Abstract
We address the problem of recognizing the object with distinctive edge features. For this purpose, a recognition approach based on local edge features is presented. First the edge features are detected in each image, and then its descriptor is computed to find the match features. Each match will give a vote with location, scale and orientation of the object. The recognition result can be found in the densest position in the vote space. Experimental results show that the presented method is robust and effective to the object with distinctive edge features.
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Tongwei Lu (1979) received his MS degree in engineering in 2004 from Wuhan Institute of Technology, China, and his Phd degree in engineering in 2008 from Huazhong University of Science and Technology, China. He is currently working with Wuhan Institute of Technology, China. His research interests involve pattern recognition, image processing and object recognition.
Ling Peng (1988) received her Bachelor degree in engineering in 2014 from Wuhan Institute of Technology, China. She is currently pursuing her MS degree studies with Wuhan Institute of Technology, China. His research interests involve imageprocessing and object recognition.
Yanduo Zhang (1971) received his MS degree in engineering in 1996 from Harbin Institute of Technology, China, and his Phd degree in engineering in 1999 from Harbin Institute of Technology, China. He is currently working with Wuhan Institute of Technology, China. His research interests involve pattern recognition and intelligent robot.
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Lu, T., Peng, L. & Zhang, Y. Edge feature based approach for object recognition. Pattern Recognit. Image Anal. 26, 350–353 (2016). https://doi.org/10.1134/S1054661816020243
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DOI: https://doi.org/10.1134/S1054661816020243