary static sensory events yields a different experience altogether, that of motion.

Gestalt Psychologists call this the �phi phenomenon.�

14Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt Psychology

A set of basic �laws� was empirically defined.

At the basis, the law of �Pragnanz� stipulates that not only are we built to experience the whole rather than the multiple individual elementary stimuli, but we are also naturally inclined to do so.

See letter �B� ratherthan collection of curvesegments?

15Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt LawsLaw of closure: if something missing from otherwise complete figure, our percept will consider that the missing part is present.

Law of similarity: group similar items together.

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16Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt Laws

Law of proximity: things close together belong together.

**********

********** 3 groups of 10 stars or10 groups of 3?

**********

Law of symmetry: we tend to group parts into objects according to symmetry.

[ ][ ][ ] (here, despite proximity)

17Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt Laws

Law of continuity: a partially occluded line appears to continue behind the occluder (rather than perceiving 2 line segments ending at theoccluder)

Figure-ground: we usually perceive as figure the smaller part of a B/W image.

18Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Gestalt Psychology

Is not restricted to perception, but also proposes theories for memory, dreams, etc.

Nowadays, Gestalt Theory has lost much of its popularity mainly because of its failure to explain (rather than simply observe) the peculiarities of perception.

Nevertheless, its basic laws and principles remain true and are omnipresent in perception.

19Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Now, on to the Illusions!

The following web site showcase the illusions described in the (web-based) rest of the lecture:

http://www.illusionworks.com

http://humanities.lit.nagoya-u.ac.jp/~illusion/index_e.html

http://thinks.com/webguide/illusions.htm

20Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Illusions

- low-level illusions: often based on the fact that we tend to underestimate acute angles and overestimate obtuse angles, to perceive nearly-right angles as being right, etc.

- constancy illusions: based on the tendency that, in the absence of low-level cues (e.g., a 3D scene seen on a 2D screen), higher-level cues become dominant.

- aftereffects: if we �burn in� (i.e., adapt to) a given image and then remove it, a �reverse� percept occurs. This makes sense in terms of selectively adapting a subpopulation of neurons.

- impossible figures: built on consistent local cues but organized as an inconsistent whole.

21Laurent Itti: CS599 � Computational Architectures in Biological Vision, USC 2001. Lecture 11: Visual Illusions

Illusions

We will see that several simple illusions can be explained as ambiguities in basic computational problems which we studied:

e.g., constancy illusions & color/contrast constancymach bands & contrast gain controlbarber pole & aperture problem

but many remain a mystery!