Fast shape recognition via a bi-level restraint reduction of contour coding
Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of fea...
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| Published in: | The Visual computer Vol. 40; no. 4; pp. 2599 - 2614 |
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| Language: | English |
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01.04.2024
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| ISSN: | 0178-2789, 1432-2315 |
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| Abstract | Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of feature structure that intrinsically deteriorates recognition performance. Furthermore, we propose a fast shape recognition method based on a bi-level restraint reduction of contour coding (CC2RR), which provides more effective theoretical support for the practical application of the visual algorithm. CC2RR reduces restraint performed from contour feature extraction and expression, respectively. First, for shape contour, the restraint of contour feature extraction is reduced by transforming the direction of contour points to contour segments; second, for the encoded contour segment, the restraint of the contour feature expression is reduced; in other words, the current direction is reduced to the previous and the next direction. Guided by these insights, Hamming code distance is used to match the coding features after the twofold restraint reduction, and the results are obtained. Experimental results verify that the method significantly improves the performance, which runs up to 500 times faster than the existing description methods based on shape contours while increasing robustness. This makes the method useful in practical software system. |
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| AbstractList | Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of feature structure that intrinsically deteriorates recognition performance. Furthermore, we propose a fast shape recognition method based on a bi-level restraint reduction of contour coding (CC2RR), which provides more effective theoretical support for the practical application of the visual algorithm. CC2RR reduces restraint performed from contour feature extraction and expression, respectively. First, for shape contour, the restraint of contour feature extraction is reduced by transforming the direction of contour points to contour segments; second, for the encoded contour segment, the restraint of the contour feature expression is reduced; in other words, the current direction is reduced to the previous and the next direction. Guided by these insights, Hamming code distance is used to match the coding features after the twofold restraint reduction, and the results are obtained. Experimental results verify that the method significantly improves the performance, which runs up to 500 times faster than the existing description methods based on shape contours while increasing robustness. This makes the method useful in practical software system. |
| Author | Meng, Fanjie Guo, Baolong Li, Zekun Jiang, Bingting |
| Author_xml | – sequence: 1 givenname: Zekun surname: Li fullname: Li, Zekun organization: Institute of Intelligent Control and Image Engineering, Xidian University – sequence: 2 givenname: Baolong orcidid: 0000-0002-8406-0689 surname: Guo fullname: Guo, Baolong email: blguo@xidian.edu.cn organization: Institute of Intelligent Control and Image Engineering, Xidian University – sequence: 3 givenname: Fanjie surname: Meng fullname: Meng, Fanjie organization: Institute of Intelligent Control and Image Engineering, Xidian University – sequence: 4 givenname: Bingting surname: Jiang fullname: Jiang, Bingting organization: College of Computer Science and Electronic Engineering, Hunan University |
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| Keywords | Contour coding Restraint Shape recognition Reduce restraint |
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| Title | Fast shape recognition via a bi-level restraint reduction of contour coding |
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