• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.186-192
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• 2019

In general, many marine organisms are attached to the bottom of a ship in operation or a ship in construction. Due to this phenomenon, the roughness of the ship surface increases, resulting in loss of ship speed, resulting in economic losses and environmental pollution. This study acquires / utilizes camera images attached to ship's bottom and underwater drones to check the condition of bottom. The acquired image will determine the roughness according to marine life by the administrator's visual confirmation. Therefore, by applying a filter algorithm to correct the image to the original image can help in the correct determination of whether or not attached to marine life. Various correction filters are required for the underwater image correction algorithm, and the lighting suitable for the dark underwater environment has a great influence on the judgment. The results of the research test according to the calibration algorithm and the roughness of each algorithm are considered to be applicable to many fields.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.38-48
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• 2020

In order to inspect the existing or newly constructed ship's hull, a professional diver directly inspects the ship's bottom of the water. However, since it is a work done by people, there are many dangers such as human casualties and crashes. To solve this problem, it is necessary to develop underwater drones for ship inspection for visual inspection. The technology applied to underwater drones, the use and manufacturing process of each component, and the method of manufacture such as firmware development were described, and the difference was compared by measuring the drone's own driving ability and driving ability using crawler under water, and the location tracking device test confirmed the error from the actual location. It is estimated that the use of underwater drones produced through this research will prevent human casualties and achieve economic effects and stability.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.18-25
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• 2011

In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.