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September 2, 2014 Computer Vision Lecture 1: Human 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun

September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Page 1: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

1

Welcome to

CS 675 – Computer Vision

Fall 2014

Instructor: Marc Pomplun

Page 2: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Instructor – Marc PomplunOffice: 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: [email protected]

Website: http://www.cs.umb.edu/~marc/cs675/

Page 3: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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The Visual Attention Lab

Cognitive Science, esp. eye movements

Page 4: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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A poor guinea pig:

Page 5: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Computer Vision:

Page 6: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Modeling of Brain Functions

Page 7: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Modeling of Brain Functionsunit and connectionin the interpretive network

unit and connectionin the gating network

unit and connectionin the top-down bias network

layer l +1

layer l -1

layer l

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Example: Distribution of Visual Attention

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Selectivity in Complex Scenes

Page 10: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Selectivity in Complex Scenes

Page 11: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Selectivity in Complex Scenes

Page 12: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Selectivity in Complex Scenes

Page 13: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Selectivity in Complex Scenes

Page 14: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Selectivity in Complex Scenes

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Human-Computer Interfaces:

Page 16: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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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%

Page 17: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Grading

95%: A 90%: A-

74%: C+ 70%: C 66%: C-

86%: B+ 82%: B 78%: B-

62%: D+ 56%: D 50%: D-

50%: F

For the assignments, exams and your course grade, the following scheme will be used to convert percentages into letter grades:

Page 18: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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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.

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Computer VisionComputer 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.

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Computer VisionIn 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.

Page 21: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

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Computer VisionDigital Images

Binary Image Processing

Color

Image Filtering

Basic Image Transformation

Edge Detection

Image Segmentation

Shape Representation

Texture

Depth

Motion

Object Recognition

Image Understanding

Page 22: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

Visible light is just a part of the electromagnetic spectrum

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Page 23: September 2, 2014Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2014 Instructor: Marc Pomplun Instructor: Marc Pomplun

Cross Section of the Human Eye

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Photoreceptor

Bipolar

Ganglion

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Major Cell Types of the Retina

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Receptive Fields

September 2, 2014 Computer Vision Lecture 1: Human Vision

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Coding of Visual Information in the Retina Photoreceptors: Trichromatic Coding

Peak wavelength sensitivities of the three cones:Blue cone: Short- Blue-violet (420 nm) Green cone: Medium- Green (530 nm)Red Cone: Long- Yellow-green (560nm)

September 2, 2014 Computer Vision Lecture 1: Human Vision

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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 Vision Lecture 1: Human Vision

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