Course Modules

I. Foundations

I. Foundations I. Foundations

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Lecture material 0

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Lecture 1: Course overview Lecture 1: Course overview 1

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Lecture 2: Foundation I Lecture 2: Foundation I 2

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Notes Notes 3

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Lecture 2 code.zip Lecture 2 code.zip 4

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The Evolution of the Eye (2010) - Richard Dawkins The Evolution of the Eye (2010) - Richard Dawkins 5

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Lecture 3: Foundation II Lecture 3: Foundation II 6

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Notes Notes 7

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Lecture 3 code.zip Lecture 3 code.zip 8

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Opinion paper 9

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External Url

P. Cavanagh. Vision is getting easier every day. Perception (24), pp. 1227–1232. P. Cavanagh. Vision is getting easier every day. Perception (24), pp. 1227–1232. 10

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Suggested additional readings: Biology 11

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External Url

An introduction to Vision Science. In: Vision Science. Chapter 1. S.E. Palmer. MIT Press. 1999. An introduction to Vision Science. In: Vision Science. Chapter 1. S.E. Palmer. MIT Press. 1999. 12

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David Hubel's Eye, Brain and Vision. Chapter 3. David Hubel's Eye, Brain and Vision. Chapter 3. 13

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Crick, F. and Asanuma, C. (1986) Certain aspects of the anatomy and physiology of the cerebral cortex. In J. L. McClelland and D. E. Rumelhart (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. II, chapter 20, pp. 333-371. MIT Press. Crick, F. and Asanuma, C. (1986) Certain aspects of the anatomy and physiology of the cerebral cortex. In J. L. McClelland and D. E. Rumelhart (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. II, chapter 20, pp. 333-371. MIT Press. 14

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Suggested additional readings: Computational models 15

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Vision. Chapter 1. David Marr. Vision. Chapter 1. David Marr. 16

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Glennerster. Computational theories of vision. Current Biology, 12, R682-685. 2002. Glennerster. Computational theories of vision. Current Biology, 12, R682-685. 2002. 17

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Optional MATLAB Exercise 18

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Pick an image. Load it into matlab using 'imread'. Display the image using 'imagesc' (remember to use 'axis image' to get the aspect ratio right!). Try to display your image using 'imshow''. You might need to rescale your original pixel values to be between 0 and 1. Using the 'max' function, find the pixel of highest intensity in the image. What is the value? Find the pixel with the lowest intensity. What is the value? Try to multiply your image by a small factor (say 0.2) and re-display the image. What happened? What about a large factor? 19

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Further Resources 20

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A good tutorial from Bruno Olshausen (UC Berkeley) to brush up on your linear algebra A good tutorial from Bruno Olshausen (UC Berkeley) to brush up on your linear algebra 21

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II. Color Processing

II. Color Processing II. Color Processing

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Lecture material 0

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Lecture 4: Color I Lecture 4: Color I 1

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External Url

Notes Notes 2

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Lecture 4 code.zip Lecture 4 code.zip 3

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Lecture 5: Color II Lecture 5: Color II 4

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External Url

Notes Notes 5

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Required Readings 7

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External Url

Color Vision: a Microcosm of Vision Science. In: Vision Science. Chapter 3. S.E. Palmer. MIT Press. 1999. Color Vision: a Microcosm of Vision Science. In: Vision Science. Chapter 3. S.E. Palmer. MIT Press. 1999. 8

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Suggested Additional Readings 9

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Brainard & Wandell. Analysis of the retinex theory of color vision. Journal of the Optical Society of America A (1986) vol. 3 (10) pp. 1651-1661. Brainard & Wandell. Analysis of the retinex theory of color vision. Journal of the Optical Society of America A (1986) vol. 3 (10) pp. 1651-1661. 10

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Land. The retinex theory of color vision. Scientific American (1977) vol. 237 (6) pp. 108-128. Land. The retinex theory of color vision. Scientific American (1977) vol. 237 (6) pp. 108-128. 11

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External Url

Russell. Why cosmetics work. In Adams, R., Ambady, N., Nakayama, K., & Shimojo, S. (Eds.) The Science of Social Vision. New York: Oxford University Press. pp. 186-203, 2010. Russell. Why cosmetics work. In Adams, R., Ambady, N., Nakayama, K., & Shimojo, S. (Eds.) The Science of Social Vision. New York: Oxford University Press. pp. 186-203, 2010. 12

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External Url

Changizi, Zhang & Shimojo. Bare skin, blood, and the evolution of primate color vision. Biology Letters, 2, 217-221, 2006. Changizi, Zhang & Shimojo. Bare skin, blood, and the evolution of primate color vision. Biology Letters, 2, 217-221, 2006. 13

