Optimised relfection imaging for surface roughness analysis using confocal laser scanning microscopy and height encoded image processing

Tomovich, S.J., and Peng, Z. (2005) Optimised relfection imaging for surface roughness analysis using confocal laser scanning microscopy and height encoded image processing. Journal of Physics: conference series, 13. pp. 426-429.

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Quantitative surface measurement is an important field in engineering. Due to the complexity of surface topography, accurate surface characterisation requires three-dimensional (3D) surface measurements not provided by many current measurement systems. A nondestructive and versatile technique for quantifying 3D surface features is Confocal Laser Scanning Microscopy (CLSM). However, there is little documentation on standard CLSM hardware settings required to capture images of suitable quality for 3D surface measurements. Understanding the complex relationship between CLSM settings, specimen properties and image quality is crucial to optimising the acquisition process for quantitative 3D surface measurements. The response of image quality to variations in CLSM hardware settings and specimen properties has been investigated in the study. Through the investigations, criteria have been developed to select optimal CLSM hardware settings to minimise image noise, eliminate image distortion, maximise contrast and resolution for reliable and accurate 3D numerical surface measurements. A reliable 3D image analysis system has been developed for image processing and surface measurement of engineering surfaces and small particles. The image analysis system developed in Matlab for the confocal system provides a new means to quantitatively characterise a wide range of engineering surfaces with accuracy and efficiency.

Item ID: 6658
Item Type: Article (Research - C1)
ISSN: 1742-6596
Keywords: height encoded images; laser scanning microscopy; reflection images; surface morphology; surface roughness
Date Deposited: 08 Feb 2010 01:40
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 60%
09 ENGINEERING > 0913 Mechanical Engineering > 091399 Mechanical Engineering not elsewhere classified @ 40%
SEO Codes: 86 MANUFACTURING > 8617 Communication Equipment > 861703 Voice and Data Equipment @ 51%
86 MANUFACTURING > 8612 Fabricated Metal Products > 861202 Machined Metal Products @ 49%
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