Instructor:
Daniel Kersten. Office: S212 Elliott Hall. Phone: 612 625-2589 email:
kersten@umn.edu
Office hours: Wednesdays 11:00-12:00 am or by appointment.
TA: Dominic Mussack. Office: N25 Elliott Hall.
email:muss0080@umn.edu
Office
hours: By appointment. Email TA for access to Elliott basement
Questions can be posted to the class forum on Moodle.
The visual perception of what is in the world is accomplished continually, instantaneously, and usually without conscious thought. The very effortlessness of perception disguises the underlying richness of the problem. We can gain insight into the processes and functions of human vision by studying the relationship between neural mechanisms and visual behavior through computer analysis and simulation. Students will learn about the anatomy and neurophysiology of vision and how they relate to the phenomona of perception. An underlying theme will be to treat vision as a process of statistical inference. There will be in-class programming exercises using the language Mathematica. No prior programming experience is required; however, some familiarity with probability, calculus and linear algebra is helpful.
(EV) Early Vision. Yuille and Kersten.To appear as a chapter in From Neuron to Cognition via Computational Neuroscience, M.A. Arbib, James J. Bonaiuto Editors, Cambridge MA: The MIT Press, in 2016 (preprint pdf)
Understanding Vision: Theory, Models, and Data. Li Zhaoping. 2014.(publisher page) (author's web outline)
(FV) Foundations of Vision. Wandell (web)
(NVN) The New Visual Neurosciences. John S. Werner and Leo M. Chalupa, edts. 2014. (pdf)
Mathematica is the primary programming environment for this course. If you wish to purchase Mathematica for Students see http://www.wolfram.com/products/student/mathforstudents/index.html.
Accessing Mathematica on the CLA servers:
For user help on using Mathematica, see: http://mathematica.stackexchange.com
http://ipython.org
http://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/index.html
http://www.scipy.org
http://nbviewer.ipython.org/github/gestaltrevision/python_for_visres/blob/master/index.ipynb
The grade weights are:
The programming assignments
will use the Mathematica programming environment. No prior experience
with Mathematica is necessary. To find out where Mathematica is available, go to http://www.oit.umn.edu/computer-labs/software/index.htm
There are also two bonus assignments, #0 and #2.5 (see Assignments column).
Assignment due BEFORE
class start time on the day due.
Late Policy: Assignments turned in within 24 hours following
the due date will have 15% deducted from the assignment score. Assignments
turned in between 24 and 48 hours following the due date will have 30% deducted
from the score. Assignments more than 48 hours late will receive a score
of zero.
Check this section before each class for recent additions and revisions.
(5036W Course material from 2013)
Lecture notes are in Mathematica Notebook and pdf format. You can download the Mathematica notebook files below to view with Mathematica or Wolfram CDF Player (which is free).
| University Calendar | Date | Lecture | Main Readings | Supplementary Material | Assignments due |
|
I.
Introduction |
Sep
9 |
1. Introduction to Computational Vision | 1.IntroToComputationalVision.nb Accessing Mathematica Olshausen, B. A. (2013). Perception as an Inference Problem. In M. Gazzaniga (Ed.), The New Cognitive Neurosciences, 5th Edition (pp. 1–22). MIT Press. (pp. 1–18). MIT Press. (pdf)
|
Screencast: http://www.wolfram.com/broadcast/screencasts/handsonstart/ (WITH AUDIO) Kersten, D., & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology, 13(2), 1-9. (pdf) EV: Section 1 |
|
Sep 14 |
2.Limits to Vision | 2.LimitsToVision.nb (pdf) Hecht, S., Shlaer, S., & Pirenne, M. H. (1942). Energy, quanta, and vision. Journal of General Physiology, 25, 819-840. (pdf) |
Barlow, H. B. (1981). Critical Limiting Factors in the Design of the Eye and Visual Cortex. Proc. Roy. Soc. Lond. B, 212, 1-34. (pdf) Baylor, D. A., Lamb, T. D., & Yau, K. W. (1979). Responses of retinal rods to single photons. Journal of Physiology, Lond., 288, 613-634. (pdf)
|
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Sep 16 |
3. The Ideal Observer | 3.TheIdealObserver.nb (pdf) |
Griffiths, T. L., & Yuille, A. (2008). A primer on probabilistic inference. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press (pdf). Try your luck against an ideal discriminator of dot density YesNoDotDiscriminationDemo.nb |
