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A novel description based on skeleton and contour for shape matching
Hu, Jinlong1,2; Peng, Xianrong1; Fu, Chengyu1
Volume9255
Pages925541
2015
Language英语
ISSN0277-786X
DOI10.1117/12.2065209
Indexed BySCI ; Ei
Subtype会议论文
AbstractIn computer vision field, feature extraction plays a critical role in shape matching, image alignment, object recognition and tracking etc. Generally speaking, feature extraction consists of three steps: feature detection, feature description and feature matching. In the second step, the detected features (e.g. gray value, SIFT, Harris corners) are converted to vectors or the form that can be described mathematically such that feature can be matched correctly. How to construct an efficient descriptor to realize accurate shape matching under a variety of transformations is still a challenge. To this end, a novel shape descriptor based on skeleton for shape matching is proposed in this paper. Firstly, the image is smoothed with Gaussian filter to remove the influence of the noise. Secondly, the smoothed image is segmented with Fuzzy C-means Cluster (FCM) to obtain a binary image. Thirdly, the binary image"™s skeleton is extracted with Medial Axis Transform (MAT), thus the skeleton"™s endpoints and joint-points locations are acquired. Furthermore, the object"™s contour is extracted with contour coding. In the construction of skeletal descriptor, the relative location vectors of the skeletal endpoints to each contour point are computed. Being similar to shape context, statistical histogram is constructed in log-polar coordinate. Consequently, shape matching is performed via two histograms"™ similarity measurement. Experiments on standard MPEG7 dataset show that the proposed shape description method allows translation, scale and rotation invariance. © 2015 SPIE.; In computer vision field, feature extraction plays a critical role in shape matching, image alignment, object recognition and tracking etc. Generally speaking, feature extraction consists of three steps: feature detection, feature description and feature matching. In the second step, the detected features (e.g. gray value, SIFT, Harris corners) are converted to vectors or the form that can be described mathematically such that feature can be matched correctly. How to construct an efficient descriptor to realize accurate shape matching under a variety of transformations is still a challenge. To this end, a novel shape descriptor based on skeleton for shape matching is proposed in this paper. Firstly, the image is smoothed with Gaussian filter to remove the influence of the noise. Secondly, the smoothed image is segmented with Fuzzy C-means Cluster (FCM) to obtain a binary image. Thirdly, the binary image"™s skeleton is extracted with Medial Axis Transform (MAT), thus the skeleton"™s endpoints and joint-points locations are acquired. Furthermore, the object"™s contour is extracted with contour coding. In the construction of skeletal descriptor, the relative location vectors of the skeletal endpoints to each contour point are computed. Being similar to shape context, statistical histogram is constructed in log-polar coordinate. Consequently, shape matching is performed via two histograms"™ similarity measurement. Experiments on standard MPEG7 dataset show that the proposed shape description method allows translation, scale and rotation invariance. © 2015 SPIE.
Conference NameProceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014
Conference Date2015
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7440
Collection光电工程总体研究室(一室)
Corresponding AuthorHu, Jinlong
Affiliation1. Institute of Optics and Electronics, Key Laboratory of Beam Control, Chinese Academy of Sciences, Chengdu, Sichuan, China
2. University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Hu, Jinlong,Peng, Xianrong,Fu, Chengyu. A novel description based on skeleton and contour for shape matching[C],2015:925541.
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