Burget, R., Uher, V., Masek, J., Trainable Segmentation Based on Local-level and Segment-level Feature Extraction, 2012 IEEE Interna-tional Symposium on Biomedical Imaging: From nano to Macro, 2012

has been cited by the following article:

• TITLE: Biology Inspired Image Segmentation using Methods of Artificial Intelligence

  AUTHORS: Radim Burget, Vaclav Uher, Jan Masek

  KEYWORDS: Image processing, Image mining, Feature extraction, Data mining

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 25, 2013

  ABSTRACT: In recent years,many efforts have been devoted to image segmentation. Although for a man general image segmentation is considered an easy task, for computers it is still considered to be difficult, computationally intensive and still unresolved task. This work presents an innovative algorithm combining theory of artificial intelligence and knowledge of human eye anatomy. The resulting algorithm has not ambitions to be universal like human brain but can be trained and perform on selected domain. The effectiveness of the algorithm is demonstrated on the selected examples.