Upload Assignment #0 to Moodle (1% bonus) Assignment_0_Mathematica.nb
|
|
Sep 21
|
4. Ideal observer analysis: Humans vs. ideals. Neurons vs. ideals | 4.IdealObserverAnalysis.nb (pdf)
|
Kersten and Mamassian (2008), Ideal observer theory. The New Encyclopedia of Neuroscience, Squire et al., editors (pdf). Geisler, W. S. (2011). Contributions of ideal observer theory to vision research. Vision Research, 51(7), 771–781.(pdf) Burgess, A. E., Wagner, R. F., Jennings, R. J., & Barlow, H. B. (1981). Efficiency of human visual signal discrimination. Science, 214(4516), 93-94. (pdf) Deneve, S., Latham, P. E., & Pouget, A. (1999). Reading population codes: a neural implementation of ideal observers. Nature Neuroscience, 2(8), 740–745. (pdf) Measure your absolute efficiency to discriminate dot density using a 2AFC task 2AFCDotDiscriminationDemo.nb |
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|
II. Image
formation, pattern synthesis |
Sep 23 |
5.Psychophysics: tools & techniques |
Farell, B. & Pelli, D. G. (1999) Psychophysical methods, or how to measure a threshold and why. In R. H. S. Carpenter & J. G. Robson (Eds.), Vision Research: A Practical Guide to Laboratory Methods, New York: Oxford University (pdf) Press.http://psych.nyu.edu/pelli/ Morgenstern, Y., & Elder, J. H. (2012). Local Visual Energy Mechanisms Revealed by Detection of Global Patterns. Journal of Neuroscience, 32(11), 3679–3696.
For a free Python psychophysics package, see: http://www.psychopy.org |
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Sep 28 |
6. Bayesian decision theory & perception | 6.BayesDecisionTheory.nb Geisler, W. S., & Kersten, D. (2002). Illusions, perception and Bayes. Nat Neurosci, 5(6), 508-510. (pdf) |
|
Upload Assignment #1 to Moodle (7%) Assignmt_1IdealDetector.nb |
|
Sep 30 |
7. Limits to spatial resolution, image modeling, introduction to linear systems | 7.ImageModelLinearSystems.nb Campbell, F. W., & Green, D. (1965). Optical and retinal factors affecting visual resolution. Journal of Physiology (Lond.), 181, 576-593. (pdf) |
Williams, D. R. (1986). Seeing through the photoreceptor mosaic. 9(5), 193-197. (pdf) |
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|
III.
Early visual coding |
Oct 5 |
8. Linear systems analysis | 8.LinearSystemsOptics.nb (pdf) |
EV: Section 2
Tutorials: |
|
Oct 7 |
9. Features and filters. Spatial filter models of early human vision | 9.NeuralSpatialFiltering.nb and matlab code |
Campbell, F. W., & Robson, J. R. (1968). Application of Fourier Analysis to the Visibility of Gratings. Journal of Physiology 197, 551-566. (pdf) De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Res, 22(5), 545-559. (pdf) Watson, A. B. (1987). Efficiency of a model human image code. J Opt Soc Am A, 4(12), 2401-2417. (pdf) IPython demo of gabor filtering Steerable pyramids: http://www.cns.nyu.edu/~eero/steerpyr/ |
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Oct 12 |
10. Features and filters. Local processing & image analysis | 10.ImageProcessing.nb (pdf) Gollisch, T., & Meister, M. (2010). Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina. Neuron, 65(2), 150–164. (pdf) |
Albrecht,
D. G., De Valois, R. L., & Thorell, L. G. (1980). Visual cortical
neurons: are bars or gratings the optimal stimuli? Science, 207(4426),
88-90.(pdf)
Adelson, E. H., & Bergen, J. R. (1991). The plenoptic function and the elements of early vision. In M. S. Landy & J. A. Movshon (Eds.), Computational Models of Visual Processing. Cambridge, MA: The MIT Press: A Bradford Book.(pdf) ClassificationImage demo (ReverseCorrelation.nb) Ahumada, A. J., Jr. (2002). Classification image weights and internal noise level estimation. J Vis, 2(1), 121-131. (pdf) |
Upload Assignment
2 to Moodle (7%) Assignmt_2_Convolve.nb |
|
Oct 14 |
11. Coding efficiency: Retina |
Geisler, W. S. (2008). Visual perception and the statistical properties of natural scenes. Annu Rev Psychol, 59, 167-192. (pdf)
|
Laughlin,
S. (1981). A simple coding procedure enhances a neuron's information
capacity. Z Naturforsch [C], 36(9-10), 910-912.(pdf)
|
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Oct 19 |
12. Coding efficiency: Cortex |
12.SpatialCodingEfficiency.nb Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annu Rev Neurosci, 24, 1193-1216.(pdf) |
Laughlin, S. B., de Ruyter van Steveninck, R. R., & Anderson,
J. C. (1998). The metabolic cost of neural information. Nat Neurosci,
1(1), 36-41.(pdf)
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IV.
