2013
DOI: 10.1080/10739149.2013.769176
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Thermal Image Refinement Approach for Scratches on a Magnetic Disk Evaluated at Various Angles

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Cited by 2 publications
(3 citation statements)
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“…In addition, for further enhancement of signal amplitude, the RF amplifier circuit is extended to transform a light signal from four photodiode detectors to four current signals. The technique is then developed to integrate the signals by using the RF summing amplifier circuit, as shown in Figure 3 and Equations (4) and (5) for the CD/DVD set, respectively.…”
Section: Rf Amplifier Of Signalsmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, for further enhancement of signal amplitude, the RF amplifier circuit is extended to transform a light signal from four photodiode detectors to four current signals. The technique is then developed to integrate the signals by using the RF summing amplifier circuit, as shown in Figure 3 and Equations (4) and (5) for the CD/DVD set, respectively.…”
Section: Rf Amplifier Of Signalsmentioning
confidence: 99%
“…Although these three methods are surface free-contact techniques, they are expensive, destructive, and time-consuming. In order to cover up these disadvantages, the scratch inspection with a satisfied resolution in a quick process was developed using a thermal infrared camera to detect the surface heat and it was found that heat appears only on the surface, not scratch [5]. A magnetic disk, in the real process, rotates at a certain velocity and researchers have proposed various possibilities to detect a scratch dynamically.…”
Section: Introductionmentioning
confidence: 99%
“…Hence, by integrating image processing techniques [6][7][8][9][10] and modifying the conventional method, [11,12] this study designs an automatic system for defects of printed art tiles, which is suitable for in-line inspection of printed tile defects. After analyzing the art tile texture features through gray level co-occurrence matrix, the analysis results were input into the backward propagation neural network in order to train the defect classifier.…”
Section: Introductionmentioning
confidence: 99%