• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.190-202
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• 2021

Background: Landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and texture-based measures as alternative indices in measuring urban heterogeneity effects on LST at multiple scales. Results: The statistical results showed that the correlation between urban landscape heterogeneity and LST increased as the spatial extent (scale) of under-study landscapes increased. Overall, landscape metrics showed that the less fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was seen between edge density (ED) and LST (r = - 0.47) at the sub-region scale. Texture measures showed a stronger relationship (R² = 34.84% on average) with LST than landscape metrics (R² = 15.33% on average) at all spatial scales, meaning that these measures had a greater ability to describe landscape heterogeneity than the landscape metrics. Conclusion: This study suggests alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into urban design.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.262-265
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• 2012

In this paper we propose a new deinterlacing method with block classification and motion vector smoothing. The proposed method classifies a block, then depending on the region it belongs to, the motion estimation or line averaging is applied. To classify a block its variance is calculated. Then, for those blocks that belong to simple non-texture region the line averaging is done while motion estimation is applied to complex region. The motion vector field is smoothed using median filter what yields more accurate interpolation. In the experiments for the subjective evaluation, the proposed method bas shown satisfying results in terms of computation time consumption and peak signal-to-noise ratio. Due to the simplicity of the algorithm computation time was drastically decreased.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1245-1254
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• 2020

In this study, the limitations of the existing water level detection algorithm using CCTV images were recognized and the water level detection algorithm was improved by applying the Region growing technique. It applied three techniques (Horizontal projection profile, Texture analysis, and Optical flow) to estimate the water area, and the results were analyzed in a comprehensive analysis to select the initial water area. The water level was then continuously detected by the Region growing technique, referring to the initial water area. As a result, it was possible to confirm that the exact level of water was detected without being affected by environmental factors compared to the existing level detection algorithm, which had frequent mis-detection phenomena depending on the surrounding environmental factors. In addition, the water level was detected in the video showing flooded roads in urban areas, not in the video of the river. These results are believed to be able to supplement the difficulty of monitoring at all times with limited manpower by automatically detecting the level of water through numerous CCTV footage installed throughout the country, and to contribute to laying the foundation for preventing disasters caused by torrential rains and typhoons in advance.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.632-642
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• 2005

This paper presents a new vertex selection scheme for the polygon-based contour approximation. In the proposed method, the entire contour is partitioned into partial segments and they are approximated adaptively with variable accuracy. The approximation accuracy of each segment is controlled based on its relative significance. By computing the relative significance with the texture degradation in the approximation error region, the visual quality enhancement in the reconstructed frames can be achieved under the same amount of the contour data. For this purpose, a decision rule for $d_{max}$ is derived based on inter-region contrasts. In addition, an adaptive vertex selection method using the derived rule is proposed. Experimental results are presented to show the superiority of the proposed method over conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1904-1926
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• 2016

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.777-785
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• 2017

In the modern real world, we can extract and recognize some texts to get a lot of information from the scene containing them, so the techniques for extracting and recognizing text areas from a scene are constantly evolving. They can be largely divided into texture-based method, connected component method, and mixture of both. Texture-based method finds and extracts text based on the fact that text and others have different values such as image color and brightness. Connected component method is determined by using the geometrical properties after making similar pixels adjacent to each pixel to the connection element. In this paper, we propose a method to adaptively change to improve the accuracy of text region extraction, detect and correct the slope of the image using edge and image segmentation. The method only extracts the exact area containing the text by correcting the slope of the image, so that the extracting rate is 15% more accurate than MSER and 10% more accurate than EEMSER.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.11-24
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• 2008

Region-based image retrieval techniques, which divide image into similar regions having similar characteristics and examine similarities among divided regions, were proposed to support an efficient low-dimensional color indexing scheme. However, color image segmentation techniques are required additionally. The problem of segmentation is difficult because of a large variety of color and texture. It is known to be difficult to identify image regions containing the same color-texture pattern in natural scenes. In this paper we propose an automatic color image segmentation algorithm. The colors in each image are first quantized to reduce the number of colors. The gray level of image representing the outline edge of image is constructed in terms of Fisher's multi-class linear discriminant on quantized images. The gray level of image is transformed into a binary edge image. The edge showing the outline of the binary edge image links to the nearest edge if disconnected. Finally, the final segmentation image is obtained by merging similar regions. In this paper we design and implement a region-based image retrieval system using the proposed segmentation. A variety of experiments show that the proposed segmentation scheme provides good segmentation results on a variety of images.

• Journal of architectural history
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• v.8 no.2 s.19
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• pp.109-118
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• 1999

The purpose of this paper is to apply Stephen C. Pepper's contextualism to architecture: to interpret the former in the light of architectural theory, and ultimately to liberate architecture from the Western 'Idea' and return it to its context. The major concepts of Pepper used in the paper are quality, texture, spread, change, fusion, strand and context. Pepper's contextualism makes us realize that architecture cannot be separated from its context where human beings, history, neighborhood, and nature are all interpenetrating, and create a quality. Contextualism thus teaches us to make an effort to understand the region where we belong, and to create an architectural device that interrelates form and function of an architecture with its space-time environment, or its strand, texture and context.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.255-257
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• 2008

본 논문에서는 최근 IT 기술의 발전에 따라 무수히 양산되고 있는 멀티미디어 데이터를 효율적으로 검색하기 위한 방법을 제안한다. 영상 검색 시스템에 사용되는 데이터베이스(DB) 영상들에 존재하는 각 객체들의 존재 영역을 기반으로 질의 영상 (query image)의 객체 영역을 추정해서 검색에 활용하는 것이다. 이는 질의 영상의 전체 영역으로부터 객체를 추정하는 것보다 데이터베이스 영상들로부터 추출한 통계적 객체 분포 범위를 기반으로 추정하기 때문에 빨리 객체 추출이 가능하도록 한다. 따라서 객체를 추출하기 위한 배경 지식이나, 사용자 입력이 전혀 필요 없다. 이렇게 추출된 객체 영역의 영상들로부터 GLCM 알고리즘을 이용해서 객체 영역의 특성이 잘 반영된 질감 특징 값을 바탕으로 검색에 활용 할 경우 원본 영상의 질감 특징을 활용한 경우보다, 객체의 질감 특징을 더 잘 반영한다는 것을 실험을 통해 확인할 수 있었다.