• Korean Journal of Optics and Photonics
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• v.20 no.6
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• pp.354-360
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• 2009

We have constructed and analyzed the performance of a simple fiber bundle multi-focal microscope. The microscope had a fiber bundle substituted for micro-lens array that is the core part of MMM(multi-focal multi-photon microscope). The MMM is a type of confocal microscope. To analyze the performance and characteristics of the fiber bundle multi-focal microscope, three types of samples were used: a standard grating, USAF 1951(7, 3), and 1951(7, 6). Using two polarizers and a polarizing beam splitter, we eliminated noise and got clear images. We obtained the FWHM of fiber spot images with the standard grating using two different magnifier lenses which were 63X and 20X, and found an image of the sample as a distribution of fiber spot images. For this case we used the low magnification lens, which gives denser distribution, so that we could get clearer images. In order to test the resolution of the fiber bundle multi-focal microscopic system, we used the USAF 1951 sample which has a smaller line interval than that of the standard grating. The FWHM of the line width of the image coincides well with the real line width of the USAF 1951 sample. We confirmed the performance of a fiber bundle multi-focal microscopic system which is relatively simple but has submicron resolution and is able to get 1600 images at the same time.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.10-16
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• 2012

For high speed visual inspection in semiconductor industries, it is essential to acquire two-dimensional images on regions of interests with a large field of view (FOV) and a high resolution simultaneously. In this paper, an imaging system is newly proposed to achieve high quality image in terms of precision and FOV, which is composed of single lens, a beam splitter, two camera sensors, and stereo image grabbing board. For simultaneously acquired object images from two camera sensors, Zhang's camera calibration method is applied to calibrate each camera first of all. Secondly, to find a mathematical mapping function between two images acquired from different view cameras, the matching matrix from multiview camera geometry is calculated based on their image homography. Through the image homography, two images are finally registered to secure a large inspection FOV. Here the inspection system of using multiple images from multiple cameras need very fast processing unit for real-time image matching. For this purpose, parallel processing hardware and software are utilized, such as Compute Unified Device Architecture (CUDA). As a result, we can obtain a matched image from two separated images in real-time. Finally, the acquired homography is evaluated in term of accuracy through a series of experiments, and the obtained results shows the effectiveness of the proposed system and method.