Louis G. Vuurpijl
Affiliations: | Radboud University Nijmegen, Nijmegen, Gelderland, Netherlands |
Area:
Pen Computing, Man-Machine Interaction, Neural NetworksWebsite:
http://hwr.nici.kun.nl/~vuurpijl/Google:
"Louis Vuurpijl"Bio:
http://hdl.handle.net/2066/18657
Mean distance: 19.52 (cluster 23) | S | N | B | C | P |
Parents
Sign in to add mentorTheo E. Schouten | grad student | 1998 | Radboud University Nijmegen | |
Jan Vytopil | grad student | 1998 | Radboud University Nijmegen (MathTree) | |
(Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems) |
Children
Sign in to add traineeEgon L. van den Broek | grad student | 2001-2005 | Radboud University Nijmegen |
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Publications
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Van Den Broek EL, Kok T, Schouten TE, et al. (2008) Human-centered content-based image retrieval Proceedings of Spie - the International Society For Optical Engineering. 6806 |
Van Den Broek EL, Kisters PMF, Vuurpijl LG. (2005) Content-based image retrieval benchmarking: Utilizing color categories and color distributions Journal of Imaging Science and Technology. 49: 293-301 |
Van Den Broek EL, Kisters PMF, Vuurpijl LG. (2004) Design guidelines for a content-based image retrieval color-selection interface Acm International Conference Proceeding Series. 65 |
Van Den Broek EL, Kisters PMF, Vuurpijl LG. (2004) The utilization of human color categorization for content-based image retrieval Proceedings of Spie - the International Society For Optical Engineering. 5292: 351-362 |
Vuurpijl L, Schomaker L, Van Den Broek E. (2002) Vind(x): Using the user through cooperative annotation Proceedings - International Workshop On Frontiers in Handwriting Recognition, Iwfhr. 221-226 |
Schomaker L, de Leau E, Vuurpijl L. (1999) Using pen-based outlines for object-based annotation and image-based queries Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1614: 585-592 |
Vuurpijl L, Schouten T, Vytopil J. (1995) Performance prediction of large MIMD systems for parallel neural network simulations Future Generation Computer Systems. 11: 221-232 |