Surface quality assessment using computer vision methods

Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

“…In their experiments, the authors found one exception where the ratio of the spread and the mean of the grey-level distribution was not a monotonically increasing function of surface roughness; therefore, the value of the ratio may lead to an incorrect measurement. Furthermore, the same research work (Hoy & Yu, 1991) addressed the possibility of using the Fourier transform (FFT) to characterise surface roughness in the frequency domain. However, only simple visual judgement of surface images in the frequency plane was discussed.…”

“…In addition, no information regarding the spatial distribution of periodic features can be obtained from the gray-level histogram. Hoy and Yu (1991) adapted the algorithm of Luk et al (1989)to characterise the surface quality of turned and milled specimens. In their experiments, the authors found one exception where the ratio of the spread and the mean of the grey-level distribution was not a monotonically increasing function of surface roughness; therefore, the value of the ratio may lead to an incorrect measurement.…”

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

“…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].…”

A vision system for surface roughness assessment using neural networks

“…In addition, no infoI~nation regarding the spatial distribution of periodic features can be obtained from the grey-level histogram. Hoy and Yu [4] adopted the algorithm of Luk and Huynh to characterise surface quality of turned and milled specimens. In their experiments they found one exception where the ratio of the spread and the mean of the grey-level distribution is not a strictly increasing function of surface roughness and, therefore, the value of the ratio may lead to incorrect measurement.…”