Mercurial > cortex
comparison org/literature-review.org @ 376:057d47fc4789
reviewing ullman's stuff.
author | Robert McIntyre <rlm@mit.edu> |
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date | Thu, 11 Apr 2013 05:40:23 +0000 |
parents | 9c37a55e1cd2 |
children | 80cd096682b2 |
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40 partially occluded objects,” IEEE Trans. on Pattern Analysis and | 40 partially occluded objects,” IEEE Trans. on Pattern Analysis and |
41 Machine Intelligence, 19, 9 (1997), pp. 1043–48. | 41 Machine Intelligence, 19, 9 (1997), pp. 1043–48. |
42 | 42 |
43 - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust | 43 - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust |
44 technique for matching two uncalibrated images through the recovery | 44 technique for matching two uncalibrated images through the recovery |
45 of the unknown epipolar geometry,” Artificial In- telligence, 78, | 45 of the unknown epipolar geometry,” Artificial Intelligence, 78, |
46 (1995), pp. 87-119. | 46 (1995), pp. 87-119. |
47 | 47 |
48 | 48 |
49 | 49 |
50 | 50 |
51 | 51 |
52 * Alignment by Maximization of Mutual Information, Paul A. Viola | 52 * Alignment by Maximization of Mutual Information, Paul A. Viola |
53 | 53 |
54 PhD Thesis recommended by Winston. Describes a system that is able | 54 PhD Thesis recommended by Winston. Describes a system that is able |
55 to align a 3D computer model of an object with an image of that | 55 to align a 3D computer model of an object with an image of that |
56 object. | 56 object. |
61 which is worth reading, especially the part about entropy. | 61 which is worth reading, especially the part about entropy. |
62 | 62 |
63 - Differential entropy seems a bit odd -- you would think that it | 63 - Differential entropy seems a bit odd -- you would think that it |
64 should be the same as normal entropy for a discrete distrubition | 64 should be the same as normal entropy for a discrete distrubition |
65 embedded in continuous space. How do you measure the entropy of a | 65 embedded in continuous space. How do you measure the entropy of a |
66 half continuous, half discrete random variable? | 66 half continuous, half discrete random variable? Perhaps the |
67 problem is related to the delta function, and not the definition | |
68 of differential entropy? | |
67 | 69 |
68 - Expectation Maximation (Mixture of Gaussians cool stuff) | 70 - Expectation Maximation (Mixture of Gaussians cool stuff) |
69 (Dempster 1977) | 71 (Dempster 1977) |
70 | 72 |
71 - Good introduction to Parzen Window Density Estimation. Parzen | 73 - Good introduction to Parzen Window Density Estimation. Parzen |
72 density functions trade construction time for evaulation | 74 density functions trade construction time for evaulation |
73 time.(Pg. 41) | 75 time.(Pg. 41) They are a way to transform a sample into a |
76 distribution. They don't work very well in higher dimensions due | |
77 to the thinning of sample points. | |
78 | |
79 - Calculating the entropy of a Markov Model (or state machine, | |
80 program, etc) seems like it would be very hard, since each trial | |
81 would not be independent of the other trials. Yet, there are many | |
82 common sense models that do need to have state to accurately model | |
83 the world. | |
84 | |
85 - "... there is no direct procedure for evaluating entropy from a | |
86 sample. A common approach is to model the density from the sample, | |
87 and then estimate the entropy from the density." | |
88 | |
89 - pg. 55 he says that infinity minus infinity is zero lol. | |
90 | |
91 - great idea on pg 62 about using random samples from images to | |
92 speed up computation. | |
93 | |
94 - practical way of terminating a random search: "A better idea is to | |
95 reduce the learning rate until the parameters have a reasonable | |
96 variance and then take the average parameters." | |
97 | |
98 - p. 65 bullshit hack to make his parzen window estimates work. | |
99 | |
100 - this alignment only works if the initial pose is not very far | |
101 off. | |
102 | |
74 | 103 |
75 Occlusion? Seems a bit holistic. | 104 Occlusion? Seems a bit holistic. |
76 | 105 |
77 | |
78 ** References | 106 ** References |
79 - "excellent" book on entropy (Cover & Thomas, 1991) | 107 - "excellent" book on entropy (Cover & Thomas, 1991) [Elements of |
80 | 108 Information Theory.] |
109 | |
110 - Canny, J. (1986). A Computational Approach to Edge Detection. IEEE | |
111 Transactions PAMI, PAMI-8(6):679{698 | |
112 | |
113 - Chin, R. and Dyer, C. (1986). Model-Based Recognition in Robot | |
114 Vision. Computing Surveys, 18:67-108. | |
115 | |
116 - Grimson, W., Lozano-Perez, T., Wells, W., et al. (1994). An | |
117 Automatic Registration Method for Frameless Stereotaxy, Image | |
118 Guided Surgery, and Enhanced Realigy Visualization. In Proceedings | |
119 of the Computer Society Conference on Computer Vision and Pattern | |
120 Recognition, Seattle, WA. IEEE. | |
121 | |
122 - Hill, D. L., Studholme, C., and Hawkes, D. J. (1994). Voxel | |
123 Similarity Measures for Auto-mated Image Registration. In | |
124 Proceedings of the Third Conference on Visualization in Biomedical | |
125 Computing, pages 205 { 216. SPIE. | |
126 | |
127 - Kirkpatrick, S., Gelatt, C., and Vecch Optimization by Simulated | |
128 Annealing. Science, 220(4598):671-680. | |
129 | |
130 - Jones, M. and Poggio, T. (1995). Model-based matching of line | |
131 drawings by linear combin-ations of prototypes. Proceedings of the | |
