# HG changeset patch # User Robert McIntyre # Date 1365658823 0 # Node ID 057d47fc4789ded634147d8ba06379a23e19c466 # Parent cf6eea5d651b8f37cfa58c056367243cd1984923 reviewing ullman's stuff. diff -r cf6eea5d651b -r 057d47fc4789 org/gabor.org --- a/org/gabor.org Tue Mar 12 04:34:37 2013 +0000 +++ b/org/gabor.org Thu Apr 11 05:40:23 2013 +0000 @@ -164,10 +164,7 @@ (draw-kernel! (gabor-kernel 50 4 (/ Math/PI 3) 3 0) (str img-base "gabor-50-4-pi-over3-3.png")) #+end_src - - - - + #+name: gabor-tail #+begin_src clojure diff -r cf6eea5d651b -r 057d47fc4789 org/ideas.org --- a/org/ideas.org Tue Mar 12 04:34:37 2013 +0000 +++ b/org/ideas.org Thu Apr 11 05:40:23 2013 +0000 @@ -116,3 +116,11 @@ ;;Builders wrought with greatest care ;;Each minute and unseen part; ;;For the Gods see everywhere. + + +* misc + - use object tracking on moving objects to derive good static + detectors and achieve background separation + - temporal scale pyramids. this can help in verb recognition by + making verb identification time-scale independent (up to a certian + factor) \ No newline at end of file diff -r cf6eea5d651b -r 057d47fc4789 org/literature-review.org --- a/org/literature-review.org Tue Mar 12 04:34:37 2013 +0000 +++ b/org/literature-review.org Thu Apr 11 05:40:23 2013 +0000 @@ -42,13 +42,13 @@ - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust technique for matching two uncalibrated images through the recovery - of the unknown epipolar geometry,” Artificial In- telligence, 78, + of the unknown epipolar geometry,” Artificial Intelligence, 78, (1995), pp. 87-119. - + * Alignment by Maximization of Mutual Information, Paul A. Viola PhD Thesis recommended by Winston. Describes a system that is able @@ -63,18 +63,198 @@ - Differential entropy seems a bit odd -- you would think that it should be the same as normal entropy for a discrete distrubition embedded in continuous space. How do you measure the entropy of a - half continuous, half discrete random variable? + half continuous, half discrete random variable? Perhaps the + problem is related to the delta function, and not the definition + of differential entropy? - Expectation Maximation (Mixture of Gaussians cool stuff) (Dempster 1977) - Good introduction to Parzen Window Density Estimation. Parzen density functions trade construction time for evaulation - time.(Pg. 41) + time.(Pg. 41) They are a way to transform a sample into a + distribution. They don't work very well in higher dimensions due + to the thinning of sample points. + + - Calculating the entropy of a Markov Model (or state machine, + program, etc) seems like it would be very hard, since each trial + would not be independent of the other trials. Yet, there are many + common sense models that do need to have state to accurately model + the world. + + - "... there is no direct procedure for evaluating entropy from a + sample. A common approach is to model the density from the sample, + and then estimate the entropy from the density." + + - pg. 55 he says that infinity minus infinity is zero lol. + + - great idea on pg 62 about using random samples from images to + speed up computation. + + - practical way of terminating a random search: "A better idea is to + reduce the learning rate until the parameters have a reasonable + variance and then take the average parameters." + + - p. 65 bullshit hack to make his parzen window estimates work. + + - this alignment only works if the initial pose is not very far + off. + Occlusion? Seems a bit holistic. +** References + - "excellent" book on entropy (Cover & Thomas, 1991) [Elements of + Information Theory.] + + - Canny, J. (1986). A Computational Approach to Edge Detection. IEEE + Transactions PAMI, PAMI-8(6):679{698 + + - Chin, R. and Dyer, C. (1986). Model-Based Recognition in Robot + Vision. Computing Surveys, 18:67-108. + + - Grimson, W., Lozano-Perez, T., Wells, W., et al. (1994). An + Automatic Registration Method for Frameless Stereotaxy, Image + Guided Surgery, and Enhanced Realigy Visualization. In Proceedings + of the Computer Society Conference on Computer Vision and Pattern + Recognition, Seattle, WA. IEEE. + + - Hill, D. L., Studholme, C., and Hawkes, D. J. (1994). Voxel + Similarity Measures for Auto-mated Image Registration. In + Proceedings of the Third Conference on Visualization in Biomedical + Computing, pages 205 { 216. SPIE. + + - Kirkpatrick, S., Gelatt, C., and Vecch Optimization by Simulated + Annealing. Science, 220(4598):671-680. + + - Jones, M. and Poggio, T. (1995). Model-based matching of line + drawings by linear combin-ations of prototypes. Proceedings of the + International Conference on Computer Vision + + - Ljung, L. and Soderstrom, T. (1983). Theory and Practice of + Recursive