Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo hóa học: " Research Article A Conditional Random Field Approach to Unsupervised Texture Image 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 A Conditional Random Field Approach to Unsupervised Texture Image Segmentation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 167942 12 pages doi 10.1155 2010 167942 Research Article A Conditional Random Field Approach to Unsupervised Texture Image Segmentation Chang-Tsun Li Department of Computer Science University of Warwick Coventry CV4 7AL UK Correspondence should be addressed to Chang-Tsun Li c-t.li@warwick.ac.uk Received 6 June 2010 Revised 16 August 2010 Accepted 10 September 2010 Academic Editor Stephen Marshall Copyright 2010 Chang-Tsun Li. 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. An unsupervised multiresolution conditional random field CRF approach to texture segmentation problems is introduced. This approach involves local and long-range information in the CRF neighbourhood to determine the classes of image blocks. Like most Markov random field MRF approaches the proposed method treats the image as an array of random variables and attempts to assign an optimal class label to each. While most MRFs involve only local information extracted from a small neighbourhood our method also allows a few long-range blocks to be involved in the labelling process. This alleviates the problem of assigning different class labels to disjoint regions of the same texture and oversegmentation due to the lack of long-range interaction among the neighbouring and distant blocks. The proposed method requires no a priori knowledge of the number and types of regions textures. 1. Introduction Image segmentation is essentially the first step toward many image analysis and computer vision problems. It is usually formulated as an optimization problem in which the image in question is partitioned into a number of homogeneous regions each characterized by a unique set of features 1 . Its applications can be found in a wide variety of areas .