• 한국측량학회지
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• 제38권5호
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• pp.455-465
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• 2020

As cities continue to expand and additional underground structures are constructed, a policy is being planned to expand addresses, which are national framework data. In this study, the application scope of a proposed 3D (three-dimensional) mixed-use complex space for assigning 3D addresses was defined, based on past research on 3D address systems and by analyzing related laws. Underpass shopping malls, underground walkways (excluding underground shopping malls), and 3D mixed-use transfer centers with a gross floor area of 2,000㎡ or more are connected with each space by underground walkways or public paths. In addition, the detailed space corresponding to the public space of the 3D mixed-use complex space was presented and distinguished from the space to which the detailed address is assigned. The criteria for setting the road section were presented based on the intermediate space, which is a characteristic of 3D mixed-use complex spaces. The proposed criteria were applied to the Express Bus Terminal station (3D mixed-use transfer center) and COEX mall (underpass shopping malls). Thus, the road section was set for an unfamiliar 3D mixed-use complex space. However, by applying the proposed criteria to various 3D mixed-use complex spaces, additional and detailed criteria for different cases should be prepared.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.659-669
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• 2015

Face Recognition assumes much significance in the context of security based application. Normally, high resolution images offer more details about the image and recognizing a face from a reasonably high resolution image would be easier when compared to recognizing images from very low resolution images. This paper addresses the problem of recognizing faces from a very low resolution image whose size is as low as $8{\times}8$. With the use of CCTV(Closed Circuit Television) and with other surveillance camera-based application for security purposes, the need to overcome the shortcomings with very low resolution images has been on the rise. The present day face recognition algorithms could not provide adequate performance when employed to recognize images from VLR images. Existing methods use super-resolution (SR) methods and Relation Based Super Resolution methods to construct from very low resolution images. This paper uses a learning based super resolution method to extract and construct images from very low resolution images. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.