Intermediate-level vision, integration, grouping |
Oct 21 |
13. Edge detection | 13.EdgeDetection.nb (pdf) |
Hubel, D. H., & Wiesel, T. N. (1977). Ferrier lecture. Functional architecture of macaque monkey visual cortex. Proc R Soc Lond B Biol Sci, 198(1130), 1-59. (pdf) |
|
Oct 26 |
MID-TERM | MID-TERM Study guide (16%) |
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Oct 28 |
14. Objects and scenes from images. The visual cortical pathways and hierarchy. | 14.ScenesfromImages.nb Kersten, D. J., & Yuille, A. L. (2014). Inferential Models of the Visual Cortical Hierarchy. In M. S. Gazzaniga & G. R. Mangun (Eds.), The New Cognitive Neurosciences, 5th Edition (pp. 1–22). MIT Press. (pdf) |
Zhou H, Friedman HS, von der Heydt R (2000) Coding of border ownership in monkey visual cortex. J Neuroscience 20: 6594-6611. (pdf) | ||
Nov 2 |
15. Scene-based generative models | 15.SurfaceGeometryDepth.nb Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian Inference. Annual Review of Psychology, 55, 271-304. (pdf) |
Anaconda python installation recommended. We will use Juypter/IPython, a browser-based notebook interface for python. |
Upload Assignment #2.5 to Moodle (3% bonus) Assignment_2.5_IPython | |
Nov 4 |
16. Shape-from-X | 16.ShapeFromX.nb (pdf) |
Reflectance map: Shape from shading: Horn BKP (1986) Robot Vision. Cambridge MA: MIT Press. Ch 11 (pdf). Barron, J. T., & Malik, J. (2015). Shape, Illumination, and Reflectance from Shading. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1670–1687. http://doi.org/10.1109/TPAMI.2014.2377712 (pdf) Belhumeur,
P. N., Kriegman, D. J., & Yuille, A. (1997). The Bas-Relief Ambiguity.
(pdf) Johnson, M. K., & Adelson, E. H. (2011). Shape Estimation in Natural Illumination. Computer Vision and Pattern Recognition (CVPR), 2553–2560. |
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Nov 9 |
17. Shape from shading |
17. Shape from shading.nb 17. IPython notebook
|
See here for illustrations of IPython cell types, and here for a collection of sample notebooks. Look here for some good tips on installation, as well as the parent directory for excellent ipython-based course material on scientific computing using Monte Carlo methods. For a quick start to scientific programming, see: http://nbviewer.ipython.org/gist/rpmuller/5920182 For a comphrensive coverage of scientific python see:https://scipy-lectures.github.io And for a ground-up set of tutorials on python see: http://learnpythonthehardway.org/book/ Switching from matlab to python? http://wiki.scipy.org/NumPy_for_Matlab_User |
|
|
Nov 11 |
18. Motion: optic flow |
OpenCV python demo: OpticFlowSparse.ipynb |
Horn, B. K. P., & Schunck, B. G. (1981). Determining Optical Flow. Artificial Intelligence, 17, 185-203. (pdf) Optic Flow (2013) Florian Raudies, Scholarpedia, 8(7):30724. doi:10.4249/scholarpedia.30724 (link) (with available matlab code) Optic flow matlab code from Michael Black's lab. (link) Borst, A. (2007). Correlation versus gradient type motion detectors: the pros and cons. Philos Trans R Soc Lond B Biol Sci, 362(1479), 369-374.
