• Marr, D. and Hildreth, E. "Theory of Edge Detection," Proceedings of the Royal Society of London, Series B, Vol. 207, p.187-217, 1980.
• J. Canny, "A Computational Approach to Edge Detection, " IEEE Transactions on PAMI, vol. 8, pp. 679--698, 1986.
• H. Blum, "A Transformation for Extracting New Descriptors of Shape," Models for the Perception of Speech and Visual Form,W. Wathen-Dunn, ed., MIT Press, 1967, pp. 362--380.

• R. M. Haralick, "Digital Step Edges from Zero Crossing of Second Directional Derivatives," IEEE Trans. PAMI, Vol. PAMI-6, No. 1, Jan. 1984.
• H. Moon, R. Chellappa, and A. Rosenfeld, "Optimal Edge-Based Shape Detection," IEEE Trans. Image Processing, Nov. 2002.

• Markov Random Fields - Introduction
• Besag, J. Spatial Interaction and the Statistical Analysis of Lattice Systems, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 36, No. 2. (1974), pp. 192-236.

• Markov Random Field Modeling in Computer Vision . Webpage
• B. S. Manjunath, T. Simchony, & R. Chellappa, "Stochastic and Determinstic Networks for Texture Segmentation," IEEE Acoustic, Speech, and Signal Processing, Vol. 38, No. 5, July 1990.
• Fure-Ching Jeng, J. W. Woods, & S. Rastogi, "Compound Gauss-Markov Random Fields for Parallel Image Processing," Markov Random Fields Theory and Application, Academic Press, 1993.
• Rotation-invariant Texture Classification

• Camera models: Perp, Para-perp, Affine and Omnidirectional.
• Geometry and Invariance (as related to Camera models)
• Optimization Theory

• S. Geman and D. Geman.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE-PAMI, 6, 1984, 721-741.

• Perona and Malik, "Preattentive texture discrimination with early vision mechanisms," J. Opt. Soc. Am. A, Vol. 7, No. 5, May 1990
• Manjunath, B., and Ma, W."Texture features for browsing and retrieval of image data," IEEE Trans. on PAMI, Vol. 18, No. 8, pp. 836-842, Aug. 1996. download

On Spectral Clustering: Analysis and an algorithm, Andrew Y. Ng, Michael Jordan, and Yair Weiss. In NIPS 14,, 2002

Deepak Verma and Marina Meila  "Comparison of Spectral Clustering Methods" (submitted).

Learning Segmentation with Random Walk Marina Maila and Jianbo Shi Neural Information Processing Systems, NIPS, 2001

Segmentation using eigenvectors: a unifying view. Weiss Y.  Proceedings IEEE International Conference on Computer Vision p. 975-982 (1999)

• Gordon, N.J. Salmond, D.J. Smith, A.F.M. ,Novel approach to nonlinear/non-Gaussian Bayesian state estimation IEE Radar and Signal Processing, Apr 1993
• Arnaud Doucet, Simon Godsill, Christophe Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering Statistics and Computing 2000.
• J. S. Liu and R. Chen, "Sequential Monte Carlo Methods for Dynamic Systems,"  J. Amer. Statist. Assoc., Vol 93 in press. download

• M. Isard and A. Blake, "Condensation -- conditional density propagation for visual tracking," International Journal of Computer Vision, Vol. 29, No. 1, pp. 5--28, 1998. Webpage

• Structure from Motion - Problem Description
• Structure from motion, prior to 1981.
• R. Y. Tsai and T. S. Huang, "Estimating 3-d motion parameters of a rigid planar patch," IEEE ASSP, pp. 525--534, Vol. 30, 1981.
• R. Tsai and T. S. Huang, "Estimating Three-dimensional motion parameters of a rigid planar patch, II: singular value decomposition," IEEE Transactions on Acoustic, Speech and Signal Processing, 30, 1982.

• R.Y. Tsai and T. S. Huang. "Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surface." IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI6: 13--26, 1984.
• Jia-Qi Fang and T. S. Huang, "Some experiments on estimating the 3-D motion parameters of a rigid body from two consecutive frames," IEEE PAMI, Vol PAMI-6, No. 5, September 1984. 

Structure From Motion 2 - Filtering based approaches

• T. J. Broida, S. Chandrashekhar, and R. Chellappa, "Recursive estimation of 3D motion from a monocular image sequence," IEEE Transactions on Aerosp. Electron. Syst., 26(4):639--656, July 1990.
• A. Azarbayejani and A. P. Pentland, "Recursive estimation of motion, structure, and focal length," IEEE Trans. PAMI, Vol. 17, No. 6, June 1995.

• T. J. Broida and Rama Chellappa, "Estimating the kinematics and structure of a rigid object from a sequence of monocular images," IEEE Trans. Pattern Analysis and Machine Intelligence, 13(6):497--513, June 1991.
• Gang Qian and R.Chellappa, "Structure from motion using Sequential Monte Carlo," accpeted by ICCV, 2001. download

• B. K. P. Horn and B. Schunk, Determining optical flow, Artificial Intelligence, 17:185-- 203, 1981.
• H. Liu , R. Chellappa and A. Rosenfeld, Fast Two-frame Multi-scale Dense Optical Flow Estimation Using Discrete Wavelet Filters, Jl. Opt. Soc. of America , A.

