U. Minn. Psy 5036
Daniel Kersten
Final project ideas

Format

Should be written like a scientific paper. 
Might require most of the code to be put in appendices. 
Can use modules you find elsewhere, but preserve copyrights, and reference
Will post final notebooks on the class web site.
Your "audience" will be your class peers.

Types of projects

Three broad types of projects are listed below.

Support of the scientific field, course development:

Develop Mathematica package for: Pyramid image decomposition: Laplacian pyramid or wavelets

Develop Mathematica tutorial: Singular value decomposition & principal components

Perceptual demonstrations

Motion illusions
e.g. stereograms, autostereograms, lightness illusions
with interactive parameter variation
http://viperlib.york.ac.uk/

Natural visual "mistakes"

Look for everyday visual mistakes or illusions. For example, sometimes a shadow is mistaken for a pigment change, the slant of a surface is under or over-estimated, the size of an object is incorrect, or varies depending on context, ...
Use a digital camera to document these naturally occuring illusions. Find a dozen or so, classify them, and interpret them from the point of view of vision solving a computational problem.

Visual psychophysics (quantitative measurements)

What does the eye see best?

E.g. Right-side up vs. upside-down faces. (Mathematica basic template notebook available)

Data analysis/report of data collected elsewhere (by you)

OK to complement other projects, but need to clarify how the work load is divided up.

Classification images

The idea is the development of a computer program that can reveal an observer's internal perceptual template for a task.
See: http://www.journalofvision.org/2/1/introduction.html
See: Gold et al. (2000) Ringach (1998), , Olman and Kersten (2004)

Importance of the phase spectrum in visual recognition

See, Glass patterns, Barlow and Olshausen

Models of human/biological vision

Computational models

Machine vision: but should have  discussion/comparison of relevance to human vision.

Orthogonal wavelet decomposition in Mathematica

See: http://www.cns.nyu.edu/~eero/software.html for Matlab versions

Bayesian edge detection

Statistical analyses of images
e.g. Bayesian edge detector, correlational analyses, ...

Analyze image statistics for natural image contours

Histogram matching for textures

(e.g. Heeger, 1995)

Dynamic model of hand reach

Neural network simulations

Biologically constrained neural network for stereo vision

Neural network models
	e.g. adaptive receptive field development, visual attention,...

Neuroimaging simulation

Cortical magnification

Use function interpolation to illustrate how the retintopic map maps the visual field onto primary visual cortex.
See: http://gandalf.psych.umn.edu/~kersten/kersten-lab/demos/RetCorLogPolInt.mov

Linear systems analysis of cortical activity in fMRI

E.g. Boynton et al. (1996), Engel et al. (1997)

References

Adelson, E. H., Simoncelli, E., & Hingorani, R. (1987). Orthogonal Pyramid Transforms for Image Coding. Paper presented at the Proc. SPIE - Visual Communication & Image Proc. II, Cambridge, MA.
Barlow, H. B., & Olshausen, B. A. (2004). Convergent evidence for the visual analysis of optic flow through anisotropic attenuation of high spatial frequencies. J Vis, 4(6), 415-426.
Boynton, G. M., Engel, S. A., Glover, G. H., & Heeger, D. J. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci, 16(13), 4207-4221.
Daugman, J. G. (1988). An information-theoretic view of analog representation in striate cortex, Computational Neuroscience. Cambridge, Massachusetts: M.I.T. Press.
Engel, S. A., Glover, G. H., & Wandell, B. A. (1997). Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb Cortex, 7(2), 181-192.
Gold, J. M., Murray, R. F., Bennett, P. J., & Sekuler, A. B. (2000). Deriving behavioural receptive fields for visually completed contours. Curr Biol, 10(11), 663-666.
Heeger, D., & Bergen, J. (1995). Pyramid-based texture analysis/synthesis. Paper presented at the Proceedings of ACM SIGGRAPH.
Konishi, S. M., Yuille, A. L., Coughlan, J. M., & Zhu, S. C. (2003). Statistical edge detection: Learning and evaluating edge cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 57-74.
Olman, C. A., & Kersten, D. (2004). Classification objects, ideal observers & generative models. Cognitive Science, 28, 227-239.
Marr, D., & Poggio, T. (1976). Cooperative computation of stereo disparity. Science, 194, 283-287.
Marr, D., Palm, G., & Poggio, T. (1978). Analysis of a cooperative stereo algorithm. Biol. Cybernetics, 28, 223-239.
Mayhew, J. E. W., & Frisby, J. P. (1981). Psychophysical and Computational Studies Towards a Theory of Human Stereopsis. Artificial Intelligence, 17, 349-385.
Moore, C., & Engel, S. A. (2001). Neural response to perception of volume in the lateral occipital complex. Neuron, 29(1), 277-286.
Ringach, D. L. (1998). Tuning of orientation detectors in human vision. Vision Res, 38(7), 963-972.
Schwartz, E. L. (1980). A quantitative model of the functional architecture of human striate cortex with application to visual illusion and cortical texture analysis. Biol Cybern, 37(2), 63-76.
Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995). An Internal Model for Sensorimotor Integration. Science, 269(29 September), 1880-1882.
http://library.wolfram.com/howtos/images/#histograms

© 2004, 2006 Daniel Kersten, Computational Vision Lab, Department of Psychology,  University of Minnesota. kersten . org