Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
Classifying local image symmetry using a co-localised family of linear filters
Griffin, L. D. & Lillholm, Martin, 2008, In: Perception. 37, Supplement, p. 122-122 1 p.Research output: Contribution to journal › Conference abstract in journal › Research
Mode estimation using pessimistic scale space tracking
Griffin, L. D. & Lillholm, Martin, 2003, Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings. Griffin, L. D. & Lillholm, M. (eds.). Springer, p. 266-280 15 p. (Lecture notes in computer science, Vol. 2695).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Hypotheses for image features, icons and textons
Griffin, L. D. & Lillholm, Martin, 2006, In: International Journal of Computer Vision. 70, 3, p. 213-230 18 p.Research output: Contribution to journal › Journal article › Research › peer-review
Feature category systems for 2nd order local image structure induced by natural image statistics and otherwise
Griffin, L. D. & Lillholm, Martin, 2007, Human Vision and Electronic Imaging XII . Rogowitz, B. E., Pappas, T. N. & Daly, S. J. (eds.). SPIE - International Society for Optical Engineering, 11 p. 649209. (Proceedings of S P I E - International Society for Optical Engineering, Vol. 6492).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Image features and the 1-D, 2nd order gaussian derivative jet
Griffin, L. D. & Lillholm, Martin, 2005, Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005, Hofgeismar, Germany, April 7-9, 2005. Proceedings. Kimmel, R., Sochen, N. A. & Weickert, J. (eds.). Springer, p. 26-37 12 p. (Lecture notes in computer science, Vol. 3459).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Scale Space Methods in Computer Vision: 4th International Conference, Scale-Space 2003, Isle of Skye, UK, June 10-12, 2003, Proceedings
Griffin, L. D. (ed.) & Lillholm, Martin (ed.), 2003, Springer. (Lecture notes in computer science, Vol. 2695).Research output: Book/Report › Book › Research › peer-review
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 › Research › peer-review
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
Natural image profiles are most likely to be step edges
Griffin, L. D., Lillholm, Martin & Nielsen, Mads, 2004, In: Vision Research. 44, 4, p. 407-421 15 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Jimenez-Solem, E., Petersen, T. S., Hansen, C., Hansen, C., Lioma, C., Igel, C., Boomsma, W., Krause, O., Lorenzen, S., Selvan, R., Petersen, J., Nyeland, M. E., Ankarfeldt, M. Z., Virenfeldt, G. M., Winther-Jensen, M., Linneberg, A., Ghazi, M. M., Detlefsen, N., Lauritzen, A. D., Smith, A. G. & 15 others, , 2021, In: Scientific Reports. 11, 1, 12 p., 3246.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
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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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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
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Published