A unified hypothesis generation framework for multi-structure model fitting

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• 作者：Taotao Lai^aAuthor Vitae ; Hanzi Wang^a ; ^{wang.hanzi@gmail.com}Author Vitae ; Yan Yan^aAuthor Vitae ; Liming Zhang^bAuthor Vitae
• 关键词：Model fitting ; Hypothesis generation ; Unified framework ; Multi-structure data
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：26 January 2017
• 年：2017
• 卷：222
• 期：Complete
• 页码：144-154
• 全文大小：1901 K
• 卷排序：222

文摘

A unified sampling framework UHG is proposed for multi-structure model fitting. UHG combines the merits of a global search strategy and a local search strategy. The global search strategy is guided by the conventional sampling strategy. The local search strategy is guided by using the results of model selection. Experimental results show the effectiveness of UHG on public datasets.