tailieunhanh - Báo cáo hóa học: " Research Article Heterogeneous Stacking for Classification-Driven Watershed Segmentation"

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: Research Article Heterogeneous Stacking for Classification-Driven Watershed Segmentation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 485821 9 pages doi 2008 485821 Research Article Heterogeneous Stacking for Classification-Driven Watershed Segmentation Ilya Levner Hong Zhang and Russell Greiner Department of Computing Science University of Alberta Edmonton Alberta Canada T6G 2E8 Correspondence should be addressed to Ilya Levner ilya@ Received 30 September 2007 Accepted 19 January 2008 Recommended by Sébastien Lefevre Marker-driven watershed segmentation attempts to extract seeds that indicate the presence of objects within an image. These markers are subsequently used to enforce regional minima within a topological surface used by the watershed algorithm. The classification-driven watershed segmentation CDWS algorithm improved the production of markers and topological surface by employing two machine-learned pixel classifiers. The probability maps produced by the two classifiers were utilized for creating markers object boundaries and the topological surface. This paper extends the CDWS algorithm by i enabling automated feature extraction via independent components analysis and ii improving the segmentation accuracy by introducing heterogeneous stacking. Heterogeneous stacking an extension of stacked generalization for object delineation improves pixel labeling and segmentation by training base classifiers on multiple target concepts extracted from the original ground truth which are subsequently fused by the second set of classifiers. Experimental results demonstrate the effectiveness of the proposed system on real world images and indicate significant improvement in segmentation quality over the base system. Copyright 2008 Ilya Levner et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Pixel