IEEE Trans Image Process - Robust feature point matching with sparse model.

Tópicos

{ problem(2511) optim(1539) algorithm(950) }
{ method(1219) similar(1157) match(930) }
{ featur(1941) imag(1645) propos(1176) }
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{ imag(1947) propos(1133) code(1026) }
{ result(1111) use(1088) new(759) }
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{ take(945) account(800) differ(722) }
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{ group(2977) signific(1463) compar(1072) }
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{ gene(2352) biolog(1181) express(1162) }
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Resumo

Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.

Resumo Limpo

featur point match incorpor pairwis constraint can cast integ quadrat program iqp problem sinc nphard approxim method requir optim solut iqp match problem discret binari thus spars natur motiv us use spars model featur point match problem main advantag propos spars featur point match spm method generat spars solut thus natur impos discret map constraint approxim optim process therefor can optim iqp match problem approxim discret domain addit effici algorithm can deriv solv spm problem promis experiment result synthet point set match realworld imag featur set match task show effect propos featur point match method

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