1. Welcome to CS 675 – Computer Vision Spring 2018
Instructor: Marc Pomplun
January 23, 2018
Computer Vision Lecture 1: Human Vision

2. Instructor – Marc Pomplun
Office: S-3-171
Lab: S-3-135
Office Hours: Tuesdays 4:30-5:30, 7:00-8: Thursdays 4:30-5:30
Phone: (office)
Website:
January 23, 2018
Computer Vision Lecture 1: Human Vision

3. The Visual Attention Lab
Cognitive Science, esp. eye movements
January 23, 2018
Computer Vision Lecture 1: Human Vision

4. Computer Vision Lecture 1: Human Vision
A poor guinea pig:
January 23, 2018
Computer Vision Lecture 1: Human Vision

5. Computer Vision Lecture 1: Human Vision
January 23, 2018
Computer Vision Lecture 1: Human Vision

6. Modeling of Brain Functions
January 23, 2018
Computer Vision Lecture 1: Human Vision

7. Modeling of Brain Functions
unit and connection l a y e r l + 1
in the interpretive network
unit and connection
in the gating network
unit and connection
in the top-down bias network l a y e r l l a y e r l - 1
January 23, 2018
Computer Vision Lecture 1: Human Vision

8. Example: Distribution of Visual Attention
January 23, 2018
Computer Vision Lecture 1: Human Vision

9. Selectivity in Complex Scenes
January 23, 2018
Computer Vision Lecture 1: Human Vision

10. Selectivity in Complex Scenes
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Computer Vision Lecture 1: Human Vision

11. Selectivity in Complex Scenes
January 23, 2018
Computer Vision Lecture 1: Human Vision

12. Selectivity in Complex Scenes
January 23, 2018
Computer Vision Lecture 1: Human Vision

13. Selectivity in Complex Scenes
January 23, 2018
Computer Vision Lecture 1: Human Vision

14. Selectivity in Complex Scenes
January 23, 2018
Computer Vision Lecture 1: Human Vision

15. Human-Computer Interfaces:
January 23, 2018
Computer Vision Lecture 1: Human Vision

16. Computer Vision Lecture 1: Human Vision
Your Evaluation
6 sets of exercises (individual work)
paper-and-pencil questions: %
programming tasks: %
midterm (75 minutes) %
final exam (2.5 hours) %
January 23, 2018
Computer Vision Lecture 1: Human Vision

17. Computer Vision Lecture 1: Human Vision
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
January 23, 2018
Computer Vision Lecture 1: Human Vision

18. Computer Vision Lecture 1: Human Vision
Academic Dishonesty
You are allowed to discuss problems regarding your homework with other students in the class.
However, you have to do the actual work (computing values, writing algorithms, drawing graphs, etc.) by yourself.
You cannot copy anything from other sources (Wikipedia, other students’ work, etc.)
The first violation will result in zero points for the entire homework or exam (and official notification).
The second violation will result in failing the course.
January 23, 2018
Computer Vision Lecture 1: Human Vision

19. Complaints about Grading
If you think that the grading of your homework was unfair, please talk to the TA, Shaohua Jia.
If you are still unhappy afterwards, please talk to me.
If you think that the grading of your midterm exam was unfair, please indicate your concerns by putting sticky notes or attaching an extra sheet and give it to me or put it into my mailbox.
January 23, 2018
Computer Vision Lecture 1: Human Vision

20. Computer Vision Lecture 1: Human 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.
January 23, 2018
Computer Vision Lecture 1: Human Vision

21. Computer Vision Lecture 1: Human 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.
January 23, 2018
Computer Vision Lecture 1: Human Vision

22. Computer Vision Lecture 1: Human Vision
Digital Images
Binary Image Processing
Color
Image Filtering
Basic Image Transformation
Edge Detection
Image Segmentation
Shape Representation
Texture
Object Recognition
Image Understanding
Depth
Motion
January 23, 2018
Computer Vision Lecture 1: Human Vision

23. Visible light is just a part of the electromagnetic spectrum
January 23, 2018
Computer Vision Lecture 1: Human Vision

24. Cross Section of the Human Eye
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Computer Vision Lecture 1: Human Vision

25. Computer Vision Lecture 1: Human Vision
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Computer Vision Lecture 1: Human Vision

26. Computer Vision Lecture 1: Human Vision
Photoreceptor
Bipolar
Ganglion
January 23, 2018
Computer Vision Lecture 1: Human Vision

27. Computer Vision Lecture 1: Human Vision
Major Cell Types of the Retina
January 23, 2018
Computer Vision Lecture 1: Human Vision

28. Computer Vision Lecture 1: Human Vision
Receptive Fields
January 23, 2018
Computer Vision Lecture 1: Human Vision

29. 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)
January 23, 2018
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.
January 23, 2018
Computer Vision Lecture 1: Human Vision

31. Computer Vision Lecture 1: Human Vision
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Computer Vision Lecture 1: Human Vision

32. Computer Vision Lecture 1: Human Vision
January 23, 2018
Computer Vision Lecture 1: Human Vision