Nary coded structured light-based range scanners using color invariants

被引:24
|
作者
Benveniste, Rifat [1 ]
Unsalan, Cem [1 ]
机构
[1] Yeditepe Univ, Dept Elect & Elect Engn, Comp Vis Res Lab, TR-34755 Istanbul, Turkey
关键词
Range scanners; TI DM6437 EVM; Color invariants; ACQUISITION; REGISTRATION;
D O I
10.1007/s11554-011-0235-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Three dimensional range data provides useful information for various computer vision and computer graphics applications. For these, extracting the range data reliably is of utmost importance. Therefore, various range scanners based on different working principles are proposed in the literature. Among these, coded structured light-based range scanners are popular and used in most industrial applications. Unfortunately, these range scanners cannot scan shiny objects reliably. Either highlights on the shiny object surface or the ambient light in the environment disturb the code word. As the code is changed, the range data extracted from it will also be disturbed. In this study, we focus on developing a system that can scan shiny and matte objects under ambient light. Therefore, we propose color invariant-based binary, ternary, and quaternary coded structured light-based range scanners. We hypothesize that, by using color invariants, we can eliminate the effect of highlights and ambient light in the scanning process. Thus, we can extract the range data of shiny and matte objects in a robust manner. We implemented these scanners using a TI DM6437 EVM board with a flexible system setup such that the user can select the scanning type. Furthermore, we implemented a TI MSP430 microcontroller-based rotating table system that accompanies our scanner. With the help of this system, we can obtain the range data of the target object from different viewpoints. We also implemented a range image registration method to obtain the complete object model from the range data extracted. We tested our scanner system on various objects and provided their range and model data.
引用
收藏
页码:359 / 377
页数:19
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