Welcome to
CS 675 –
Computer Vision
Fall 2014
Instructor: Marc Pomplun
September 2, 2014
Computer Vision
Lecture 1: Human Vision
1
Instructor – Marc Pomplun
Office:
S-3-171
Lab:
S-3-135
Office Hours:
Tuesdays 3:30-4:00, 5:15–7:00
Thursdays 5:15– 6:00
Phone:
287-6443 (office)
287-6485 (lab)
E-Mail:
marc@cs.umb.edu
Website:
http://www.cs.umb.edu/~marc/cs675/
September 2, 2014
Computer Vision
Lecture 1: Human Vision
2
The Visual Attention Lab
Cognitive Science, esp. eye movements
September 2, 2014
Computer Vision
Lecture 1: Human Vision
3
A poor guinea pig:
September 2, 2014
Computer Vision
Lecture 1: Human Vision
4
Computer Vision:
September 2, 2014
Computer Vision
Lecture 1: Human Vision
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Modeling of Brain Functions
September 2, 2014
Computer Vision
Lecture 1: Human Vision
6
Modeling of Brain Functions
unit and connection
in the interpretive network
layer l +1
unit and connection
in the gating network
unit and connection
in the top-down bias network
layer l
layer l -1
September 2, 2014
Computer Vision
Lecture 1: Human Vision
7
Example: Distribution of Visual Attention
September 2, 2014
Computer Vision
Lecture 1: Human Vision
8
Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
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Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
10
Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
11
Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
12
Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
13
Selectivity in Complex Scenes
September 2, 2014
Computer Vision
Lecture 1: Human Vision
14
Human-Computer Interfaces:
September 2, 2014
Computer Vision
Lecture 1: Human Vision
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Your Evaluation
• 4 sets of exercises (individual work)
o paper-and-pencil questions:
10%
o programming tasks:
30%
• midterm (75 minutes)
25%
• final exam (2.5 hours)
35%
September 2, 2014
Computer Vision
Lecture 1: Human Vision
16
Grading
For the assignments, exams and your course grade,
the following scheme will be used to convert
percentages into letter grades:
 95%: A
 90%: A-
 86%: B+
 82%: B
 78%: B-
 74%: C+
 70%: C
 66%: C-
 62%: D+
 56%: D
 50%: D-
 50%: F
September 2, 2014
Computer Vision
Lecture 1: Human Vision
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Complaints about Grading
If you think that the grading of your
assignment or exam was unfair,
• write down your complaint (handwriting is OK),
• attach it to the assignment or exam,
• and give it to me or put it in my mailbox.
I will re-grade the exam/assignment and return it to
you in class.
September 2, 2014
Computer Vision
Lecture 1: Human Vision
18
Computer Vision
Computer Vision is the science of building systems
that can extract certain task-relevant information from
a visual scene.
Such systems can be used for applications such as
optical character recognition, analysis of satellite and
microscopic images, magnetic resonance imaging,
surveillance, identity verification, quality control in
manufacturing etc.
September 2, 2014
Computer Vision
Lecture 1: Human Vision
19
Computer Vision
In a way, Computer Vision can be considered the
inversion of Computer Graphics.
A computer graphics systems receives as its input the
formal description of a visual scene, and its output is
a visualization of that scene.
A computer vision system receives as its input a
visual scene, and its output is a formal description of
that scene with regard to the system’s task.
Unfortunately, while a computer graphics task only
allows one solution, computer vision tasks are often
ambiguous, and it is unclear what the correct output
should be.
September 2, 2014
Computer Vision
Lecture 1: Human Vision
20
Computer Vision
Digital Images
Binary Image Processing
Color
Image Filtering
Basic Image Transformation
Edge Detection
Image Segmentation
Shape Representation
Texture
Depth
Motion
Object Recognition
Image Understanding
September 2, 2014
Computer Vision
Lecture 1: Human Vision
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Visible light is just a part of the
electromagnetic spectrum
September 2, 2014
Computer Vision
Lecture 1: Human Vision
2222
Cross Section of the Human Eye
September 2, 2014
Computer Vision
Lecture 1: Human Vision
2323
September 2, 2014
Computer
24 Vision
Lecture 1: Human Vision
Photoreceptor
Bipolar
Ganglion
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Computer
25 Vision
Lecture 1: Human Vision
Major Cell Types of the Retina
September 2, 2014
Computer
26 Vision
Lecture 1: Human Vision
Receptive Fields
September 2, 2014
Computer
27 Vision
Lecture 1: Human Vision
Coding of Visual Information in the Retina
 Photoreceptors: Trichromatic Coding
 Peak wavelength sensitivities of the three cones:
Blue cone:
ShortBlue-violet (420 nm)
Green cone:
MediumGreen (530 nm)
Red Cone:
LongYellow-green (560nm)
September 2, 2014
Computer
28 Vision
Lecture 1: Human Vision
September 2, 2014
Computer
29 Vision
Lecture 1: Human Vision
Coding of Visual Information in the Retina
 Retinal Ganglion Cells:
 Opponent-Process Coding
 Negative afterimage:
 The image seen after a portion of the retina is exposed to an
intense visual stimulus; consists of colors complimentary to
those of the physical stimulus.
 Complimentary colors:
 Colors that make white or gray when mixed together.
September 2, 2014
Computer
30 Vision
Lecture 1: Human Vision
September 2, 2014
Computer
31 Vision
Lecture 1: Human Vision
September 2, 2014
Computer
32 Vision
Lecture 1: Human Vision