tailieunhanh - Báo cáo hóa học: " Correction of Misclassifications Using a Proximity-Based Estimation Method"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Correction of Misclassifications Using a Proximity-Based Estimation Method | EURASIP Journal on Applied Signal Processing 2004 8 1142-1155 2004 Hindawi Publishing Corporation Correction of Misclassifications Using a Proximity-Based Estimation Method Antti Niemisto Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Email Department of Pathology The University of Texas . Anderson Cancer Center 1515 Holcombe Boulevard Houston TX 77030 USA Ilya Shmulevich Department of Pathology The University of Texas . Anderson Cancer Center 1515 Holcombe Boulevard Houston TX 77030 USA Email is@ Vladimir V. Lukin Department 504 National Aerospace University 17 Chkalova Street 61070 Kharkov Ukraine Email lukin@ Alexander N. Dolia Department 504 National Aerospace University 17 Chkalova Street 61070 Kharkov Ukraine School of Electronics and Computer Science University of Southampton Southampton SO171BJ England UK Email ad@ Olli Yli-Harja Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Email Received 14 October 2003 Revised 17 December 2003 Recommended for Publication by John Sorensen An estimation method for correcting misclassifications in signal and image processing is presented. The method is based on the use of context-based temporal or spatial information in a sliding-window fashion. The classes can be purely nominal that is an ordering of the classes is not required. The method employs nonlinear operations based on class proximities defined by a proximity matrix. Two case studies are presented. In the first the proposed method is applied to one-dimensional signals for processing data that are obtained by a musical key-finding algorithm. In the second the estimation method is applied to two-dimensional signals for correction of misclassifications in images. In the first case study the proximity matrix employed by the estimation method follows directly from music perception