• Korean Journal of Environment and Ecology
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• v.28 no.2
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• pp.253-262
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• 2014

This study was conducted to find methods of qualitative landscape assessment for vegetational landscapes using ecological analysis. The study site was Seoraksan National Park in Korea. For this study, differing unique landscape resources were categorized and identified according to ecosystems. After identifying the study areas, the relationship between trail visitor preference and the amount of visible overexposure caused by people to the resources was examined. Landscape resources chosen for ecological analysis at Seoraksan National Park were subalpine vegetation community, high mountain rocks, ombrogenous deciduous broadleaf forest in the valley area, edaphic climax community, big tree community, flowering tree dominant community, autumnal tree dominant community and needle-leaf forest in the subalpine area. As a result of the study, it was found that the landscape resources with the highest correlation to visitor trail preference were big tree community area, flowering tree dominant community area, and needle-leaf forest in the subalpine area. As a result of overlapping the analysis of the amount of visible overexposure to the landscape resources and the analysis of preferential use of trails by visitors, guidance for the appropriate season for each trail can be provided. Since a positive correlation exists between certain sections of the natural landscapes and visitor preference, ecological impact on landscape resource ecosystems did not appear to cover wide areas of the trails, but was limited to certain areas preferred by visitors.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.