• H. Liu, T-H Hong, M. Herman and R. Chellappa, "A General Motion Model and Spatio-Temporal Filters for Computing Optical Flow," International Journal of Computer Vision 22(2), 141-172(1997) download

• M. V. Srinivasan, S. W. Zhang and N. J. Bidwell, Visually Mediated Odometry in Honeybees The Journal of Experimental Biology 200, 2513–2522 (1997)
• G. A. Horridge; H. C. Longuet-HigginsWhat can Engineers Learn from Insect Vision? [and Discussion] Philosophical Transactions: Biological Sciences, Vol. 337, No. 1281, Natural and Artificial Low-Level Seeing Systems. (Sep. 29, 1992), pp. 271-282.

• S. Srinivasan, Extracting Structure from Optical Flow Using the Fast Error Search Techinique," Intl. J. Computer Vision, 37(3), 203-230, 2000.
• A. Roy Chowdhury, Stochastic Approximation for Robust Structure and Motion Estimation With Uncertainty Analysis"

• C. Tomasi and T. Kanade, "Shape and motion from image streams -- a factorization method," International Journal of Computer Vision, 9(2):137--154, 1992.
• C. Poleman and T Kanade, A Paraperspective Factorization Method for Shape and Motion Recovery IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 19, No. 3, March 1997
• J. Li, and R. Chellappa, Structure from planar motion, accepted to IEEE transactions on Image Processing (TIP), Feb., 06
• Bill Triggs,Factorization Methods for Projective Structure and Motion, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1996 (CVPR'96) p. 845

• M. Polleyfeys, et. al. Visual modeling with a hand-held camera International Journal of Computer Vision 59(3), 207-232, 2004.
• A. Agrawal and R. Chellappa, Robust Ego-Motion Estimation and 3D Model Refinement using Surface Parallax, IEEE Trans. on Image Processing, Vol. 15, No. 5, May 2006

• Z. Zhang and O.D. Faugeras, "Estimation of displacements from two 3-D frames obtained from stereo," IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(12):1141--1155, December 1992.
• G. Young and R. Chellappa, "3D motion estimation using a sequence of noisy stereo images: Models, estimation, and uniqueness results," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 735--759, Aug. 1990.

• J.P. Costeira and T. Kanade. A multibody factorization method for independently moving objects. IJCV, 29(3):159--179, 1998.
• C.W. Gear. multibody grouping from motion images. IJCV, 29(2):133--150, 1998.

• R. Vidal, Y. Ma, S. Soatto and S. Sastry.
  Two-View Multibody Structure from Motion.
  International Journal of Computer Vision, volume 68, number 1, pages 7-25, June, 2006.

• B. Horn and M. Brooks, The Variational Approach to Shape from Shading, CVGIP 33, 174-208 (1986).
• R. Fronkot and R. Chellappa, A Method for Enforcing Integrability in Shape from Shading Algorithms, IEEE Trans. on PAMI, Vol. 10, No. 4, 1988, pp. 439-451.
• Qinfen Zheng and R. Chelleppa, Estimation of Illuminant Direction, Albedo, and Shape from Shading, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7):680--702, July 1991.

• Marr, "Vision"
• Grimson, "From images to surfaces"

• H. S. Lim and T. O. Binford, "Stereo Correspondence: A Hierarchical Approach," Proceedings of Image Understanding Workshop, pp. 234-241, Feburary, 1987.
• V. Venkateswar and R.Chellappa, "Hierarchical stereo and motion correspondence using feature groupings." IJCV, 1995. 

• B. Bhanu and O. Faugeras,  "Shape matching of two-dimensional objects," IEEE Transactions on PAMI, Vol. PAMI-6, No. 2, pp. 137-- 155, March 1984.
• S. Dubois and F. Glanz, "An Autoregrssive Model Approach to Two-Dimensional Shape Classification," IEEE Trans. PAMI, Vol. PAMI-8, No. 1, January 1986.

• M. Turk and A. Pentland, "Eigenfaces for recognition." Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, 1991.
• K. Etemad and R. Chellappa, "Discriminant Analysis for Recognition of Human Face Images," Journal of Optical Society of America A, pp. 1724-1733, Aug. 1997. download

• P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection", IEEE Trans. PAMI, Vol. 19, No. 7, July 1997. download
• Martin Lades, Jan C. Vorbruggen, J.Buhmann, J.Lange, C von der Malsburg, R.P. Wurtz, and W. Konen, "Distortion Invariant Object Recognition in the Dynamic Link Architecture", IEEE Trans. Computers, Vol 42, No. 3, pp 300-311,1993 download
• L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg, "Face recognition by elastic graph matching," IEEE Trans. PAMI, Vol. 19, No. 7, 1997. download

• Grimson, "Object Recognition by Computer"
• Geometric Hashing

• S. Zhou, V. Krueger, and R. Chellappa, "Probabilistic Recognition of Human Faces from Video", CVIU, 2003.

• N. Vaswani, A. Roy Chowdhury, and R. Chellappa, "Activity Analysis"