[Main] [Publications] [Abstracts and Posters] [Software] [Awards and Scholarships] [Computer Vision Related Notes] [Links]
Below are a collection of links to computer vision related notes I've written (as a mean of grasping certain topics) and collected during my tenure as a grad student. Comments? Suggestions? Feel free to send me email at kosta at cs dot yorku dot ca
Table of contents:
Motion:
- A review of the characterization and recovery of motion from image sequences by Konstantinos G. Derpanis (York University): link
- The motion field and its affine approximation by Konstantinos G. Derpanis (York University): pdf
- The kinematic parameterization of affine motion by Konstanitnos G. Derpanis (York University): pdf
- A brief note on the computation of optical and affine flow by Konstantinos G. Derpanis (York University): pdf
- Single 2D Orientation Estimation via the Structure Tensor by Konstantinos G. Derpanis (York University): pdf
- A short review of Masahiko Shizawa and Kenji Mase's Superposition Principle for multiple motion analysis by Konstantinos G. Derpanis (York University): pdf
- A short review of phase-based optical flow by Konstantinos G. Derpanis (York University): pdf
- A summary of Bayesian motion estimation by Konstantinos G. Derpanis (York University): pdf
- A summary of the derivation of the frequency space interpretation of a translating scene. This forms the basis for several frequency domain motion estimation techniques by Konstantinos G. Derpanis (York University): pdf
- Relationship between SSD and cross correlation template matching by Konstantinos G. Derpanis (York University): pdf
Frequency analysis:
- A review of representing frequency content in complex notation by Konstantinos G. Derpanis (York University): pdf
- Fourier transform definitions by Konstantinos G. Derpanis (York University): pdf
- Review of Quadrature filters by Konstantinos G. Derpanis (York University): pdf
- General proof of smoothness for Eero Simoncelli's Donut Operator by Konstantinos G. Derpanis (York University): pdf
- Fast Gabor filtering by Konstantinos G. Derpanis (York University): pdf
- Review of Fourier analysis and sampling by James W. MacLean (University of Toronto): pdf
- A brief introduction to the Fourier transform by Hagit Shatkay (Queen's University): link
- Proof of the Time-Frequency Uncertainty Principle by (Bob Williamson coauthor of the Fourier Song): link
Multiscale analysis:
- Review of the Gaussian Pyramid by Konstantinos G. Derpanis (York University): pdf
- A tutorial on scale-space theory by Tony Lindeberg (KTH NADA): link
(Left) original image, (Right) several slices from the linear scale-space representation.
- A review of Gabor filters by Konstantinos G. Derpanis (York University): pdf
- Outline of the relationship between the difference-of-Gaussian and Laplacian-of-Gaussian representations by Konstantinos G. Derpanis (York University): pdf
- Review of scale-space relations of rescaled signals by Konstantinos G. Derpanis (York University): pdf
- A review of integral image-based representations by Konstantinos G. Derpanis (York University): pdf
Steerable filters:
The three leftmost images represent the modulated G_{2} basis filters and the rightmost represents the resulting steered G_{2} filter. For an animation depicting the steering of the first to fourth derivative of the 2D Gaussian, see avi.
