Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
- 2005
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
- 2004
Brownian Images: a generic background model
Steenstrup Pedersen, Kim & Lillholm, Martin, 2004, Proceedings of the ECCV'04 Workshop on Statistical Learning in Computer Vision.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Jet based feature classification
Lillholm, Martin & Steenstrup Pedersen, Kim, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004: ICPR 2004. IEEE, p. 787-790 4 p.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
On spatial metamerism in computational vision
Lillholm, Martin, 2004, IT-Universitetet i København. 113 p.Research output: Book/Report › Ph.D. thesis › Research
- 2003
Feature-based image analysis
Lillholm, Martin, Nielsen, Mads & Griffin, L. D., 2003, In: International Journal of Computer Vision. 52, 2, p. 73-95 23 p.Research output: Contribution to journal › Journal article › Research › peer-review
Gaussian scale space from insufficient image information
Loog, M., Lillholm, Martin, Nielsen, M. & Viergever, M. A., 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. 757-769 (Lecture notes in computer science, Vol. 2695/2003).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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
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
- 2001
What do features tell about images?
Nielsen, Mads & Lillholm, Martin, 2001, Scale-Space and Morphology in Computer Vision: Third International Conference, Scale-Space 2001 Vancouver, Canada, July 7–8, 2001 Proceedings. Kerckhove, M. (ed.). Springer, p. 39-50 12 p. (Lecture notes in computer science, Vol. 2106).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
ID: 152298477
Most downloads
-
1632
downloads
Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
635
downloads
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
Published -
348
downloads
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