pdf) http://web.mit.edu/persci/people/adelson/illusions_demos.html EV: Section 2.4 FV: Chapter 10 |
Upload Assignment 3 to Moodle |
|
Nov 16 |
19. Motion: biological, human perception | 19.MotionHumanPerception.nb Weiss, Y., Simoncelli, E. P., & Adelson, E.
H. (2002). Motion illusions as optimal percepts. Nat Neurosci, 5(6),
598-604. |
Heeger,
D. J., Simoncelli, E. P., & Movshon, J. A. (1996). Computational
models of cortical visual processing. Proc Natl Acad Sci U S A, 93(2),
623-627. (pdf) EV: Section 4.4 |
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Nov 18 |
20. Material perception |
V1 and lightness (pdf) Doerschner, K., Fleming, R. W., Yilmaz, O., Schrater, P. R., Hartung, B., & Kersten, D. (2011). Visual motion and the perception of surface material. Current Biology, 21(23), 2010–2016. (pdf) |
Fleming,
R. W., Dror, R. O., & Adelson, E. H. (2003). Real-world illumination
and the perception of surface reflectance properties. J Vis, 3(5),
347-368. (link)
Adelson, E. H. (1993). Perceptual organization and the judgment of brightness. Science, 262, 2042-2044 (pdf) Boyaci, H., Fang, F., Murray, S. O., & Kersten, D. (2007). Responses to lightness variations in early human visual cortex. Curr Biol, 17(11), 989-993 (pdf)http://www.bilkent.edu.tr/~hboyaci/Vision/ http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html http://gandalf.psych.umn.edu/users/kersten/kersten-lab/demos/transparency.html http://gandalf.psych.umn.edu/~kersten/kersten-lab/demos/MatteOrShiny.html |
Upload Final project title & paragraph outline to Moodle (2%) | |
Nov 23 |
21. Texture. |
Freeman, J., & Simoncelli, E. P. (2011). Metamers of the ventral stream. Nature Publishing Group, 14(9), 1195–1201. http://doi.org/10.1038/nn.2889 (pdf) |
Heeger DJ and Bergen JR, Pyramid Based Texture Analysis/Synthesis, Computer Graphics Proceedings, p. 229-238, 1995. (pdf).
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Nov 25 |
22.Science writing (Thanksgiving week) | 22.ScienceWriting.nb (pdf) |
Gopen & Swan, 1990 (pdf) |
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| V.
High-level vision |
Nov 30 |
23.Perceptual integration | 23.PerceptualIntegration.nb (pdf) |
McDermott,
J., Weiss, Y., & Adelson, E. H. (2001).
Beyond junctions: nonlocal form constraints on motion interpretation.
Perception, 30(8), 905-923. (pdf) Hillis, J. M., Ernst, M. O., Banks, M. S., & Landy, M. S. (2002). Combining sensory information: mandatory fusion within, but not between, senses. Science, 298(5598), 1627-1630.(pdf) Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415(6870), 429-433. (pdf) Stocker, A. A., & Simoncelli, E. (2008). A Bayesian model of conditioned perception. Advances in Neural Information Processing Systems, 20, 1409–1416. (pdf) IPython demo of ideal integration EV: Section 5 |
|
Dec 2 |
24. Object recognition I |
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415–434. (pdf) |
Liu,
Z., Knill, D. C., & Kersten, D. (1995). Object Classification
for Human and Ideal Observers. Vision Research, 35(4), 549-568. (pdf) Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 111(23), 8619–8624. (pdf) |
Upload Assignment 4 to Moodle (7%) |
|
Dec 7 |
25. Object recognition II feeforward architectures | 25_Bidirectional_I.key.pdf (pdf) Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nat Neurosci, 5(7), 682-687. (pdf) |
Grill-Spector, K. (2003). The neural basis of object perception. Curr Opin Neurobiol, 13(2), 159-166.(pdf) Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci, 2(1), 79-87. (pdf) Bullier, J. (2001). Integrated model of visual processing. Brain Res Brain Res Rev, 36(2-3), 96-107. (pdf) Tenenbaum JB: Bayesian modeling of human concept learning. In Advances in Neural Information Processing Systems. Edited by Kearns MSS, Solla A, Cohn DA: Cambridge, MA: MIT Press: 1999.(pdf) |
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Dec 9 |
26. Object recognition III feedback architectures | 26_BidirectionalFeedback.key.pdf (pdf)
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Torralba, A., Oliva, A., Castelhano, M. S., & Henderson, J. M. (2006). Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychol Rev, 113(4), 766-786. (pdf) Chikkerur, S., Serre, T., Tan, C., & Poggio, T. (2010). What and where: A Bayesian inference theory of attention. Vision Research, 50(22), 2233–2247.
