报告时间：星期六（2015.10.31）9：30am
报告地点：电子信息楼308
报告人：Ivica Kopriva, Professor, Ru?er Boškovi? Institute, Croatia
邀请人：陈新建 特聘教授
Abstract: Blind (unsupervised) signal separation (BSS) is one of the fundamental problems in signal/information processing. Thereby, underdetermined nonlinear blind separation of correlated signals is especially challenging. Solution of nonlinear BSS problem is of practical importance, among others, in decomposition (segmentation) of multichannel image composed of objects with highly similar spectra or densities. We shall present method for nonlinear BSS problem in Hilbert kernel spaces (Kopriva et al, J. Chemometrics 28, 704-715, 2014; J. Chemometrics 27, 189-197, 2013). The method will be demonstrated on segmentation of RGB microscopic images of unstained specimens in histopathology (Kopriva et al, Scientific Reports 5: 11576, DOI: 10.1038/srep11576).
Biography: Ivica Kopriva obtained PhD degree from the Faculty of Electrical Engineering and Computing, University of Zagreb in 1998 with a subject in blind source separation. From 2001 till 2005 he was research and senior research scientist at Department of Electrical and Computer Engineering, The George Washington University, Washington D.C., USA. Since 2006 he is senior scientist at the Ru?er Boškovi? Institute, Zagreb, Croatia. His research interests are related to development of algorithms for unsupervised learning with applications in biomedical image analysis, chemometrics and bioinformatics. He published over 40 papers in internationally recognized journals and hold 3 US patents. He is co-author of the research monograph: Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised and Unsupervised Learning, Springer Series: Studies in Computational Intelligence, 2006. He is senior member of the IEEE and the OSA.