• Journal of the Korean Vacuum Society
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• v.2 no.4
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• pp.515-519
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• 1993

Image processin software is developed to get spot intensity form the frame of a LEEd pattern obtained by using a CCD Camera and a frame grabber. Imporved algorithm for more reasonable background substraction is implemented in this software . I/V Charateristicsof some spots form Cu(001) suface are collected . These results are compared with those of Davis and Noonan and found to be consistent with the latter results. This software is also applied to meausre RHEEd spot intensity oscillatin, and gives clear oscillations very easily.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.