86-375/675 (15-387) Computational Perception
Carnegie Mellon University
The perceptual capabilities of even the simplest biological organisms are far beyond what we can achieve with machines. Whether you look at sensitivity, robustness, or sheer perceptual power, perception in biology just works, and works in complex, ever changing environments, and can pick up the most subtle sensory patterns. Is it the neural hardware? Does biology solve fundamentally different problems? What can we learn from biological systems and human perception?
In this course, we will first study the biological and psychological data of biological perceptual systems in depth, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. The course will focus primarily on visual perception this year. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. The course is targeted to neuroscience and psychology students who are interested in learning computational thinking, and computer science and engineering students who are interested in learning more about the neural basis of perception. Prerequisites: First year college calculus, some linear algebra, probability theory and programming experience are desirable.
|Instructors ||Office (Office hours) ||Email (Phone)
|Tai Sing Lee (Professor) ||Mellon Inst. Rm 115 ||email@example.com (412-268-1060)
|Feng Wang (TA) ||Mellon Inst. Rm 115 || firstname.lastname@example.org
|Jessica Lee (TA) || main campus || email@example.com
|Yi-Ching Lee (TA) || main campus || firstname.lastname@example.org
- Class location and time: PH226A Monday/Wednesday 1:30 p.m - 3:00 p.m.
- Website: http://www.cnbc.cmu.edu/~tai/cp16.html (course info)
- Blackboard: http://www.cmu.edu/blackboard/ (Both 375/675 students should use 375 BB for
access of course materials and announcements. If instructed to do so, 675
students may find additional materials relevant to them.
- Handouts in Blackboard.
- Frisby and Stone Seeing: The computational approach to biological vision . MIT Press, 2010 (recommended).
- Supplementary Simon Prince Computer Vision: Models, Learning, and Inference . Cambridge University Press , 2012. Downloadable at: http://www.computervisionmodels.com. More relevant to graduate students.
- Please turn OFF your laptop, cell phones or any other electronic devices in the classroom.
Grading scheme: A: > 88, B: > 75. C: > 65.
|Evaluation||% of Grade |
|Assignments || 60 |
|Midterm ||10 |
|Final Exam ||30 |
|Term project ||20 (substitition for homework) |
|675 Term Project || Required |
- 5 major assignments that can potentially be decomposed into smaller assignments.
Each assignment will involve Matlab homework as well as reading/research assignments.
- Term project is optional for undergraduate versions of the course.
A student can use the OPTIONAL term project (with a max score of 20 points)
to replace up to two assignments or midterm , used toward the final exam grade.
No collaboration is allowed.
It will require a 6 pages written final report and a presentation to the class.
Matlab codes and additional output should also be submitted as supplementary
materials in a different pdf/doc file and/or matlab zip files.
- Term project is required of 675 students. It is anticipated to take 20-30 hours
Students can work on issues of their choice that are not covered in the course, but
should discuss project proposals with the faculty instructors in advance for
- There will be a midterm to test understanding of the materials covered
discussed in the lectures and the reading assignments.
- Each student will have 7 days grace period for late homework. This grace period
can be used for one or multiple assignments or the term paper. Use it wisely and
do not ask for more.
Blackboard submission after the starting of class time is considered late.
| ||SENSORY CODING || || || |
|M 8/29 ||1. Introduction and philosophy || ch. 1, Marr || |
|W 8/31 ||2. Overview: visual system || ch. 9, 10, Van Essen || |
|W 9/07 ||3. Retinal processing || ch 6 Meister || Homework 1 out |
|M 9/12 ||4. Linear Transform || ch 5 Abbot || |
|W 9/14 ||5. Representations || handout || |
|M 9/19 ||6. Primary visual cortex || paper handout ||   |
|W 9/21 ||7. Sparse coding || paper handout, Olshausen || Homework 2 out |
| ||EARLY PERCEPTUAL INFERENCE || || |
|M 9/26 ||8. Source separation || Sejnowski, Hyvarinen || |
|W 9/28 ||9. Inferring What and Where || Sompolinsky || |
|M 10/3 ||10. Edges and contours || ch 5, Mumford || |
|W 10/5 ||11. Statistics and Bayesian inference || ch 13, Geisler || |
|M 10/10 ||12. Lightness and Color || ch 16, Adelson || |
|W 10/12 ||13. Retinex || ch 17, Land, Morel || |
|M 10/17 ||14. Review/Discussion || || |
|W 10/19 ||15. Midterm || ||   |
| ||SURFACE PERCEPTION || || |
|M 10/24 ||16. Figure-ground segregation || Ch 7 || Midterm grade due |
|W 10/26 ||17. Depth and Stereo || ch 18,19 ||   |
|M 10/31 ||18. Texture and surfaces || ch 2 ||   |
|W 11/2 ||19. Shape from Shading || Zucker, Potetz ||   |
|M 11/7 ||20. Structure from motion || ch 14,15 || |
|M 11/14 ||22. Cue combination and selection || ch 20 ||   |
| ||OBJECT AND SCENES || || |
|W 11/16 ||23. Object perception || ch 8 || |
|M 11/21 ||24. Context and scenes || Torrelba, Oliva || |
|W 11/23 ||Thanksgiving || || |
|M 11/28 ||25. Hierarchical organization || ch 10 || |
|W 11/30 ||26. Attention and Saliency || || |
|M 12/5 ||27. Predictive and constructive vision || || |
|W 12/7 ||28. Review || || Term paper due |
|X 12/X ||Final Examination || || |
Questions or comments:
contact Tai Sing Lee
Last modified: March 1, 2016, Tai Sing Lee