September 2, 2014 Computer Vision Lecture 1: Human Vision

1

Welcome to

CS 675 – Computer Vision

Fall 2014

Instructor: Marc Pomplun

September 2, 2014 Computer Vision Lecture 1: Human Vision

2

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: marc@cs.umb.edu

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

September 2, 2014 Computer Vision Lecture 1: Human Vision

3

The Visual Attention Lab

Cognitive Science, esp. eye movements

September 2, 2014 Computer Vision Lecture 1: Human Vision

4

A poor guinea pig:

September 2, 2014 Computer Vision Lecture 1: Human Vision

5

Computer Vision:

September 2, 2014 Computer Vision Lecture 1: Human Vision

6

Modeling of Brain Functions

September 2, 2014 Computer Vision Lecture 1: Human Vision

7

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

September 2, 2014 Computer Vision Lecture 1: Human Vision

8

Example: Distribution of Visual Attention

September 2, 2014 Computer Vision Lecture 1: Human Vision

9

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

Selectivity in Complex Scenes

September 2, 2014 Computer Vision Lecture 1: Human Vision

15

Human-Computer Interfaces:

September 2, 2014 Computer Vision Lecture 1: Human Vision

16

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

17

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:

September 2, 2014 Computer Vision Lecture 1: Human Vision

18

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

19

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.

September 2, 2014 Computer Vision Lecture 1: Human Vision

20

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.

September 2, 2014 Computer Vision Lecture 1: Human Vision

21

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

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

24September 2, 2014 Computer Vision Lecture 1: Human Vision

25

Photoreceptor

Bipolar

Ganglion

September 2, 2014 Computer Vision Lecture 1: Human Vision

26

Major Cell Types of the Retina

September 2, 2014 Computer Vision Lecture 1: Human Vision

27

Receptive Fields

September 2, 2014 Computer Vision Lecture 1: Human Vision

28

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

29September 2, 2014 Computer Vision Lecture 1: Human Vision

30

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

31September 2, 2014 Computer Vision Lecture 1: Human Vision

32September 2, 2014 Computer Vision Lecture 1: Human Vision