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Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines...

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Low-Level Vision
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Page 1: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Low-Level Vision

Page 2: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Low Level Vision--outline

• Problem to be solved

• Example of one computation—lines

• How simple computations yield more complex information

Page 3: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Problems to be solved

Problem 1: IndeterminaciesProblem 2: The input to resolve these indeterminacies is impoverished

Page 4: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Indeterminacies

Many of the qualities of objects that we would like to know about trade off with other qualities.

shape/orientationreflectance/light source/shadowsize/distance

Page 5: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Shape/Orientation

Page 6: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Reflectance/Light Source/Shadow

This joke turns on the assumption that you will see a shadow, not a difference in reflectance of the object (moon) across its face.

Page 7: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Size/Distance

Page 8: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Problems

So problem 1 is that the types of information that we want trade off

with one another

Problem 2 is that the initial information the visual system has is extremely impoverished

Page 9: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

This is the input

You end up with the #of objects, their sizes,shapes, distances,textures, motions.

Page 10: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.
Page 11: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

How do you get from one to the other?

Researchers divide this question into two parts:Low-level vision: we assume that we can’t get much information out of this array of intensity values.There must be algorithms that summarize this info.High-level vision: taking the output of the low-level processes and transforming it to get objects & their properties.

Page 12: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Simple computation

35 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 5

You saw this before. . . . Can you tell what this is?

Page 13: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Crucial summary--find edges

An edge is a sudden discontinuity in intensity.

35 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 535 35 35 5 5 5

Page 14: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Why edges?Edges frequently correspond to the boundaries of objects; a map of edges is a good start to identifying objects.

Edges are invariant to lighting conditions.

Page 15: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

How to find edges?

Computationallyeasy to find discontinuities

Compare means of adjacent columns, rows, diagonals

Page 16: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

What about textures?

Why don’t you see a million objects when you see a hat with many “edges” (Herringbone pattern)?

Page 17: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Assess at more than one scale

Assess neighboring columns: yields five edges

Page 18: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Assess every three columns (i.e., take the mean) yields one edge

Assess at more than one scale

Page 19: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.
Page 20: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Biological evidence

Page 21: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Retina

Page 22: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Ganglion cells: center-surround

Page 23: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

On-off can combine to form line detectors

Page 24: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Or an edge detector

Page 25: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Hubel & Wiesel’s experiments

Page 26: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Biological evidence

It does seem that some of the cells relatively early in the visual processing stream care about edges.

Page 27: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

All of this was about lines.

Now how do you get distance, shapes, etc.

Page 28: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Shape/orientation indeterminacy

Perkin’s laws--conjunctions of lines assumed to correspond to different 3D shapes.

Page 29: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Perkins’ Laws

Possibly built into early visual processing.

Pop-out with perkins’ laws type angles, but not with other angles.

Page 30: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

That’s it for lines

Focus on other assumptions

Page 31: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Light source/reflectance/shadow

What’s this?

Page 32: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Assumption 1: surfaces are uniformly colored. (That’swhy shading gives the impression of 3 dimensions. Shading is assumed to be due to hills & valleys.

Page 33: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Light/reflectance/shadow

Shadow: light is assumed to be coming from above.

Page 34: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.
Page 35: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Reflectance/Light Source/Shadow

Page 36: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

How is constancy figured out?

Obviously, absolute constancy is not calculated

Page 37: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Local contrast

Assumption 3: the brightest thing around is white; the darkest thing around is black.

Page 38: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Distance/size

Isn’t it the case that we frequently just know the size an object should be?

This is familiar size and it’s actually not that powerful a cue.

Page 39: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Familiar size

When you remove the cue of height in the picture planethe person looks tiny.

Page 40: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.
Page 41: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Cues to distanceConvergence--not very effective

More effective are a range of cues that can be evaluatedin a picture plane, and so are often called pictorial cues.

Page 42: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Occlusion

Page 43: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Texture Gradient

Page 44: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.
Page 45: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Linear perspective

Page 46: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Height in picture plane

Page 47: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Atmospheric Perspective

Page 48: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Stereopsis

Very important cue. This is NOT a pictorial cue.

Based on the fact that the two eyes get slightly different views of the world.

Page 49: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

StereopsisDifference between what the left eye and right eye sees is called retinal disparity.

Farther object, less difference

Page 50: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Stereopsis

Problem: how do you match up the views of the two retinas if objects are similar?This is called the correspondence problem.(Consider that highlights differ because of different reflectionsand there are geometric distortions due to seeing things froma different angle.)

Page 51: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

StereopsisSolutions to the correspondence problem

1. Uniqueness constraint: an object in the left eye can be matched to only one item in the right eye.2. Epipolar line constraint: because the eyes don’t move independently in vertical dimension, there is a limited number of places that an object on the left retina can be on the right retina.

Page 52: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Where will the boat be in the right retina?

It can’t be just anywhere. It must be somewhere on the horizon line

Page 53: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

More generally. . . .

Page 54: Low-Level Vision. Low Level Vision--outline Problem to be solved Example of one computation—lines How simple computations yield more complex information.

Summary• Problems

– Indeterminacies– Impoverished Input

• Lines– Computations– Biology

• Solutions– Shape/orientation (Perkins’s laws)– reflectance/light source/shadow (uniform color, local

contrast)– Size/distance (familiar size, convergence, occlusion,

texture gradient, linear perspective, relative height, atmospheric perspective, stereopsis).


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