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

2. September 2, 2014Computer Vision Lecture 1: Human Vision 2 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/

3. September 2, 2014Computer Vision Lecture 1: Human Vision 3 The Visual Attention Lab Cognitive Science, esp. eye movements

4. September 2, 2014Computer Vision Lecture 1: Human Vision 4 A poor guinea pig:

5. September 2, 2014Computer Vision Lecture 1: Human Vision 5 Computer Vision:

6. September 2, 2014Computer Vision Lecture 1: Human Vision 6 Modeling of Brain Functions

7. September 2, 2014Computer Vision Lecture 1: Human Vision 7 Modeling of Brain Functions unit and connection in the interpretive network unit and connection in the gating network unit and connection in the top-down bias network layer l +1 layer l -1 layer l

8. September 2, 2014Computer Vision Lecture 1: Human Vision 8 Example: Distribution of Visual Attention

9. September 2, 2014Computer Vision Lecture 1: Human Vision 9 Selectivity in Complex Scenes

10. September 2, 2014Computer Vision Lecture 1: Human Vision 10 Selectivity in Complex Scenes

11. September 2, 2014Computer Vision Lecture 1: Human Vision 11 Selectivity in Complex Scenes

12. September 2, 2014Computer Vision Lecture 1: Human Vision 12 Selectivity in Complex Scenes

13. September 2, 2014Computer Vision Lecture 1: Human Vision 13 Selectivity in Complex Scenes

14. September 2, 2014Computer Vision Lecture 1: Human Vision 14 Selectivity in Complex Scenes

15. September 2, 2014Computer Vision Lecture 1: Human Vision 15 Human-Computer Interfaces:

16. September 2, 2014Computer Vision Lecture 1: Human Vision 16 Your Evaluation 4 sets of exercises (individual work)4 sets of exercises (individual work) o paper-and-pencil questions: 10% o programming tasks: 30% midterm (75 minutes) 25%midterm (75 minutes) 25% final exam (2.5 hours) 35%final exam (2.5 hours) 35%

17. September 2, 2014Computer 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:

18. September 2, 2014Computer 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), write down your complaint (handwriting is OK), attach it to the assignment or exam, attach it to the assignment or exam, and give it to me or put it in my mailbox. 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.

19. September 2, 2014Computer Vision Lecture 1: Human Vision 19 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.

20. September 2, 2014Computer Vision Lecture 1: Human Vision 20 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.

21. September 2, 2014Computer Vision Lecture 1: Human Vision 21 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

22. Visible light is just a part of the electromagnetic spectrum September 2, 2014Computer Vision Lecture 1: Human Vision 22

23. Cross Section of the Human Eye September 2, 2014Computer Vision Lecture 1: Human Vision 23

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

25. 25 PhotoreceptorBipolarGanglion September 2, 2014Computer Vision Lecture 1: Human Vision

26. 26 Major Cell Types of the Retina September 2, 2014Computer Vision Lecture 1: Human Vision

27. 27 Receptive Fields September 2, 2014Computer Vision Lecture 1: Human Vision

28. 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, 2014Computer Vision Lecture 1: Human Vision

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

30. 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, 2014Computer Vision Lecture 1: Human Vision

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

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