Paper physics: Paper and board surface roughness characterization using laser profilometry and gray level cooccurrence matrix

Machine vision based surface roughness inspection method is applied to assess different cylindrical grinding surfaces under LED illumination. Images directly recorded by a camera are analyzed by gray level co-occurrence matrix (GLCM) method to discover its texture information. It shows obviously relationship between feature values of the matrix and their corresponding surface roughness values. Uniform table are also designed to choose optimal parameters, which is five of distance between pixel pairs and ninety degree of angle to calculate GLCM. Entropy is chosen to represent different surface roughness images by comparison of correlation coefficients between the parameters and the corresponding surface roughness values.

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Study on Brittle Graphite Surface Roughness Detection Based on Gray-Level Co-occurrence Matrix

2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce.2018.00062 ◽

2018 ◽

Cited By ~ 2

Author(s):

Li Zhou ◽

Hanzhang Liu ◽

Xiaopeng Zhuang ◽

Dawei Liu

Keyword(s):

Surface Roughness ◽

Graphite Surface ◽

Gray Level ◽

Occurrence Matrix

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Estimation of Articular Cartilage Surface Roughness Using Gray-Level Co-Occurrence Matrix of Laser Speckle Image

Materials ◽

10.3390/ma10070714 ◽

2017 ◽

Vol 10 (7) ◽

pp. 714 ◽

Cited By ~ 10

Author(s):

Doaa Youssef ◽

Hatem El-Ghandoor ◽

Hamed Kandel ◽

Jala El-Azab ◽

Salah Hassab-Elnaby

Keyword(s):

Surface Roughness ◽

Articular Cartilage ◽

Laser Speckle ◽

Gray Level ◽

Cartilage Surface ◽

Speckle Image ◽

Articular Cartilage Surface ◽

Occurrence Matrix

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Surface roughness of impression materials and dental stones scanned by non-contacting laser profilometry

Dental Materials ◽

10.1016/j.dental.2008.10.003 ◽

2009 ◽

Vol 25 (4) ◽

pp. 500-505 ◽

Cited By ~ 25

Author(s):

Jose M. Rodriguez ◽

Richard V. Curtis ◽

David W. Bartlett

Keyword(s):

Surface Roughness ◽

Impression Materials ◽

Laser Profilometry

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Surface roughness evaluation of marble by 3D laser profilometry and pulsed thermography

NDT & E International ◽

10.1016/j.ndteint.2004.03.002 ◽

2004 ◽

Vol 37 (7) ◽

pp. 571-575 ◽

Cited By ~ 24

Author(s):

N.P Avdelidis ◽

E.T Delegou ◽

D.P Almond ◽

A Moropoulou

Keyword(s):

Surface Roughness ◽

Pulsed Thermography ◽

Laser Profilometry ◽

Roughness Evaluation

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Intelligent Vision Based Technique Using ANN for Surface Finish Assessment of Machined Components

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.364-366.1251 ◽

2007 ◽

Vol 364-366 ◽

pp. 1251-1256

Author(s):

M.Rajaram Narayanan ◽

S. Gowri

Keyword(s):

Surface Roughness ◽

Surface Finish ◽

Image Features ◽

Gray Level ◽

Image Processing Algorithm ◽

Vlsi Chip ◽

Vision Processing ◽

Accepted Standard ◽

Machined Parts ◽

Fpga Chip

In this work, an FPGA hardware based image processing algorithm for preprocessing the images and enhance the image quality has been developed. The captured images were processed using a FPGA chip to remove the noise and then using a neural network, the surface roughness of machined parts produced by the grinding process was estimated. To ensure the effectiveness of this approach the roughness values quantified using these image vision techniques were then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness was performed by extracting key image features using Fourier transform and the standard deviation of gray level intensity values. A VLSI chip belonging to the Xilinx family Spartan-IIE FPGA board was used for the hardware based filter implementation. The coding was done using the popular VHDL language with the algorithms developed so as to exploit the implicit parallel processing capability of the chip. Thus, in this work an exhaustive analysis was done with comparison studies wherever required to make sure that the present approach of estimating surface finish based on the computer vision processing of image is more accurate and could be implemented in real time on a chip.

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