Texture analysis using computer vision

Damodarasamy, Sampath; Raman, S. Ganesh Sundara

doi:10.1016/0166-3615(91)90005-t

Cited by 35 publications

(10 citation statements)

References 10 publications

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“…In the last few decades, alternative methods rather than stylus have been developed. Those can be loosely divided into optical methods (such as vertical scanning interferometry) and non-optical methods (such as scanning tunneling microscopy) [1] [3].…”

Section: Introductionmentioning

confidence: 99%

Average Surface Roughness Measurement Using Photometric Stereo Method

Somthong¹,

Yang²

2016

ujeee

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This paper presents a technique for measuring surface roughness (Ra), using photometric stereo method. The method utilizes three or more images of the measured surface from the same view point, taken sequentially under different illumination conditions. The scenes captured by the camera were reconstructed by the least square method to obtain surface normal from surface topography. Three-dimensional surface textural patterns were recovered from those surfaces normal. The system was validated with three standard surface roughness specimens. The Ra calculated from the recovered surface was compared with the nominal value of the standard surface roughness. The best coefficient of the slant angles can also be determined through the linear regression. The experimental results indicate that the technique can be applied to well recover standard surface roughness.

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Section: Introductionmentioning

confidence: 99%

Average Surface Roughness Measurement Using Photometric Stereo Method

Somthong¹,

Yang²

2016

ujeee

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“…Furthermore, the procedure is a post-process approach, which is applied off-line and not amenable for automation, and the measurement is also relatively slow. In recent years, the modeling and prediction problems of surface roughness of a workpiece by computer vision in turning operations have received a great deal of attention [1][2][3][4][5][6][7][8]. Although it has been shown that the surface roughness is strongly characterized by the surface image, practical surface roughness instruments based on computer vision technology are still difficult [1].…”

Section: Introductionmentioning

confidence: 99%

Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system

Lee

2005

Precision Engineering

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“…The main quality of surface roughness can be measured by using two techniques, namely optical and stylus techniques. Radii of the diamond can be checked using stylus but surface roughness (Damodarasamy and Raman, 2003) can be checked by optical techniques which are quite expensive. Now-a-days machine vision is used to evaluate and analyze the microscopic defects in surface of the materials.…”

Section: Introductionmentioning

confidence: 99%