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Roorda A and Williams D. The arrangement of the three cone classes in the living human eye. Nature 397, 520-522 (11 February 1999) Roorda A and Williams D. The arrangement of the three cone classes in the living human eye. Nature 397, 520-522 (11 February 1999) 14

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Optional MATLAB Exercise 15

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Choose a color image (either one of the ones used in class or one of your own). Load the image using the 'imread' command. Return the image size using the 'size' command. Display the image using the 'imagesc' command. Convert the image to grayscale using the 'rgb2gray' command. As done in class, try all possible combinations of color swapping for this image and display them. 16

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III. Computing with receptive fields

III. Computing with receptive fields III. Computing with receptive fields

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Lecture material 0

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Lecture 6: Center-surround processing Lecture 6: Center-surround processing 1

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External Url

Notes Notes 2

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Lecture 7: Filtering and convolution Lecture 7: Filtering and convolution 3

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External Url

Notes Notes 4

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Lecture 7 code.zip Lecture 7 code.zip 5

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Required Readings 6

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External Url

David Hubel's Eye, Brain and Vision. Chapter 4. David Hubel's Eye, Brain and Vision. Chapter 4. 7

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Suggested Additional Readings 8

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Attachment

Processing image structure In: Vision Science. Chapter 4. S.E. Palmer. MIT Press. 1999. Processing image structure In: Vision Science. Chapter 4. S.E. Palmer. MIT Press. 1999. 9

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External Url

Koch, C. (1997) Computation and the single neuron. Nature, 385 (6613). pp. 207-210. Koch, C. (1997) Computation and the single neuron. Nature, 385 (6613). pp. 207-210. 10

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McCulloch and Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology (1943) vol. 5 (4) pp. 115-133. McCulloch and Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology (1943) vol. 5 (4) pp. 115-133. 11

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Lettvin et al. What the frog's eye tells the frog's brain. Proc. of the Institute of Radio Engineers (1959) pp. 1940-1951. Lettvin et al. What the frog's eye tells the frog's brain. Proc. of the Institute of Radio Engineers (1959) pp. 1940-1951. 12

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Context Module Sub Header

Optional MATLAB Exercise (from Bruno Olshausen, UC Berkeley) 13

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Create two orthogonal vectors x and y. Show both x and y together on the same plot, using Matlab. Use different line styles for x and y and use 'legend' to denote which is which. Create a vector z that is not orthogonal to x and plot both x and z in Matlab. Compute the angle between these vectors using the inner product and inverse cosine (acos). 14

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Linear neuron: Let’s say we have a linear neuron with two inputs (x1, x2) whose corresponding weights are (-0.5, 1). Plot the weight vector within the 2D space x1, x2, and shade the portion of the input space for which the neuron would have a positive output value. (It may be easier to just do this by hand rather than in MATLAB.) 15

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IV. Computing with simple and complex cells

IV. Computing with simple and complex cells IV. Computing with simple and complex cells

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Lecture material 0

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Lecture 8: Simple cells I Lecture 8: Simple cells I 1

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Notes Notes 2

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Lecture 8 Code Lecture 8 Code 3

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Lecture 9: Simple cells II Lecture 9: Simple cells II 4

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Notes Notes 5

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Lecture 10: Complex cells Lecture 10: Complex cells 6

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Notes Notes 7

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Lecture 10 Code Lecture 10 Code 8

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Required reading 9

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Adelson, E.H. & Bergen, J.R. The plenoptic function and the elements of early vision. In Landy, M. & Movshon, J.A.(Eds), Computational Models of Visual Processing, Cambridge, MA: MIT Press. pp. 3-20, 1991. Adelson, E.H. & Bergen, J.R. The plenoptic function and the elements of early vision. In Landy, M. & Movshon, J.A.(Eds), Computational Models of Visual Processing, Cambridge, MA: MIT Press. pp. 3-20, 1991. 10

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Suggested Additional Readingss 11

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CVPR2012 Speeding Up MATLAB applications CVPR2012 Speeding Up MATLAB applications 12

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Hubel, D., & Wiesel, T. (1959). Receptive fields of single neurones in the cat's striate cortex. J Physiol, 148, 574–591. Hubel, D., & Wiesel, T. (1959). Receptive fields of single neurones in the cat's striate cortex. J Physiol, 148, 574–591. 13

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Hubel, D., & Wiesel, T. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 160, 106–154. Hubel, D., & Wiesel, T. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 160, 106–154. 14

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Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. J Physiol, 195(1), 215–243. Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. J Physiol, 195(1), 215–243. 15

