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Chao, Ping; Hwang, Yeong Dong

doi:10.1023/a:1018552620196

Cited by 21 publications

(1 citation statement)

References 8 publications

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“…In recent years, the use of computer visionbased systems and artificial intelligence techniques as such, artificial neural network, to estimate tool's lifetime in metal cutting process has been aimed by many researches (ALAJMI et al, 2005;PATRA et al, 2007;CHAO and HWANG, 1997;ALAJMI and ALFARES, 2007;VOLKAN ATLI et al, 2006;GADELMAWLA et al, 2008;INOUE, KONISHI and IMAI, 2009;WANG et al, 2009). Usually, the lifetime is predicted by detecting visible -sometimes very small -degeneration in images of a cutting tool, which is supplied by a typical experiment in the turning process.…”

Section: Introductionmentioning

confidence: 99%

Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures

Pereira¹,

Baptista²,

Coppini³

et al. 2010

Exacta

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This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

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