Curve representation and matching based on feature points and minimal area

• Typical applications • Previous Articles Next Articles

Curve representation and matching based on feature points and minimal area

Received:2008-10-28 Revised:2008-12-31 Online:2009-04-01 Published:2009-04-01
Contact: Wei REN

基于特征点和最小面积的曲线描述和匹配

张桂梅任伟徐芬

南昌航空大学南昌航空大学

通讯作者: 任伟

Abstract

Abstract: In order to recognize the curve whose feature points are the same but the curvature between the feature points is different, a new method for representing and recognizing the contour curve was proposed. First, feature points of the contour were extracted for the rough matching; then the sampling points of the sub-curve were obtained based on the precision requirement using the given minimal area threshold. A new recognition vector of sample points was defined, and a novel recognition vector matrix was constructed based on the recognition vector of sample points; last the similarity of the corresponding sub-curves was calculated by comparing the recognition vector matrix. The curve was recognized by recognizing their each sub-curve. The matching method was a process from simple to complex, thus many redundancies calculations were avoided. The experimental results show the proposed algorithm is efficient and feasible.

Key words: feature point, recognition vector, recognition vector matrix, curve representation

摘要： 为了对关键特征点相同而子曲线曲率不同的曲线进行识别，提出一种新的平面曲线的描述和匹配方法。基于关键特征点进行粗匹配，根据精度要求设定最小面积阈值在子曲线上重新采样点，定义了一种新的采样点的识别向量，并根据子曲线上采样点的识别向量构造了新的识别向量矩阵，最后根据识别向量矩阵的差异度度量子曲线的相似性。通过对所有子曲线的识别实现对整条曲线的识别。该识别方法逐层筛选、由粗到精，避免了冗余操作。实验表明该方法高效、可行。

关键词: 特征点, 识别向量, 识别向量矩阵, 曲线描述

CLC Number:

TP391.41

Gui-mei ZHANG Wei REN Fen XU. Curve representation and matching based on feature points and minimal area[J]. Journal of Computer Applications.

张桂梅任伟徐芬. 基于特征点和最小面积的曲线描述和匹配[J]. 计算机应用.

[1]	ZHANG Hui, ZHANG Nana, HUANG Jun. Multi-angle head pose estimation method based on optimized LeNet-5 network [J]. Journal of Computer Applications, 2021, 41(6): 1667-1672.
[2]	Hao FU, Hegen XU, Zhiming ZHANG, Shaohua QI. Visual simultaneous localization and mapping based on semantic and optical flow constraints in dynamic scenes [J]. Journal of Computer Applications, 2021, 41(11): 3337-3344.
[3]	ZHENG Sicheng, KONG Linghua, YOU Tongfei, YI Dingrong. Semantic SLAM algorithm based on deep learning in dynamic environment [J]. Journal of Computer Applications, 2021, 41(10): 2945-2951.
[4]	DENG Xiwen, FENG Ziliang, QIU Chenpeng. Face liveness detection method based on near-infrared and visible binocular vision [J]. Journal of Computer Applications, 2020, 40(7): 2096-2103.
[5]	SHI Zhiliang, CAI Wangyue, WANG Guoqiang, XIONG Linjie. Intrinsic shape signature algorithm based on adaptive neighborhood [J]. Journal of Computer Applications, 2020, 40(4): 1151-1156.
[6]	DING Hui, LI Lihong, YUAN Gang. Image registration algorithm combining GMS and VCS+GC-RANSAC [J]. Journal of Computer Applications, 2020, 40(4): 1138-1143.
[7]	ZHU Chengde, LI Zhiwei, WANG Kai, GAO Yan, GUO Hengchang. Image matching algorithm based on improved RANSAC-GMS [J]. Journal of Computer Applications, 2019, 39(8): 2396-2401.
[8]	WEI Jiawang, WANG Xiao, YUAN Yubo. Adaptive window regression method for face feature point positioning [J]. Journal of Computer Applications, 2019, 39(5): 1459-1465.
[9]	ZHOU Yujin, WANG Xiaodong, ZHANG Lige, ZHU Kai, YAO Yu. Feature point localization of left ventricular ultrasound image based on convolutional neural network [J]. Journal of Computer Applications, 2019, 39(4): 1201-1207.
[10]	XIU Chunbo, MA Yunfei, PAN Xiaonan. Image feature point matching method based on distance fusion [J]. Journal of Computer Applications, 2019, 39(11): 3158-3162.
[11]	WANG Xupeng, LEI Hang, LIU Yan, SANG Nan. Hierarchical approach for 3D non-rigid shape registration [J]. Journal of Computer Applications, 2018, 38(8): 2381-2385.
[12]	YE Feng, HONG Zheng, LAI Yizong, ZHAO Yuting, XIE Xianzhi. Improved D-Nets algorithm with matching quality purification [J]. Journal of Computer Applications, 2018, 38(4): 1121-1126.
[13]	WEI Yuxi, WU Yueqing, TAO Pan, YAO Yu. Left ventricle segmentation in transesophageal echocardiography based on supervised descent method [J]. Journal of Computer Applications, 2018, 38(2): 545-549.
[14]	SUN Zengyou, DUAN Yushuai, LI Ya. Image feature point matching algorithm based on center surround filter detection [J]. Journal of Computer Applications, 2017, 37(12): 3547-3553.
[15]	BAI Xuebing, CHE Jin, MU Xiaokai, ZHANG Ying. Improved feature points matching algorithm based on speed-up robust feature and oriented fast and rotated brief [J]. Journal of Computer Applications, 2016, 36(7): 1923-1926.