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Carandini & Heeger. (2012) Normalization as a canonical neural computation. Nature Neuroscience, 13, 51-62. Carandini & Heeger. (2012) Normalization as a canonical neural computation. Nature Neuroscience, 13, 51-62. 16

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Jones, J.P. & Palmer, L.A.(1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol, 58,1233-1258. Jones, J.P. & Palmer, L.A.(1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol, 58,1233-1258. 17

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V. Scenes, textures and surfaces

V. Scenes, textures and surfaces V. Scenes, textures and surfaces

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Lecture material 0

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Lecture 11: Frequency channels Lecture 11: Frequency channels 1

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Notes Notes 2

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Code Code 3

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Lecture 12: Scene gist Lecture 12: Scene gist 4

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Notes Notes 5

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Lecture 13: Texture analysis Lecture 13: Texture analysis 6

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Notes Notes 7

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Lecture 13 Code Lecture 13 Code 8

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Lecture 14: Texture cont'd Lecture 14: Texture cont'd 9

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Notes Notes 10

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Lecture 15: Texture cont'd Lecture 15: Texture cont'd 12

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Notes Notes 13

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Required reading 14

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External Url

Oliva, A. (in press). Scene Perception. Chapter in the New Visual Neurosciences. Eds John S. Werner and Leo. M. Chalupa. Oliva, A. (in press). Scene Perception. Chapter in the New Visual Neurosciences. Eds John S. Werner and Leo. M. Chalupa. 15

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Fei-Fei, L., Iyer, A., Koch, C., & Perona, P. (2007). What do we perceive in a glance of a real-world scene? Journal of Vision, 7(1):10, 1–29, http://journalofvision.org/7/1/10, doi:10.1167/7.1.10. Fei-Fei, L., Iyer, A., Koch, C., & Perona, P. (2007). What do we perceive in a glance of a real-world scene? Journal of Vision, 7(1):10, 1–29, http://journalofvision.org/7/1/10, doi:10.1167/7.1.10. 16

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Tutorial on spatial frequency channels (Vanderbilt university) Tutorial on spatial frequency channels (Vanderbilt university) 17

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Javier Portilla and Eero Simoncelli. Representation and Synthesis of Visual Texture. Javier Portilla and Eero Simoncelli. Representation and Synthesis of Visual Texture. 18

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Suggested Additional Readings 19

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External Url

Oliva, A. & Torralba, A. (2006). Building the Gist of a Scene: The Role of Global Image Features in Recognition. Progress in Brain Research: Visual perception, 155, 23-36. Oliva, A. & Torralba, A. (2006). Building the Gist of a Scene: The Role of Global Image Features in Recognition. Progress in Brain Research: Visual perception, 155, 23-36. 20

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External Url

Malik and Perona. Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A (1990) vol. 7 (5) pp. 923-32. Malik and Perona. Preattentive texture discrimination with early vision mechanisms. J. Opt. Soc. Am. A (1990) vol. 7 (5) pp. 923-32. 21

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External Url

Julesz. Textons, the elements of texture perception, and their interactions. Nature (1981) vol. 290 (5802) pp. 91. Julesz. Textons, the elements of texture perception, and their interactions. Nature (1981) vol. 290 (5802) pp. 91. 22

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Julesz. Texton gradients: the texton theory revisited. Biol. Cyb. (1986) vol. 54 (4-5) pp. 245-51. Julesz. Texton gradients: the texton theory revisited. Biol. Cyb. (1986) vol. 54 (4-5) pp. 245-51. 23

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External Url

Leung and Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision (2001) vol. 43 (1) pp. 29-44. Leung and Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision (2001) vol. 43 (1) pp. 29-44. 24

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External Url

Renninger & Malik. When is scene identiﬁcation just texture recognition? Vision Research 44 (2004) 2301–2311. Renninger & Malik. When is scene identiﬁcation just texture recognition? Vision Research 44 (2004) 2301–2311. 25

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Martin et al. Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 26, No. 5, May 2004 Martin et al. Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 26, No. 5, May 2004 26

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Bergen & Landy. Computational Modeling of Visual Texture Segregation. From M. Landy and J. A. Movshon (eds), Computational Models of Visual Processing (pp. 253-271). Cambridge, MA: MIT Press (1991). Bergen & Landy. Computational Modeling of Visual Texture Segregation. From M. Landy and J. A. Movshon (eds), Computational Models of Visual Processing (pp. 253-271). Cambridge, MA: MIT Press (1991). 27

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VI. Motion and stereo processing

VI. Motion and stereo processing VI. Motion and stereo processing

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Lecture material 0

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Lecture 16: Motion processing Lecture 16: Motion processing 1