- Notes on steerable filters by David J. Heeger (New York University): pdf
- A summary of the steerability of one-dimensional nth-order derivatives by Konstantinos G. Derpanis (York University): pdf
- A summary of Mats T. Andersson's adaptive (steerable) filters by Konstantinos G. Derpanis (York University): pdf
Expectation Maximization Algorithm (EM algorithm):
- A summary of Jensen's Inequality by Konstantinos G. Derpanis (York University): pdf
- A review of the Expectation Maximization (EM) Algorithm by Konstantinos G. Derpanis (York University): pdf
- K-means clustering by Konstantinos G. Derpanis (York University): pdf
- An introduction to mixture models and the EM algorithm by Justus H. Piater (Universite de Liege): pdf
- A tutorial on the EM algorithm by Sean Borman (University of Notre Dame): pdf
Differential geometry:
- A non-rigorous introduction to differential geometry by Bryan S. Morse (Brigham Young University): pdf
- A summary of the Isophote Curvature in an Image by Konstantinos G. Derpanis (York University): pdf
Kalman filter:
- R.E. Kalman's seminal paper on the Kalman filter: link
- A Kalman filter introduction by Greg Welch and Gary Bishop (University of North Carolina at Chapel Hill): link
- A historical view of Least Squares Estimation , beginning with Gauss' contributions up to Kalman's by H.W. Sorenson: pdf
- Introduction to Linear Dynamical Systems by Konstantinos G. Derpanis (York University): pdf
Interest point operators:
- A derivation of the Harris Corner Detector by Konstantinos G. Derpanis (York University): pdf, a copy of the original paper titled "A Combined Corner and Edge Detector" by Chris Harris and Mike Stephens can be found here
- A draft survey (with slides) on local invariant features by Tinne Tuytelaars (K.U.Leuven) and Krystian Mikolajczyk (University of Surrey): link
Computer vision course notes:
- Lecture notes covering introductory topics of computer vision by Richard Wildes (York University): link
- Lecture notes covering mathematical methods for computer vision by George Bebis (University of Nevada): link
- Video lecture on model-based vision (1987) by Joseph Mundy (Brown University): link
Mathematical methods:
- Review of the calculus of variations by Konstantinos G. Derpanis (York University): pdf
- Review of the Singular Value Decomposition by Konstantinos G. Derpanis (York University): pdf
- Notes of standard material on mathematical methods for robotics and computer vision by Carlo Tomasi (Duke University): pdf
- Notes on standard optimization methods in computer vision by Neil Thacker (University of Manchester) and Tim Cootes (University of Manchester): pdf
Statistics/probability notes:
- Proof of the Cramer-Rao bound for a single parameter by Konstantinos G. Derpanis (York University): pdf
- Summary of Mean Shift clustering by Konstantinos G. Derpanis (York University): pdf
- Mean Shift derivation by Konstantinos G. Derpanis (York University): pdf
Mean shift path
- A tutorial on the Mean Shift algorithm that covers both theory and applications by Yaron Ukrainitz and Bernard Sarel (Weizmann Institute of Science): ppt
- An overview of RANdom SAmple Consensus (RANSAC) algorithm by Konstantinos G. Derpanis (York University): pdf
- The integral of a Gaussian by Konstantinos G. Derpanis (York University): pdf
Linear algebra course notes:
- Professor Gilbert Strang's Linear Algebra Class Lecture Videos: link
- Reference material covering linear algebra and the properties of real and complex matrices: link
Machine learning:
- Collection of machine learning related video lectures: link
- Sam Roweis' (University of Toronto) collection of machine learning related tutorial notes: link
- Max Welling's (University of California, Irvine) classnotes in machine learning: link
Technical writing:
- "How to write a conference Paper", by William Freeman (MIT): ppt
- "Notes on technical writing", by Don Knuth (Stanford): pdf
- "What's wrong with these equations", by David Mermin, Physics Today, Oct., 1989: pdf
Miscellaneous:
- A summary of the SVD approach for decomposing general 2D linear digital filters into the sum of separable filters by Konstantinos G. Derpanis (York University): pdf
- The Bhattacharyya measure for measuring the dissimilarity between distributions by Konstantinos G. Derpanis (York University): pdf
- Fourier transform of the Gaussian by Konstantinos G. Derpanis (York University): pdf
- Implementing continuous convolutions by Konstantinos G. Derpanis (York University): pdf
- Review of binomial filters by Konstantinos G. Derpanis (York University): pdf
- Review of Lagrange multipliers by Konstantinos G. Derpanis (York University): pdf
- A comparative study of the application of graph cut methods in vision, graphics and machine learning by Sudipta Sinha (University of North Carolina): pdf
- Kermit Sigmon's Matlab Primer Third Edition: pdf
- A quick introduction to OpenGL and GLUT by Bill Kapralos (York University): Part 1 (pdf) and Part 2 (pdf)
- A review of linear least squares by David R. Martin (Boston College): pdf
- A short introduction to rotations with quaternions by Peter Grogono (Concordia University): pdf
- A list of titles of Jim Blinn's (Microsoft Research) column for the IEEE Computer Graphics and Applications journal. The articles deal (at a fairly highly level) with topics in graphics and signal processing: link
- Videos of ICCV 2003 podium presentations: link
- Resources for students and scholars by Fredo Durand (MIT): link
- Videos of talks on a wide range of topics in computer vision: link
- "Paper Gestalt" (appearing in the Secret Proceedings of CVPR 2010) describes an automated system for deciding whether to accept/reject a conference paper using state-of-the-art computer vision methods by Carven von Bearensquash (University of Phoenix)
Last Updated: August 1, 2010.