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	A novel description based on skeleton and contour for shape matching
	Hu, Jinlong1,2; Peng, Xianrong1; Fu, Chengyu1
	2015
会议名称	Proceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014
会议日期	2015
卷号	9255
页码	925541
通讯作者	Hu, Jinlong
中文摘要	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.
英文摘要	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.
收录类别	SCI ; EI
学科主题	Binary images - Computer vision - Edge detection - Extraction - Feature extraction - Geometry - Graphic methods - High power lasers - Musculoskeletal system - Object detection - Object recognition - Statistical methods
语种	英语
ISSN号	0277-786X
内容类型	会议论文
源URL	[http://ir.ioe.ac.cn/handle/181551/7440]
专题	光电技术研究所_光电工程总体研究室（一室）
作者单位	1.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
推荐引用方式 GB/T 7714	Hu, Jinlong,Peng, Xianrong,Fu, Chengyu. A novel description based on skeleton and contour for shape matching[C]. 见:Proceedings of SPIE: 20th International Symposium on High Power Systems and Applications 2014, HPLS and A 2014. 2015.

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