• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1016-1026
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• 1996

The purpose of this study was to extract protypes from features and concrete images of Lotus and Crane patterns. A qualitative and a quantitative methods were used. Qualitative informations were obtained from depth Interviews for pattern selection and feature extraction, and quantitative informations from a quail-experiment for pattern caregorization. The subjects were 20 female design students and non-design, students in Teajon. The results were summerized into a similarity metrix which was interpreted by the cluster analysis and the multi-dimensional scling(MDS). The patterns for the study were grouped into 8 clusters. Four dimensions were chosen for the MDS. The location of each pattern was visualized in a 2-dimesional spaces and the location of each cluster in a 3-dimensional spaces. The first dimension, "Lotus" vs "Crane" refired to pattern types, and the second dimension, "realistic" vs "transformable", the transformability. The third dimension, "simple" vs "complex", refired to the degree of simplification, and the forth dimension, "continuous" vs "discontinuous", continuity. The results of the Quantitative analysis could be summerized into 3-level prototype hiararchy In the first level, the patterns were devided clearly into two groups. Lotus and Crane by pattern types. In the second levelk, each group was devided into twosubgroups by continuity. In the third, each subgroup was divided into four subgroups by transformability and the degree of simplification. Four protypes, the final targets of the present study, were extracted from the third level. The are Stylized, Realistic, Decorative, Abstract types.d from the third level. The are Stylized, Realistic, Decorative, Abstract types.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1184-1192
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• 2004

A real-time pupil detection system that detects the pupil movement from the real-time video data achieved by the visual light camera for general purpose personal computer is proposed. It is implemented with three steps; at first, face region is detected using the Haar-like feature detection scheme, and then eye region is detected within the face region using the template-based scheme. Finally, pupil movement is detected within the eye region by convolution of the horizontal and vertical histogram profiling and Gaussian filter. As results, we obtained more than 90% of the detection rate from 2375 simulation images and the data processing time is about 160㎳, that detects 7 times per second.