Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
- 2005
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
On image reconstruction from multiscale top points
Kanters, F., Lillholm, Martin, Duits, R., Janssen, B., Platel, B., Florack, L. & ter Haar Romeny, B., 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. 431-442 12 p. (Lecture notes in computer science, Vol. 3459).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- 2006
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 › peer-review
A category system on the shape index descriptor of local image structure induced by natural image statistics
Lillholm, Martin & Griffin, L. D., 2006, In: Perception. 35, Supplement, p. 48-49 2 p.Research output: Contribution to journal › Conference abstract in journal
- 2007
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
Maximum likelihood metameres for local 2nd order image structure of natural images
Lillholm, Martin & Griffin, L. D., 2007, Scale Space and Variational Methods in Computer Vision: First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings. Sgallari, F., Murli, A. & Paragios, N. (eds.). Springer, p. 394-405 12 p. (Lecture notes in computer science, Vol. 4485).Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
- 2008
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
Larger feature alphabets can improve object recognition even with simpler visual words
Lillholm, Martin & Griffin, L., 2008, In: Perception. 37, Supplement, p. 33-33 1 p.Research output: Contribution to journal › Conference abstract in journal
Novel image feature alphabets for object recognition
Lillholm, Martin & Griffin, L., 2008, 19th International Conference on Pattern Recognition, 2008: ICPR 2008.. IEEE, 4 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- 2009
- 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
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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301
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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