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- LS-EPI: 1647
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ltx200: ½ð±Ò+1, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2014-03-08 16:35:20
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2Â¥2014-03-08 16:23:58
baiyuefei
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- LS-EPI: 1647
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- ¹ó±ö: 46.969
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- ºì»¨: 995
- ɳ·¢: 81
- Ìû×Ó: 69424
- ÔÚÏß: 13328.4Сʱ
- ³æºÅ: 676696
- ×¢²á: 2008-12-18
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ltx200: ½ð±Ò+3, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2014-03-08 16:35:10
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gruyclewee: ½ð±Ò+3, ¸ÐлӦÖú£¬½±ÀøÒ»Ï¡£ 2014-03-08 21:11:37
oven1986: ¼ìË÷EPI+1, ¸ÐлӦÖú£¬¹ÄÀøÒ»Ï¡£ 2014-03-09 23:38:44
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Image segmentation using adaptive loopy belief propagation ×÷Õß:Xu, SJ (Xu, Sheng-Jun)[ 1,2 ] ; Han, JQ (Han, Jiu-Qiang)[ 1 ] ; Yu, JQ (Yu, Jun-Qi)[ 2 ] ; Zhao, L (Zhao, Liang)[ 2 ] OPTIK ¾í: 124 ÆÚ: 22 Ò³: 5732-5738 DOI: 10.1016/j.ijleo.2013.04.013 ³ö°æÄê: 2013 ²é¿´ÆÚ¿¯ÐÅÏ¢ ÕªÒª Loopy belief propagation (LBP) algorithm over pairwise-connected Markov random fields (MRFs) has become widely used for low-level vision problems. However, Pairwise MRF is often insufficient to capture the statistics of natural images well, and LBP is still extremely slow for application on an MRF with large discrete label space. To solve these problems, the present study proposes a new segmentation algorithm based on adaptive LBP. The proposed algorithm utilizes local region information to construct a local region model, as well as a local interaction region MRF model for image segmentation. The adaptive LBP algorithm maximizes the global probability of the proposed MRF model, which employs two very important strategies, namely, "message self-convergence" and "adaptive label pruning". Message self-convergence can improve the reliability of a pixel in choosing a label in local region, and label pruning can dismiss impossible labels for every pixel. Thus, the most reliable information messages transfer through the LBP algorithm. The experimental results show that the proposed algorithm not only obtains more accurate segmentation results but also greater speed. (C) 2013 Elsevier GmbH. All rights reserved. ¹Ø¼ü´Ê ×÷Õ߹ؼü´Ê:Adaptive loopy belief propagation; Markov random fields; Local interaction region MRF; Message self-convergence; Label pruning KeyWords Plus:MARKOV RANDOM-FIELDS; ENERGY MINIMIZATION; GRAPH CUTS; VISION ×÷ÕßÐÅÏ¢ ͨѶ×÷ÕßµØÖ·: Xu, SJ (ͨѶ×÷Õß) Xi An Jiao Tong Univ, MoE Key Lab Intelligent Networks & Network Secur, Xian 710049, Peoples R China. µØÖ·: [ 1 ] Xi An Jiao Tong Univ, MoE Key Lab Intelligent Networks & Network Secur, Xian 710049, Peoples R China [ 2 ] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian 710055, Peoples R China µç×ÓÓʼþµØÖ·:duplin@sina.com »ù½ð×ÊÖúÖÂл »ù½ð×ÊÖú»ú¹¹ ÊÚȨºÅ National Natural Science Foundation for Young Scientists of China 51209167 Natural Science Foundation of Shaanxi Province, China 2012JM8026 ²é¿´»ù½ð×ÊÖúÐÅÏ¢ ³ö°æÉÌ ELSEVIER GMBH, URBAN & FISCHER VERLAG, OFFICE JENA, P O BOX 100537, 07705 JENA, GERMANY Àà±ð / ·ÖÀà Ñо¿·½Ïò:Optics Web of Science Àà±ð:Optics ÎÄÏ×ÐÅÏ¢ ÎÄÏ×ÀàÐÍ:Article ÓïÖÖ:English Èë²ØºÅ: WOS:000325835400098 ISSN: 0030-4026 ÆÚ¿¯ÐÅÏ¢ Impact Factor (Ó°ÏìÒò×Ó): Journal Citation Reports® ÆäËûÐÅÏ¢ IDS ºÅ: 236YM Web of Science ºËÐĺϼ¯ÖÐµÄ "ÒýÓõIJο¼ÎÄÏ×": 27 Web of Science ºËÐĺϼ¯ÖÐµÄ "±»ÒýƵ´Î": |
3Â¥2014-03-08 16:24:17
baiyuefei
°æÖ÷ (ÎÄѧ̩¶·)
·çÑ©
- LS-EPI: 1647
- Ó¦Öú: 4642 (¸±½ÌÊÚ)
- ¹ó±ö: 46.969
- ½ð±Ò: 658104
- É¢½ð: 11616
- ºì»¨: 995
- ɳ·¢: 81
- Ìû×Ó: 69424
- ÔÚÏß: 13328.4Сʱ
- ³æºÅ: 676696
- ×¢²á: 2008-12-18
- ÐÔ±ð: GG
- רҵ: ºÏ³ÉÒ©Îﻯѧ
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4Â¥2014-03-08 16:24:36
ltx200
ľ³æ (ÕýʽдÊÖ)
- Ó¦Öú: 8 (Ó×¶ùÔ°)
- ½ð±Ò: 4004.5
- É¢½ð: 1515
- Ìû×Ó: 499
- ÔÚÏß: 261.3Сʱ
- ³æºÅ: 972997
- ×¢²á: 2010-03-16
- רҵ: ģʽʶ±ð
5Â¥2014-03-08 16:28:31
ltx200
ľ³æ (ÕýʽдÊÖ)
- Ó¦Öú: 8 (Ó×¶ùÔ°)
- ½ð±Ò: 4004.5
- É¢½ð: 1515
- Ìû×Ó: 499
- ÔÚÏß: 261.3Сʱ
- ³æºÅ: 972997
- ×¢²á: 2010-03-16
- רҵ: ģʽʶ±ð
6Â¥2014-03-08 16:32:05
ltx200
ľ³æ (ÕýʽдÊÖ)
- Ó¦Öú: 8 (Ó×¶ùÔ°)
- ½ð±Ò: 4004.5
- É¢½ð: 1515
- Ìû×Ó: 499
- ÔÚÏß: 261.3Сʱ
- ³æºÅ: 972997
- ×¢²á: 2010-03-16
- רҵ: ģʽʶ±ð
7Â¥2014-03-08 16:36:39
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8Â¥2014-03-09 10:40:09













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