132 International Conference on Computer Vision | |
133 | |
134 - Ljung, L. and Soderstrom, T. (1983). Theory and Practice of | |
135 Recursive Identi cation. MIT Press. | |
136 | |
137 - Shannon, C. E. (1948). A mathematical theory of communication. Bell | |
138 Systems Technical Journal, 27:379-423 and 623-656. | |
139 | |
140 - Shashua, A. (1992). Geometry and Photometry in 3D Visual | |
141 Recognition. PhD thesis, M.I.T Artificial Intelligence Laboratory, | |
142 AI-TR-1401. | |
143 | |
144 - William H. Press, Brian P. Flannery, S. A. T. and Veterling, | |
145 W. T. (1992). Numerical Recipes in C: The Art of Scienti c | |
146 Computing. Cambridge University Press, Cambridge, England, second | |
147 edition edition. | |
148 | |
149 * Semi-Automated Dialogue Act Classification for Situated Social Agents in Games, Deb Roy | |
150 | |
151 Interesting attempt to learn "social scripts" related to resturant | |
152 behaviour. The authors do this by creating a game which implements a | |
153 virtual restruant, and recoding actual human players as they | |
154 interact with the game. The learn scripts from annotated | |
155 interactions and then use those scripts to label other | |
156 interactions. They don't get very good results, but their | |
157 methodology of creating a virtual world and recording | |
158 low-dimensional actions is interesting. | |
159 | |
160 - Torque 2D/3D looks like an interesting game engine. | |
161 | |
162 | |
163 * Face Recognition by Humans: Nineteen Results all Computer Vision Researchers should know, Sinha | |
164 | |
165 This is a summary of a lot of bio experiments on human face | |
166 recognition. | |
167 | |
168 - They assert again that the internal gradients/structures of a face | |
169 are more important than the edges. | |
170 | |
171 - It's amazing to me that it takes about 10 years after birth for a | |
172 human to get advanced adult-like face detection. They go through | |
173 feature based processing to a holistic based approach during this | |
174 time. | |
175 | |
176 - Finally, color is a very important cue for identifying faces. | |
177 | |
178 ** References | |
179 - A. Freire, K. Lee, and L. A. Symons, BThe face-inversion effect as | |
180 a deficit in the encoding of configural information: Direct | |
181 evidence,[ Perception, vol. 29, no. 2, pp. 159–170, 2000. | |
182 - M. B. Lewis, BThatcher’s children: Development and the Thatcher | |
183 illusion,[Perception, vol. 32, pp. 1415–21, 2003. | |
184 - E. McKone and N. Kanwisher, BDoes the human brain process objects | |
185 of expertise like faces? A review of the evidence,[ in From Monkey | |
186 Brain to Human Brain, S. Dehaene, J. R. Duhamel, M. Hauser, and | |
187 G. Rizzolatti, Eds. Cambridge, MA: MIT Press, 2005. | |
188 | |
189 | |
190 | |
191 | |
192 heee~eeyyyy kids, time to get eagle'd!!!! | |
193 | |
194 | |
195 | |
196 | |
197 | |
198 * Ullman | |
199 | |
200 Actual code reuse! | |
201 | |
202 precision = fraction of retrieved instances that are relevant | |
203 (true-postives/(true-positives+false-positives)) | |
204 | |
205 recall = fraction of relevant instances that are retrieved | |
206 (true-positives/total-in-class) | |
207 | |
208 cross-validation = train the model on two different sets to prevent | |
209 overfitting. | |
210 | |
211 | |
212 | |
213 | |
214 | |
215 ** Getting around the dumb "fixed training set" methods | |
216 | |
217 *** 2006 Learning to classify by ongoing feature selection | |
218 | |
219 Brings in the most informative features of a class, based on | |
220 mutual information between that feature and all the examples | |
221 encountered so far. To bound the running time, he uses only a | |
222 fixed number of the most recent examples. He uses a replacement | |
223 strategy to tell whether a new feature is better than one of the | |
224 corrent features. | |
225 | |
226 *** 2009 Learning model complexity in an online environment | |
227 | |
228 Sort of like the heirichal baysean models of Tennanbaum, this | |
229 system makes the model more and more complicated as it gets more | |
230 and more training data. It does this by using two systems in | |
231 parallell and then whenever the more complex one seems to be | |
232 needed by the data, the less complex one is thrown out, and an | |
233 even more complex model is initialized in its place. | |
234 | |
235 He uses a SVM with polynominal kernels of varying complexity. He | |
236 gets good perfoemance on a handwriting classfication using a large | |
237 range of training samples, since his model changes complexity | |
238 depending on the number of training samples. The simpler models do | |
239 better with few training points, and the more complex ones do | |
240 better with many training points. | |
241 | |
242 The more complex models must be able to be initialized efficiently | |
243 from the less complex models which they replace! | |
244 | |
245 | |
246 ** Non Parametric Models | |
247 | |
248 *** Visual features of intermediate complexity and their use in classification | |
249 | |
250 *** The chains model for detecting parts by their context | |
251 | |
252 Like the constelation method for rigid objects, but extended to | |
253 non-rigid objects as well. | |
254 | |
255 Allows you to build a hand detector from a face detector. This is | |
256 usefull because hands might be only a few pixels, and very | |
257 ambiguous in an image, but if you are expecting them at the end of | |
258 an arm, then they become easier to find. | |
259 | |
260 |