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Journal Article
Vision Research, ISSN 0042-6989, 2000, Volume 40, Issue 9, pp. 1143 - 1155
Journal Article
British Machine Vision Conference, BMVC 2009 - Proceedings, 2009
Conference Proceeding
Proceedings of the Royal Society of London. Series B: Biological Sciences, ISSN 0962-8452, 07/1999, Volume 266, Issue 1426, pp. 1361 - 1365
Journal Article
Personal and Ubiquitous Computing, ISSN 1617-4909, 10/2003, Volume 7, Issue 5, pp. 287 - 298
Despite the increasing sophistication of augmented reality (AR) tracking technology, tracking in unprepared environments still remains an enormous challenge... 
ComputerScience | Homography | Fundamental matrix | Vision based tracking | Augmented reality | Optical flow | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | Algorithms
Journal Article
Vision Research, ISSN 0042-6989, 2000, Volume 40, Issue 8, pp. 913 - 924
Journal Article
Vision Research, ISSN 0042-6989, 1998, Volume 38, Issue 17, pp. 2533 - 2537
Disparity discrimination thresholds are known to increase with both retinal eccentricity and distance from the horopter. However, little is known about how the... 
Eccentricity | Stereopsis | Acuity | PERCEPTION | stereopsis | acuity | SPATIAL-FREQUENCY | eccentricity | OPHTHALMOLOGY | CORTICAL MAGNIFICATION FACTOR | CHANNELS | DEPTH | NEUROSCIENCES | PSYCHOLOGY | SURFACES | Retina | Vision Disparity | Visual Fields | Depth Perception | Humans
Journal Article
NeuroImage, ISSN 1053-8119, 03/2017, Volume 148, pp. 77 - 102
Journal Article
11/2002, Volume 22, Issue 6, 7
To realistically integrate 3D graphics into an unprepared environment, camera position must be estimated by tracking natural image features. We apply our... 
Transmission line matrix methods | Motion estimation | Layout | Video sequences | Prototypes | Virtual reality | Cameras | Robustness | Application software | Augmented reality | COMPUTER SCIENCE, SOFTWARE ENGINEERING | Motion | Machine vision | Image processing | Three-dimensional graphics | Computer graphics | Research | Tracking | Images | Outdoor | Transformations | Three dimensional
Magazine Article
British Machine Vision Conference, BMVC 2010 - Proceedings, 2010
Conference Proceeding
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2015, Volume 7-12-, pp. 4156 - 4164
Subspace models have been very successful at modeling the appearance of structured image datasets when the visual objects have been aligned in the images... 
Context | Visualization | Analytical models | Image color analysis | Mathematical model | Active appearance model | Context modeling | Occlusion | Computer vision | Alignment | Bananas | Images | Pattern recognition | Subspaces | Visual
Conference Proceeding
BMVC 2006 - Proceedings of the British Machine Vision Conference 2006, 2006, pp. 889 - 898
Conference Proceeding
by Way, Gregory P and Sanchez-Vega, Francisco and Armenia, Joshua and Chatila, Walid K and Luna, Augustin and Cherniack, Andrew D and Mina, Marco and Demchok, John A and Felau, Ina and Kasapi, Melpomeni and Hutter, Carolyn M and Sofia, Heidi J and Tarnuzzer, Roy and Yang, Liming and Zenklusen, Jean C and Chudamani, Sudha and Naresh, Rashi and Pihl, Todd and Sun, Qiang and Wan, Yunhu and Wu, Ye and Cho, Juok and DeFreitas, Timothy and Heiman, David I and Lawrence, Michael S and Lin, Pei and Meier, Sam and Noble, Michael S and Saksena, Gordon and Voet, Doug and Zhang, Hailei and Bernard, Brady and Chambwe, Nyasha and Dhankani, Varsha and Knijnenburg, Theo and Kramer, Roger and Leinonen, Kalle and Miller, Michael and Shmulevich, Ilya and Thorsson, Vesteinn and Zhang, Wei and Hegde, Apurva M and Ju, Zhenlin and Korkut, Anil and Li, Jun and Liang, Han and Ling, Shiyun and Liu, Wenbin and Lu, Yiling and Mills, Gordon B and Ng, Kwok-Shing and Abeshouse, Adam and Chakravarty, Debyani and Chatila, Walid K and de Bruijn, Ino and Gao, Jianjiong and Heins, Zachary J and Kundra, Ritika and Ladanyi, Marc and Luna, Augustin and Ochoa, Angelica and Phillips, Sarah M and Sanchez-Vega, Francisco and Sander, Chris and Schultz, Nikolaus and Sumer, S. Onur and Taylor, Barry S and Wang, Jioajiao and Zhang, Hongxin and Anur, Pavana and Peto, Myron and Spellman, Paul and Benz, Christopher and Stuart, Joshua M and Wong, Christopher K and Yau, Christina and Hayes, D. Neil and Wilkerson, Matthew D and Ally, Adrian and Brooks, Denise and Carlsen, Rebecca and Dhalla, Noreen and Holt, Robert and Jones, Steven J.M and Kasaian, Katayoon and Lee, Darlene and Ma, Yussanne and Marra, Marco A and Mayo, Michael and Mungall, Andrew J and Robertson, A. Gordon and Schein, Jacqueline E and Sipahimalani, Payal and Tse, Kane and Wong, Tina and Berger, Ashton C and Beroukhim, Rameen and Gabriel, Stacey B and Meyerson, Matthew and Schumacher, Steven E and ... and The Cancer Genome Atlas Research Network and Canc Genome Atlas Res Network
Cell Reports, ISSN 2211-1247, 04/2018, Volume 23, Issue 1, pp. 172 - 180.e3
Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these... 
NRAS | drug sensitivity | pan-cancer | Ras | NF1 | KRAS | Gene expression | machine learning | HRAS | TCGA | PATHOGENESIS | PROTEIN | SIGNATURES | GENE | PRECISION ONCOLOGY | PHASE-II | SELUMETINIB |