Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
- 2009
Basic image features (BIFs) arising from approximate symmetry type
Griffin, L. D., Lillholm, Martin, Crosier, M. & van Sande, J., 2009, Scale Space and Variational Methods in Computer Vision: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings. Tai, X-C., Mørken, K., Lysaker, M. & Lie, K-A. (eds.). Springer, p. 343-355 13 p. (Lecture notes in computer science, Vol. 5567).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Statistics and category systems for the shape index descriptor of local 2nd order natural image structure
Lillholm, Martin & Griffin, L. D., 2009, In: Image and Vision Computing. 27, 6, p. 771-781 11 p.Research output: Contribution to journal › Journal article › peer-review
Single stroke gaze gestures
Mollenbach, E., Hansen, J. P., Lillholm, Martin & Gale, A., 2009, CHI '09 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, p. 4555-4560 6 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- 2010
- Published
Two dimensional shape representation manipulation method for improving general procrustes alignment process, involves relating probable relative depth of landmark in three dimensional shape of body part
Chernoff, K., Nielsen, Mads & Lillholm, Martin, 2010, IPC No. G06T-007/00, Patent No. WO2010142595-A1, 16 Dec 2010, Priority date 11 Jun 2009, Priority No. US268370Research output: Patent
- Published
Method for identifying region of interest (ROI) in human organ for performing e.g. knee cartilage quantification, involves calculating weight of feature of image in map for minimizing sample size needed to discriminate between groups
Dam, E. B., Nielsen, Mads, Qazi, A. A., Lillholm, Martin & Jørgensen, D. R., 2010, IPC No. G06K-009/00, Patent No. US2010232671-A1, 16 Sep 2010, Priority date 17 Dec 2008, Priority No. US203094PResearch output: Patent
Symmetry sensitivities of derivative-of-gaussian filters
Griffin, L. D. & Lillholm, Martin, 2010, In: I E E E Transactions on Pattern Analysis and Machine Intelligence. 32, 6, p. 1072-1083 12 p.Research output: Contribution to journal › Journal article › peer-review
Single gaze gestures
Møllenbach, E., Lillholm, Martin, Gail, A. & Hansen, J. P., 2010, Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. Association for Computing Machinery, p. 177-180 4 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
A framework for optimizing measurement weight maps to minimize the required sample size
Qazi, A. A., Jørgensen, D. R., Lillholm, Martin, Loog, M., Nielsen, Mads & Dam, Erik Bjørnager, 2010, In: Medical Image Analysis. 14, 3, p. 255-264 10 p.Research output: Contribution to journal › Journal article › peer-review
- 2011
- Published
Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling
Crimi, A., Lillholm, Martin, Nielsen, Mads, Ghosh, A., de Bruijne, Marleen, Dam, E. B. & Sporring, Jon, 2011, In: IEEE Transactions on Medical Imaging. 30, 8, p. 1514-1526 13 p.Research output: Contribution to journal › Journal article › peer-review
Method for analyzing magnetic resonance imaging (MRI) image of bone to identify e.g. osteoarthritis, involves combining features of textural information within region of interest (ROI) to estimate level of disease
Dam, E. B., Granlund, R. L. & Lillholm, Martin, 2011, IPC No. G06T-007/00, Patent No. WO2011151242-A1, 8 Dec 2011, Priority date 1 Jun 2010, Priority No. GB009101Research output: Patent
ID: 152298477
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Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › peer-review
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571
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Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study
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301
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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Research output: Contribution to journal › Journal article › peer-review
Published