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Upload a complete DRAFT of FINAL PROJECT to Moodle by Wednesday December 9th, 5 PM. |
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Dec 14 |
27. Vision
for action, spatial layout, heading. Homegeneous coordinates.
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27.SpatialLayoutScenes.nb
Kalman filter notes (pdf) |
Longuet-Higgins,
H. C., & Prazdny, K. (1980). The Interpretation of a Moving Retinal
Image. Proceedings of the Royal Society of London B, 208, 385-397. (pdf)
|
Upload your peer comments to Moodle by Monday Dec 14th. (5% ) | |
Dec 16
(Last day of class) |
FINAL EXAM | Final Study Guide | FINAL EXAM (16%) Drafts returned to you with Instructor comments |
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Dec 21 |
Upload Final Revised Draft of Project to Moodle (28%) |
Goal: This course integrates the behavioral, neural and computational principles of perception. Students often find the interdisciplinary integration to be the most challenging aspect of the course. Through writing, you will learn to synthesize results from diverse and typically isolated disciplines. By writing about your project work, you will learn to think through the broader implications of your project, and to effectively communicate the rationale and results of your contribution in words. You will do a final page research report in which you will describe, in the form of a scientific paper, the results of an original computer program on a topic in computational vision.
Your final project will involve: 1) a computer program and; 2) a 2000-3000 word final paper describing your project. For your computer project, you will do one of the following: 1) Write a program to simulate a model from the computer vision literature ; 2) Design and program a method for solving some problem in perception. 3) Design and program a psychophysical experiment to study an aspect of human visual perception. The results of your final project should be written up in the form of a short scientific paper or Mathematica Notebook, describing the motivation, methods, results, and interpretation.
If you choose to write your program in Mathematica, your paper and program can be combined can be formated as a Mathematica notebook. See: Books and Tutorials on Notebooks.
Your paper will be critiqued and returned for you to revise and resubmit in final form. You should write for an audience consisting of your class peers.
Completing the final paper involves 4 steps. Each step requires that you email a document to the teaching assistant.
Outline (2% of grade). You will submit a working title and paragraph outline by the deadline noted in the syllabus. These outlines will be critiqued in order to help you find an appropriate focus for your papers. (Consult with the instructor or TA for ideas well ahead of time).
Complete draft (5% of grade). A double-spaced, complete draft of the paper must be turned in by the deadline noted in the syllabus. Papers should be between 2000 and 3000 words. In addition to the title, author and date lines, papers must include the following sections: Abstract, Introduction, Methods, Results, Discussion, and Bibliography. Use citations to motivate your problem and to justify your claims. Cite authors by name and date, e.g. (Marr & Poggio, 1979). Citations should be original sources, not wikipedia. Use a standard citation format, such as APA. (The UM library has information on style guides, and in particular APA style.) Papers must be typed, with a page number on each page. Figures should be numbered and have figure captions. This draft will be reviewed by your instructor and one of you class peers. The point break down for the total 5% is: 2 pts for completing Introduction, 2 pts for completing Methods, 1 pt for completing Discussion)
Peer commentary (5% of grade). You will submit a written commentary (200 to 500 words) on a complete draft of one of your class peers. The project drafts and commentaries will be anonymous. The commentary should provide feedback to improve the quality and clarity of the writing.
Final draft (20% of grade) and "Cover letter" (8% of grade). The final draft must be turned in by the date noted on the syllabus. The "Cover letter" should describe how your revision addressed comments from your peer evaluator and from your instructor. It should itemize key criticisms together with a brief description of the changes you made to your draft manuscript.
Some Resources:
Student Writing Support: Center for Writing, 306b Lind Hall and satellite
locations (612.625.1893) http://writing.umn.edu.
NOTE: Plagiarism, a form of scholastic dishonesty and a disciplinary offense, is described by the Regents as follows: Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering, forging, or misusing a University academic record; or fabricating or falsifying data, research procedures, or data analysis. http://www1.umn.edu/regents/policies/academic/Code_of_Conduct.html. See too: http://writing.umn.edu/tww/plagiarism/ andhttp://writing.umn.edu/tww/plagiarism/definitions.html
