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http://dx.doi.org/10.14372/IEMEK.2017.12.6.395

Depth Image Based Feature Detection Method Using Hybrid Filter  

Jeon, Yong-Tae (Sunmoon University)
Lee, Hyun (Sunmoon University)
Choi, Jae-Sung (Sunmoon University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.12, no.6, 2017 , pp. 395-403 More about this Journal
Abstract
Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.
Keywords
Depth image; Feature detection; Hybrid filter mechanism; Noise; FAST algorithm;
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Times Cited By KSCI : 3  (Citation Analysis)
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