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  2011, Vol. 24 Issue (5): 645-650    DOI:
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A Learning Algorithm for Shortest Branch Cut Length Problem
ZHENG Dong-Liang, DA Fei-Peng
School of Automation, Southeast University, Nanjing 210096

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Abstract  Branch cut method is an effcient noise-immune algorithm for correct phase unwrapping of noisy phase maps. The shortest branch cut length promises the optimal unwrapping of the wrapped phase maps. The shortest branch cut length problem belongs to combinatorial optimizations. A learning algorithm is proposed to resolve the problem. One solution for the problem is one individual for the algorithm. Individuals learn from other individuals and mutate by themselves to realize the evolution, which is similar to the crossover and mutation operator in the genetic algorithm. Compared with the traditional methods, the learning algorithm is fast and competitive.
Key wordsShortest Branch Cut Length Problem      Phase Unwrapping      Combinatorial Optimization      Learning Algorithm     
Received: 19 July 2010     
ZTFLH: TP31  
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ZHENG Dong-Liang
DA Fei-Peng
Cite this article:   
ZHENG Dong-Liang,DA Fei-Peng. A Learning Algorithm for Shortest Branch Cut Length Problem[J]. , 2011, 24(5): 645-650.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2011/V24/I5/645
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