Feather quill crease recognition based on local Radon transform

doi:10.3724/SP.J.1087.2013.01443

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (05): 1443-1445.DOI: 10.3724/SP.J.1087.2013.01443

• Network and distributed techno • Previous Articles Next Articles

Feather quill crease recognition based on local Radon transform

YUE Hongwei¹,²,WANG Renhuang¹,ZHANG Jinghua¹

1. Faculty of Automation, Guangdong University of Technology, Guangzhou Guangdong 510006, China
2. Information College, Zhongkai University of Agriculture and Engineering, Guangzhou Guangdong 510225, China

Received:2012-11-19 Revised:2013-01-09 Online:2013-05-08 Published:2013-05-01
Contact: YUE Hongwei

局部Radon变换技术及在毛杆折痕识别中的应用

岳洪伟¹,²,汪仁煌¹,张晶华¹

1. 广东工业大学自动化学院，广州 510006
2. 仲恺农业工程学院信息学院，广州 510225

通讯作者: 岳洪伟
作者简介:岳洪伟(1979-)，男，安徽亳州人，讲师，博士研究生，主要研究方向: 图像处理、模式识别；汪仁煌(1945-)，男，上海人，教授，博士生导师，主要研究方向: 智能测控、信息处理、机器视觉；张晶华(1981-)，男，广西玉林人，博士研究生，主要研究方向: 图像处理、模式识别。
基金资助:
广东省自然科学基金资助项目(S2012010010652);广东省科技计划项目(2012B020314005)

Abstract

Abstract: Concerning the recognition of feather quill crease feature, a new feature extraction method was proposed. In order to solve the scaling and translation sensitivity of Radon transform, an improved Radon transform was used to extract moment invariants of target region and local projection technology was adopted to eliminate interference physiological texture of feather quill. Obtaining invariants matrix by changing the scale factor, Singular Value Decomposition (SVD) was provided here to obtain feature invariant for classification and recognition. The results show that this suggested method has higher robustness and higher recognition rate compared with other algorithms.

Key words: feather quill crease, Radon transform, local projection, invariant moment, image recognition

摘要： 针对羽毛杆折痕识别问题，提出一种新的特征提取方法。为了消除Radon变换对缩放平移敏感问题，采用改进的Radon变换提取目标区域的不变矩，并引入局部投影技术消除羽毛杆生理纹理干扰。通过改变尺度因子获得矩不变量矩阵，并采用奇异值分解(SVD)获得特征不变量用于分类识别。实验结果表明该方法具有较强的鲁棒性，具有较高的折痕识别率。

关键词: 羽毛杆折痕, Radon变换, 局部投影, 矩不变量, 图像识别

CLC Number:

YUE Hongwei WANG Renhuang ZHANG Jinghua. Feather quill crease recognition based on local Radon transform[J]. Journal of Computer Applications, 2013, 33(05): 1443-1445.

岳洪伟汪仁煌张晶华. 局部Radon变换技术及在毛杆折痕识别中的应用[J]. 计算机应用, 2013, 33(05): 1443-1445.

References

［1］刘洪江，汪仁煌，李学聪.基于动态轮廓模型的羽毛分割改进算法［J］.计算机应用，2011,31(8):2246-2245.

［2］刘洪江，汪仁煌，何最红,等.中心线模式Snake算法对细长目标的提取［J］.光电工程，2011，38(9):124-129.

［3］LIU H J，WANG R H，MING J F, et al.The finite ridgelet transform for defeat detection of quill［J］. Advanced Materials Research, 2011, 317/318/319: 915-918.

［4］岳洪伟，汪仁煌，何最红. 基于Normalized cut的羽毛杆自动提取［J］. 计算机应用，2012，32(7):1989-1901.

［5］罗元，谢彧，张毅. 基于Kinect传感器的智能轮椅手势控制系统的设计与实现［J］. 机器人，2012，34(1):110-113.

［6］HU M K. Visual pattern recognition by moment invariants［J］.IEEE Transactions on Information Theory,1962,8(1):179-187.

［7］王向阳，李东明，杨红颖.基于Zernike色度分布矩的彩色图像检索算法［J］. 模式识别与人工智能，2012，25(2):313-317.

［8］TEH C H,CHIN R T.On image analysis by the methods of moments［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(4):496-512.

［9］KAN C,SRINATH M D. Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments［J］.Pattern Recognition,2002,35(1):143-154.

［10］LIAO S X,PAWLAK M. On the accuracy of Zernike moments for image analysis［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(12):1358-1364.

［11］DEANS S R. The Radon transform and some of its applications［M］.New York:Wiley,1983.

［12］吕玉增.形状仿射不变特征提取与识别研究［D］.长沙：国防科学技术大学，2008.

［13］ALSHAYKH O K, DOHERTY J F. Invariant image analysis based on Radon transform and SVD［J］.IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing,1996,43(2):123-133.

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Feather quill crease recognition based on local Radon transform

局部Radon变换技术及在毛杆折痕识别中的应用

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