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1B50 – Percepts and Concepts Daniel J Hulme
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1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Dec 23, 2015

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Page 1: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

1B50 – Percepts and Concepts

Daniel J Hulme

Page 2: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Outline

• Cognitive Vision– Why do we want computers to see?– Why can’t computers see?– Introducing percepts and concepts

• Visual System– The Eye and Brain– Early visual processes– Edge Detection

• Percepts and Concepts– Late Visual Processes– Concepts

Page 3: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Lecture 1: Reminder• Cognitive Science: scientific study of intelligence• Intelligence: …. (something to do with brains?)

• Vision is an integral part (and catalyst for the evolution) of the brain

• Ambiguity and the Distal and Proximal stimulus

• Using experience to construct (perceive) one form from a potentially infinite amount of possible forms

Page 4: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.
Page 5: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Lecture 2: Reminder• The significance of retinal structure

– Rods and Cones distribution

• Receptive Fields and Neural Nets

• Early visual process: Edge Detection

• Convolution between an image and a kernel

Page 6: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Fovea

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Rods & Cones

Sum Inputs

ActivationFunction

Ganglion Cells

Optic Nerve

Activation

Horizontal & Bipolar Cells

Weighting & Join Inputs

Light Source

Stimuli Detectors

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Page 7: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Periphery

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Rods & Cones

Sum Inputs

ActivationFunction

Ganglion Cells Optic Nerve

Activation

Horizontal & Bipolar Cells

Weighting & Join Inputs

Light Source

Stimuli Detectors

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63

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Page 8: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Fovea

10

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10

20

30

0

0

Rods & Cones

Sum Inputs

ActivationFunction

Ganglion Cells

Optic Nerve

Activation

Horizontal & Bipolar Cells

Weighting & Join Inputs

Light Source

Stimuli Detectors

10

200

150

100

20

150

0

0

> 50

> 50

> 50

> 50

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x 1

x 5

x 10

Page 9: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Periphery

Rods & Cones

Sum Inputs

ActivationFunction

Ganglion Cells Optic Nerve

Activation

Horizontal & Bipolar Cells

Weighting & Join Inputs

Light Source

Stimuli Detectors

10

200

150

100

20

150

0

0

> 50∑

630

x 1

x 5

x 10

10

20

30

10

20

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0

0

Page 10: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Receptive Fields• Receptive field – the photoreceptors that affect

the ganglion cell

• One photo-receptive cell (rod or cone) may be a member of several receptive fields

• Tile the retina surface

• Always circular in shape

• On center, off surround Off center, on surround

• Edge (contour) sensitive

• Receptive fields are modeled by Difference of Gaussians

Page 11: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Primary Visual Cortex

• Groups of neurons process information about:– Form of objects– Contrast of objects– Location of objects– Movement of objects– Color of objects

Page 12: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Visual Cortex Cells Response

• Lines or edges with certain orientation or size

• Angles or corners

• Movement in one direction, but not another direction

• Two-thirds of vision research involves these types of cells

• It is thought that more complex cells actually respond to specific faces, etc

Vertical Receptive Field

Overlapping and Orientation

Page 13: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Recognising Objects• It is not completely know how we perceive solidity/planes

• Gestalt ‘grouping’ school of thought:

– proximity - how elements tend to be grouped together depending on their closeness

– similarity - how items that are similar in some way tend to be grouped together

– closure - how items are grouped together if they tend to complete a pattern

– continuity - how items are organized into figures according to symmetry, regularity, and smoothness

Page 14: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Electrophysiology

Page 15: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Stereopsis - Stereo (binocular) vision

• Allows us to approximate distance of objects up to a few meters away

• Point matching procedure is used to calculate disparity (use template matching)

• Binocular disparity relates to depth

Page 16: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Monocular Disparity• Monocular cues are cues to depth that

are effective when viewed with only one eye.

• Interposition: When one object overlaps or partly blocks our view of another object, we judge the covered object as being farther away from us

• Atmospheric Perspective: The air contains microscopic particles of dust and moisture that make distant objects look hazy or blurry

• Texture Gradient: A texture gradient arises whenever we view a surface from a slant, rather than directly from above.

• Linear Perspective: Linear perspective refers to the fact that parallel lines, such as railroad tracks, appear to converge with distance

• Size Cues: Consider the size of an object's retinal image relative to other objects when estimating its distance.

• Height Cues: We perceive points nearer to the horizon as more distant than points that are farther away from the horizon

• Motion Parallax: Motion parallax appears when objects at different distances from you appear to move at different rates when you are in motion

Page 17: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Motion

• Object displacement usually correlates to depth. I.e. objects moving towards us usually expand

• Visual system correlates image points from one moment to the next

• Evidence of short range and long range motion detectors

Page 18: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Continuity

Page 19: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Continuity

Page 20: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Concepts

• One cannot fully explain perception without showing that the beliefs it produces tends to be true

• The benefit of perception is to yield true beliefs – even if this means generating ‘incorrect’ perceptions

• Observable and Hidden Variables

• Uggs Valley

Page 21: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Closing remarks

• Cognitive Science as a science

• Sub-symbolic vs Symbolic

• Classical AI vs Modern AI

• Bayesian approach

• Computational issues

• How to solve the problem…

Page 22: 1B50 – Percepts and Concepts Daniel J Hulme. Outline Cognitive Vision –Why do we want computers to see? –Why can’t computers see? –Introducing percepts.

Questions