Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
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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
- Published
Prior knowledge regularization in statistical medical image tasks
Crimi, A., Sporring, Jon, de Bruijne, Marleen, Lillholm, Martin & Nielsen, Mads, 2009, Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. 12 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › 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
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 › Research › peer-review
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 › Research › peer-review
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578
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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
Research output: Contribution to journal › Journal article › Research › peer-review
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309
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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 › Research › peer-review
Published