Exploring local regularities for 3D object recognition

Huaiwen Tian, Sheng-feng Qin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
12 Downloads (Pure)


In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.
Original languageEnglish
Pages (from-to)1104-1113
JournalChinese Journal of Mechanical Engineering
Issue number6
Early online date26 Sept 2016
Publication statusPublished - Nov 2016


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