Journal Article10.1016/J.MEASUREMENT.2013.03.014
Surface roughness classification using image processing
T. Jeyapoovan,M. Murugan +1 more
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TL;DR: Novel methods used for human identification in biometrics are used in the present work for characterization of machined surfaces and the Euclidean and Hamming distances of the surface images are used for surface recognition.
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About: This article is published in Measurement. The article was published on 01 Aug 2013. The article focuses on the topics: Surface roughness & Image processing.
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Citations
Evaluation of turned and milled surfaces roughness using convolutional neural network
Achmad Pratama Rifai,Achmad Pratama Rifai,Hideki Aoyama,Nguyen Huu Tho,Siti Zawiah Md Dawal,Nur Aini Masruroh +5 more
TL;DR: This study proposes the use of convolutional neural network to evaluate the surface roughness directly from the digital image of surface textures, which avoids feature extraction since this step is integrated inside the network during the convolution process.
130
A new surface roughness measurement method based on a color distribution statistical matrix
TL;DR: In this article, a ground surface roughness measurement method is proposed to address current problems in the use of machine vision technology to measure roughness: the calculations are complex, and the measurement process is largely affected by the light source.
85
Evaluation of surface roughness in incremental forming using image processing based methods
TL;DR: In this paper, the authors focused on evaluation of surface roughness (Ra) in incrementally formed parts by different image processing based methods, twenty-seven parts are formed as per full factorial design in incremental forming by varying three important process parameters over three levels each.
63
Morphology-based defect detection in machined surfaces with circular tool-mark patterns
TL;DR: The proposed morphological operations with arc-shaped SEs can efficiently intensify local defects and remove the tool-mark background in the circular machined surface and can achieve high detection accuracy for various small defects, including scratch, bump and edge burst.
47
Designing indices to measure surface roughness based on the color distribution statistical matrix (CDSM)
TL;DR: A color image-based indices design and evaluation method that can quantitatively and comprehensively characterize the performance of different indices is proposed and designed.
45
References
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TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
Efficient iris recognition by characterizing key local variations
TL;DR: The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris.
Effect of surface roughness on the statistical distribution of image speckle intensity
Hitoshi Fujii,T. Asakura +1 more
TL;DR: In this article, an experimental investigation is made of the effect of surface roughness on the statistical distribution of image speckle intensity, which is formed by spatially coherent light at the image plane of an object having some roughness and its statistical properties are investigated.
145
Application of digital image magnification for surface roughness evaluation using machine vision
TL;DR: Based on the surface image features, a parameter called G a has been estimated using regression analysis, for the original images and for the magnified quality improved images and a comparison has been carried to establish correlation between magnification index, G a and surface roughness.
139
Surface measurement using active vision and light scattering
TL;DR: In this article, the authors reviewed the recent progress in surface measurement methods using active vision and light-scattering techniques and proposed an integrative method to measure surface waviness and form, roughness.
89