Identi cation. MIT Press. + + - Shannon, C. E. (1948). A mathematical theory of communication. Bell + Systems Technical Journal, 27:379-423 and 623-656. + + - Shashua, A. (1992). Geometry and Photometry in 3D Visual + Recognition. PhD thesis, M.I.T Artificial Intelligence Laboratory, + AI-TR-1401. + + - William H. Press, Brian P. Flannery, S. A. T. and Veterling, + W. T. (1992). Numerical Recipes in C: The Art of Scienti c + Computing. Cambridge University Press, Cambridge, England, second + edition edition. + +* Semi-Automated Dialogue Act Classification for Situated Social Agents in Games, Deb Roy + + Interesting attempt to learn "social scripts" related to resturant + behaviour. The authors do this by creating a game which implements a + virtual restruant, and recoding actual human players as they + interact with the game. The learn scripts from annotated + interactions and then use those scripts to label other + interactions. They don't get very good results, but their + methodology of creating a virtual world and recording + low-dimensional actions is interesting. + + - Torque 2D/3D looks like an interesting game engine. + + +* Face Recognition by Humans: Nineteen Results all Computer Vision Researchers should know, Sinha + + This is a summary of a lot of bio experiments on human face + recognition. + + - They assert again that the internal gradients/structures of a face + are more important than the edges. + + - It's amazing to me that it takes about 10 years after birth for a + human to get advanced adult-like face detection. They go through + feature based processing to a holistic based approach during this + time. + + - Finally, color is a very important cue for identifying faces. ** References - - "excellent" book on entropy (Cover & Thomas, 1991) - \ No newline at end of file + - A. Freire, K. Lee, and L. A. Symons, BThe face-inversion effect as + a deficit in the encoding of configural information: Direct + evidence,[ Perception, vol. 29, no. 2, pp. 159–170, 2000. + - M. B. Lewis, BThatcher’s children: Development and the Thatcher + illusion,[Perception, vol. 32, pp. 1415–21, 2003. + - E. McKone and N. Kanwisher, BDoes the human brain process objects + of expertise like faces? A review of the evidence,[ in From Monkey + Brain to Human Brain, S. Dehaene, J. R. Duhamel, M. Hauser, and + G. Rizzolatti, Eds. Cambridge, MA: MIT Press, 2005. + + + + +heee~eeyyyy kids, time to get eagle'd!!!! + + + + + +* Ullman + +Actual code reuse! + +precision = fraction of retrieved instances that are relevant + (true-postives/(true-positives+false-positives)) + +recall = fraction of relevant instances that are retrieved + (true-positives/total-in-class) + +cross-validation = train the model on two different sets to prevent +overfitting. + + + + + +** Getting around the dumb "fixed training set" methods + +*** 2006 Learning to classify by ongoing feature selection + + Brings in the most informative features of a class, based on + mutual information between that feature and all the examples + encountered so far. To bound the running time, he uses only a + fixed number of the most recent examples. He uses a replacement + strategy to tell whether a new feature is better than one of the + corrent features. + +*** 2009 Learning model complexity in an online environment + + Sort of like the heirichal baysean models of Tennanbaum, this + system makes the model more and more complicated as it gets more + and more training data. It does this by using two systems in + parallell and then whenever the more complex one seems to be + needed by the data, the less complex one is thrown out, and an + even more complex model is initialized in its place. + + He uses a SVM with polynominal kernels of varying complexity. He + gets good perfoemance on a handwriting classfication using a large + range of training samples, since his model changes complexity + depending on the number of training samples. The simpler models do + better with few training points, and the more complex ones do + better with many training points. + + The more complex models must be able to be initialized efficiently + from the less complex models which they replace! + + +** Non Parametric Models + +*** Visual features of intermediate complexity and their use in classification + +*** The chains model for detecting parts by their context + + Like the constelation method for rigid objects, but extended to + non-rigid objects as well. + + Allows you to build a hand detector from a face detector. This is + usefull because hands might be only a few pixels, and very + ambiguous in an image, but if you are expecting them at the end of + an arm, then they become easier to find. + + \ No newline at end of file