• Science of Emotion and Sensibility
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• v.17 no.3
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• pp.3-14
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• 2014

Since the domestic introduction of iPhone in 2009, use of smartphones rapidly increased and many tasks, previously performed by various devices, are now performed by smartphones. In this process the importance of reading little text using small smartphone screen has become highly significant. This research tested how display factors of smartphone (pixel density, sub-pixel structure, luminance) and environmental factor (illumination) affect legibility related discomfort in text reading. The results indicated that legibility related discomfort is largely affected by pixel density, where people experience inconvenience when the pixel density becomes lower than 300 PPI. Illumination has limited effect on legibility related discomfort. Participants reported more legibility related discomfort when stimulus presented in various levels of illumination rather than single illumination level. Sub-pixel structure and luminance did not affected legibility related discomfort. Based on the results we suggest lower limit resolution of smart devices (smartphones, tablet computers) of different sizes for text legibility.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.202-205
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• 2008

건물이 밀집된 도심지역을 촬영한 두 시기 항공영상에 화소기반 변화탐지 기법인 영상대차(Image Differencing), 영상중첩 분석(Image Overlay)기법을 적용하여 넓은 대도심지역의 효율적인 변화탐지 가능성을 살펴보았다. 영상대차(Image Differencing) 기법은 알고리즘이 간단하고 정량적인 분석이 가능한 결과를 얻을 수 있다는 장점이 있으나 고층건물밀집지역을 보여주고 있는 고해상도 항공영상의 적용과정에서는 폐색영역, 그림자 등으로 인해 정확한 변화탐지 결과를 보여주지 못했다. 영상중첩 분석(Image Overlay)기법은 한 번에 두 개 또는 세 개의 영상을 비교 분석할 수 있다는 장점이 있으나 직관적인 분석만을 제공하고 정량적인 분석이 불가능하였다. 현재의 화소기반 영상변화탐지 기술수준으로는 고해상도 공간영상에 대한 신뢰도 높은 변화탐지 분석결과를 얻을 수 없다는 것을 확인하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.129-136
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• 1999

The quality of image classification results is not always uniform over entire image. Thus, this study proposes the concept of ISDd (Index of Spatial Distribution by distance) and ISDs (ISD by scatteredness) for the evaluation of unevenness of result quality, and spatial distribution of satellite image classification errors. The ISDd is indexed mean distance of misclassified pixels and the ISDs is statistical indicator of scatteredness of misclassified pixels. In this study, the ISDd and the ISDs are calculated and evaluated for some satellite images, then misclassified area is extracted and the reasons of misclassification are examined. As the result of this study, using both the ISDd and the ISDs, the basis of decision on adoption/rejection of classification results is offered at sub-image level by evaluation of the local aggregation of misclassified pixels. Using Index of Spatial Distribution. as well as overall classification accuracy, users can understand the spatial distribution of misclassified pixels, and can have the additional criterion of the judgement on suitability and reliability of classification results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.915-917
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• 2011

In this paper, a comparison of superpixel characteristics for each color feature space. The superpixel is consist of several pixels with same features such as luminance, color, textures etc. The superpixel can be used on image processing and analysis with large image size to speed up the process. We compare the superpixel characteristics by means of compactness using Berkeley image database(BSD-300).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.225-231
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• 1999

In statistical analysis of remotely sensed data, means and variances of each classes are used as the basis of statistical similarity determination. Therefore, the overall accuracy of classification is affected by the training results. It is assumed that the ideal distributions of pixel values follow normal distributions, but practically they have some aggregations and biases. non anomalies of distribution can affect the classification results greatly as well as the variances of training results. In this study, relationships between the inferential variances of the training sets and the distributions of pixel values are examined. and the resulting changes of classification results are studied. Furthermore, the training method which minimizes the effect of underestimation of variances is proposed.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.11-20
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• 2009

Due to increasing frequency of yellow dust, not to mention high rate of precipitation and cloud formation in summer season of Korea, atmospheric correction of satellite remote sensing is necessary. This research analyzes the effect of atmospheric correction has on imagery classification by comparing DN distribution before and after atmospheric correction. The image used in the research is LANDSAT-5 TM. As for atmospheric correction module, commercial product ATCOR, FLAASH as well as COST model released on the internet, were used. The result of experiment shows that class separability increased in building areas.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.1-11
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• 2000

Multi-temporal data have been used frequently for analyzing dynamic characteristics of ecological environment. Little research, however, shows the characteristics and problems of the analysis of continental- or global-scale, multi-temporal satellite data. This research investigated the characteristics of large-area, multi-temporal data analysis and the problems of phenological difference of ground vegetation and scarcity of training data for a long term period. This research suggested a latitudinal image segmentation method and an invariant pixel method. As an application, the image segmentation and invariant pixel methods were applied to a set of AVHRR data covering most part of Asia from 1982 to 1993. Fuzzy classification results showed the decrease of forests and the increase of croplands at densely populated areas, however an opposite trend was detected at sparsely populated or depopulated areas.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.44-52
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• 2010

In this paper, we propose a novel method for face recognition which uses polar coordinate instead of conventional cartesian coordinate. Among the central area of a face, we select a point as a pole and make a polar image of a face by evenly sampling pixels in each direction of 360 degrees around the pole. By applying conventional feature extraction methods to the polar image, the recognition rates are improved. The polar coordinate delineates near-pole area more vividly than the area far from the pole. In a face, important regions such as eyes, nose and mouth are concentrated on the central part of a face. Therefore, the polar coordinate of a face image can achieve more vivid representation of important facial regions compared to the conventional cartesian coordinate. The proposed polar coordinate transform was applied to Yale and FRGC databases and LDA and NLDA were used to extract features afterwards. The experimental results show that the proposed method performs better than the conventional cartesian images.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.