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Notes Notes 2

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Lecture 17: Motion II Lecture 17: Motion II 3

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Notes Notes 4

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Lecture 18: Stereo Processing Lecture 18: Stereo Processing 5

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Notes Notes 6

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Required Readings 7

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External Url

E. H. Adelson and J. R. Bergen. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A/Vol. 2, No. 2 February 1985. E. H. Adelson and J. R. Bergen. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A/Vol. 2, No. 2 February 1985. 8

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Suggested Additional Readings 9

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Qian N. Computing Stereo Disparity and Motion with Known Binocular Cell Properties, Neural Computation, 1994, 6:390-404. Qian N. Computing Stereo Disparity and Motion with Known Binocular Cell Properties, Neural Computation, 1994, 6:390-404. 10

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Ramachandran V. Perception of shape from shading. Nature 331, 163 - 166 (14 January 1988); doi:10.1038/331163a0 Ramachandran V. Perception of shape from shading. Nature 331, 163 - 166 (14 January 1988); doi:10.1038/331163a0 11

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Computational Model of Neurons in Visual Area MT Computational Model of Neurons in Visual Area MT 12

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Lehky & Sejnowski. Network model of shape-from-shading: neural function arises from both receptive and projective fields. Nature 333, 452 - 454 (02 June 1988); doi:10.1038/333452a0 Lehky & Sejnowski. Network model of shape-from-shading: neural function arises from both receptive and projective fields. Nature 333, 452 - 454 (02 June 1988); doi:10.1038/333452a0 13

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Hoiem et al. Recovering Surface Layout from an Image. International Journal of Computer Vision 75(1), 151–172, 2007 Hoiem et al. Recovering Surface Layout from an Image. International Journal of Computer Vision 75(1), 151–172, 2007 14

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Ohzawa I. Mechanisms of stereoscopic vision: the disparity energy model. Current Opinion in Neurobiology 1998, 8:509–515 Ohzawa I. Mechanisms of stereoscopic vision: the disparity energy model. Current Opinion in Neurobiology 1998, 8:509–515 15

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VII. Invariant object recognition and attention

VII. Invariant object recognition and attention VII. Invariant object recognition and attention

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Lecture material 0

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Lecture 19: Object recognition I Lecture 19: Object recognition I 1

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Notes Notes 2

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Lecture 20: Object recognition II Lecture 20: Object recognition II 3

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Notes Notes 4

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Lecture 21: Object recognition III Lecture 21: Object recognition III 5

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Notes Notes 7

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Lecture 22: Action recognition Lecture 22: Action recognition 8

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Notes Notes 10

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Required Readings 11

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Context Module Sub Header

Suggested Additional Readings 12

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External Url

Cadieu et al. A Model of V4 Shape Selectivity and Invariance. Journal of Neurophysiology (2007) vol. 98 (3) pp. 1733-1750. Cadieu et al. A Model of V4 Shape Selectivity and Invariance. Journal of Neurophysiology (2007) vol. 98 (3) pp. 1733-1750. 13

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Fukushima K. A neural network model for a mechanism of visual pattern recognition. Nov 27, 2003. Video. Fukushima K. A neural network model for a mechanism of visual pattern recognition. Nov 27, 2003. Video. 14

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Li N & DiCarlo J. Unsupervised Natural Experience Rapidly Alters Invariant Object Recogntion in Visual Cortex. Science 321, 1502 (2008). Li N & DiCarlo J. Unsupervised Natural Experience Rapidly Alters Invariant Object Recogntion in Visual Cortex. Science 321, 1502 (2008). 15

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Li N & DiCarlo J. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron 67, 1062–1075, September 23, 2010. Li N & DiCarlo J. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron 67, 1062–1075, September 23, 2010. 16

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Serre T & Poggio T. A neuromorphic approach to computer vision. Communications of the ACM. 2010 Serre T & Poggio T. A neuromorphic approach to computer vision. Communications of the ACM. 2010 17

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Serre T, Oliva A, & Poggio T. A feedforward architecture accounts for rapid categorization. PNAS. 2007 Serre T, Oliva A, & Poggio T. A feedforward architecture accounts for rapid categorization. PNAS. 2007 18

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Serre T, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich, T. Poggio. A quantitative theory of immediate visual recognition Progress in Brain Research, Computational Neuroscience: Theoretical Insights into Brain Function. 2007 Serre T, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich, T. Poggio. A quantitative theory of immediate visual recognition Progress in Brain Research, Computational Neuroscience: Theoretical Insights into Brain Function. 2007 19

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