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Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture

Lu, Rong-Sheng, Tian, Gui Yun, Gledhill, Duke and Ward, Steve (2006) Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture. Applied Optics, 45 (35). pp. 8839-8847. ISSN 0003-6935

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Surface speckle pattern intensity distribution resulting from laser light scattering from a rough surface contains various information about the surface geometrical and physical properties.A surface roughness measurement technique based on the texture analysis of surface speckle pattern texture images is put forward. In the surface roughness measurement technique, the speckle pattern texture images are taken by a simple setup configuration consisting of a laser and a CCD camera. Our experimental results show that the surface roughness contained in the surface speckle pattern texture images has a good monotonic relationship with their energy feature of the gray-level co-occurrence matrices. After the measurement system is calibrated by a standard surface roughness specimen, the surface roughness of the object surface composed of the same material and machined by the same method as the standard specimen surface can be evaluated from a single speckle pattern texture image. The robustness of the characterization of speckle pattern texture for surface roughness is also discussed. Thus the surface roughness measurement technique can be used for an in-process surface measurement.

Item Type: Article
Additional Information: This paper was published in Applied Optics and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.
Uncontrolled Keywords: Speckle imaging; Instrumentation, measurement, and metrology; Surface measurements, roughness
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Visualisation, Interaction and Vision
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Depositing User: Duke Gledhill
Date Deposited: 22 Nov 2011 15:32
Last Modified: 28 Aug 2021 21:11


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