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An Algorithm for the Detection and Measurement of Rail Surface Defects

 

作者: ThomasH. Short,  

 

期刊: Journal of the American Statistical Association  (Taylor Available online 1993)
卷期: Volume 88, issue 422  

页码: 436-440

 

ISSN:0162-1459

 

年代: 1993

 

DOI:10.1080/01621459.1993.10476293

 

出版商: Taylor & Francis Group

 

关键词: Image processing;Iterated conditional modes;Restoration

 

数据来源: Taylor

 

摘要:

Defects on the surface of railroad tracks have been the cause of growing concern over the past three decades. The automated detection and classification of rail surface defects would be of great assistance to rail maintenance planners, who develop grinding strategies to prevent the development of potentially dangerous deterioration. Videotaped images of the surface of rail have been obtained, but they are subject to distortions due to the acquisition process as well as physical phenomena on the track itself. In this analysis, an algorithm is presented for the simultaneous restoration and segmentation of objects in a two-dimensional image. The algorithm relies on distributions that model the relationships between sites and neighbors in order to restore a distorted image to an estimate of its ideal form, and also obtain detailed information about the objects located in the image. The foundation of the algorithm is the Iterated Conditional Modes procedure for image restoration. The resulting extension is capable of providing detailed measurements of the geometric features of objects detected in an image. The extended algorithm is applied to an image distorted by simulated noise, and also to an image taken from a videotape of a rail surface. The results of the analysis demonstrate the potential for accurate detection, measurement, and classification of rail surface defects.

 

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