13 June 1995 Principle of least commitment in the analysis of chromosome images
Author Affiliations +
The automation of chromosome identification and visualization for a complete cell (karyotyping) has been the subject of considerable research. While rather high classification rates are possible on individual chromosomes, the cell level classification rates are still quite low. We describe a system which uses partial confidence values generated by neural and fuzzy classifiers with optimization to increase the cell level recognition rates. This is consistent with Marr's Principle of Least Commitment for the design of intelligent computer vision algorithms.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James M. Keller, James M. Keller, Paul D. Gader, Paul D. Gader, Charles W. Caldwell, Charles W. Caldwell, } "Principle of least commitment in the analysis of chromosome images", Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211800; https://doi.org/10.1117/12.211800


Towards relative gradient and its applications
Proceedings of SPIE (March 03 2015)
Deep learning in the small sample size setting ...
Proceedings of SPIE (March 23 2016)
Color image segmentation: a review
Proceedings of SPIE (February 26 2010)
Recognition of 2-D shapes using set erosion
Proceedings of SPIE (January 31 1992)

Back to Top