[1]ÂÀÌú¸Ö,ÕÅ ÑÇ,ÀîÊÀÖÐ.½áºÏ¸Ä½øÁ£×ÓȺËã·¨µÄRANSAC¾«È·Æ¥Åä·½·¨[J].»úеÓëµç×Ó,2017,(07):18-22.
¡¡LYU Tiegang,ZHANG Ya,LI Shizhong.On RANSAC Accurate Matching Method Based on Improved Particle Swarm Optimization Algorithm[J].Machinery & Electronics,2017,(07):18-22.
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¾í:
ÆÚÊý:
2017Äê07ÆÚ
Ò³Âë:
18-22
À¸Ä¿:
Éè¼ÆÓëÑо¿
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2017-07-25

ÎÄÕÂÐÅÏ¢/Info

Title:
On RANSAC Accurate Matching Method Based on Improved Particle Swarm Optimization Algorithm
ÎÄÕ±àºÅ:
1001-2257(2017)07-0018-05
×÷Õß:
ÂÀÌú¸ÖÕÅ ÑÇÀîÊÀÖÐ
(Öб±´óѧ»úµç¹¤³ÌѧԺ,ɽÎ÷ Ì«Ô­ 030051)
Author(s):
LYU Tiegang ZHANG Ya LI Shizhong
(College of Mechatronic Engineering, North University of China, Taiyuan 030051, China)
¹Ø¼ü´Ê:
Á£×ÓȺËã·¨ RANSAC µ¥Î»·Ö½â ¾«È·Æ¥Åä
Keywords:
particle swarm optimization RANSAC unit decomposition accurate match
·ÖÀàºÅ:
TP391.4
ÎÄÏ×±êÖ¾Âë:
A
ÕªÒª:
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Abstract:
A RANSAC accurate matching method based on improved particle swarm optimization algorithm is presented in this paper to solve the problems of large calculation amount and low efficiency of accurate match in traditional random sampling consistency algorithm. Firstly, divide images into several parts by using the unit decomposition method in the differential manifold. Secondly, estimate and calculate the model parameters by using the improved particle swarm optimization algorithm to select the best leaf node. Finally, return to the optimal model with the N best leaf nodes kept, and calculate the accurate matching points in the different parts. By comparing the simulation experiment and the traditional RANSAC and GASAC, it finds out that the RANSAC accurate match method based on the improved particle swarm optimization algorithm has greatly improved the matching accuracy and the results stability, and reduced the number of false matching points.

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¸üÐÂÈÕÆÚ/Last Update: 2017-07-25