• Title/Summary/Keyword: Image grouping

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Clustering of HIRIS data

  • Huan, Nguyen Van;Kim, Hakil;Kim, Sun-Hwa;Lee, Kyu-Sung
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.299-300
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    • 2007
  • Along with the development of imaging sensors, hyperspectral imaging technology is growing rapidly and contributing to many fields of science nowadays. However, the bulky size and complex structure make it difficult to be processed. Focused on in this paper is the clustering utility, implemented in HYVEW, a program involving tools and functions to manipulate with hyperspectral images. The clustering process aims to partition the surface of the imaged area into subregions by grouping the spectra subject to the similarity of spectra.

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Power Line Extraction based on thermal Image (열영상 기반의 전력선검출)

  • Kim, Dong-Wook
    • The Journal of the Korea institute of electronic communication sciences
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    • v.5 no.3
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    • pp.245-250
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    • 2010
  • In this paper, an efficient extraction technique of power lines is proposed. Power line extraction technique is used to operate unmanned autonomous vehicles. To achieve the extraction of power lines, an angle space segmentation and grouping method of Hough transform are developed. In simulation results for the proposed algorithm, our algorithm shows a good detection results in experiments for several aerial images which contain different line components.

Piecewise Image Denoising with Multi-scale Block Region Detector based on Quadtree Structure (쿼드트리 기반의 다중 스케일 블록 영역 검출기를 통한 구간적 영상 잡음 제거 기법)

  • Lee, Jeehyun;Jeong, Jechang
    • Journal of Broadcast Engineering
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    • v.20 no.4
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    • pp.521-532
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    • 2015
  • This paper presents a piecewise image denoising with multi-scale block region detector based on quadtree structure for effective image restoration. Proposed piecewise image denoising method suggests multi-scale block region detector (MBRD) by dividing whole pixels of a noisy image into three parts, with regional characteristics: strong variation region, weak variation region, and flat region. These regions are classified according to total pixels variation between multi-scale blocks and are applied principal component analysis with local pixel grouping, bilateral filtering, and structure-preserving image decomposition operator called relative total variation. The performance of proposed method is evaluated by Experimental results. we can observe that region detection results generated by the detector seems to be well classified along the characteristics of regions. In addition, the piecewise image denoising provides the positive gain with regard to PSNR performance. In the visual evaluation, details and edges are preserved efficiently over the each region; therefore, the proposed method effectively reduces the noise and it proves that it improves the performance of denoising by the restoration process according to the region characteristics.

MONITORING OF MOUNTAINOUS AREAS USING SIMULATED IMAGES TO KOMPSAT-II

  • Chang Eun-Mi;Shin Soo-Hyun
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.653-655
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    • 2005
  • More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

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CCSDS PN PROCESSING SPEED OPTIMIZATION

  • Ahn, Sang-Il;Kim, Tae-Hoon;Koo, In-Hoi
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.537-539
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    • 2007
  • Telemetry processing system requires minimum bit transition level in data streams to maintain a bit synchronization while receiving telemetry signal. PN code has a capability of providing the bit transition and is widely used in the packet communication of CCSDS. CCSDS PN code that generator polynomial is $h(x)=x^{8}+x^{7}+x^{5}+x^{3}+1$, and the random bit sequence that is generated from this polynomial is repeated with the cycle of 255 bits. As the resolution of satellite image increases, the size and transmission rate of data increases. To process of huge and bulky size of satellite image, the speed of CCSDS PN Processing is very important. This paper introduces the way of improving the CCSDS PN Processing speed through processing 128 bits at one time using the feature of cyclic structure that repeats after first 255 bytes by grouping the random bit sequence with 1 byte and Intel Streaming SIMD Extensions 2. And this paper includes the comparison data of processing speed between SSE2-applied implementation and not-applied implementation, in addition, the measured value of speed improvement.

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Effects Psychological Response Light & Color - Focusing on Experimental Application of 3D Image- (빛과 색이 심리적 반응에 미치는 영향에 관한 연구 - 주거공간 3D이미지의 실험적 적용을 중심으로 -)

  • Yoon Gab-Geun;Kang Kyoung-Won;Jung Sa-Hee
    • Korean Institute of Interior Design Journal
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    • v.14 no.3 s.50
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    • pp.199-207
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    • 2005
  • We recognize analysing and quantifying an effect of light and colors on people' psychological response changing according to environmental conditions as important. Therefore, this study intends to improve efficiency in realizing emotional design that contributes to creation of helpful and pleasant interior using architecture that is able to refresh human emotion or light and colors which are important factors in interior design. For the purpose, this study analyses an effect of colors in interior space and the effect of interaction between light and color through modeling to identify types of psychological response. And it is to present a generalized conclusion through an analysis of meaning of the two effects. The ultimate goal of this study is to identify the value and possibility of actual design. This study measures response on questionnaire through representative vocabulary by abstracting based on evaluation image selected through the 1st and 2nd preparatory research and grouping similar words. In next step, images represented both by light and colors are presented as virtual space and for data input and analysis based on psychological response corresponding to each image, this study uses SPSS 11.0 statistical package program to analyse data collected. The space to be experimented is a livingroom, a center of residential area.

Efficient Object Classification Scheme for Scanned Educational Book Image (교육용 도서 영상을 위한 효과적인 객체 자동 분류 기술)

  • Choi, Young-Ju;Kim, Ji-Hae;Lee, Young-Woon;Lee, Jong-Hyeok;Hong, Gwang-Soo;Kim, Byung-Gyu
    • Journal of Digital Contents Society
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    • v.18 no.7
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    • pp.1323-1331
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    • 2017
  • Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.

Superpixel Segmentation Scheme Using Image Complexity (영상의 복잡도를 고려한 슈퍼픽셀 분할 방법)

  • Park, Sanghyun
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.12
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    • pp.85-92
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    • 2018
  • When using complicated image processing algorithms, we use superpixels to reduce computational complexity. Superpixel segmentation is a method of grouping pixels having similar characteristics into one group. Since superpixel is used as a preprocessing of image processing, it should be generated quickly, and the edge components of the image should be well preserved. In this paper, we propose a method of generating superpixels with a small amount of computation while preserving edge components well. In the proposed method, superpixels of an image are generated by using the existing k-mean method, and similar superpixels among the generated superpixels are merged to make final superpixels. When merging superpixels, the similarity is calculated only for superpixels. Therefore, the amount of computation is maintained small. It is shown by experimental results that the superpixel images produced by the proposed method are conserving edge information of the original image better than those produced by the existing method.

A Study on Characteristics of Pink Color and Fashion Images Used in Gender Neutral Men's Fashion (젠더 뉴트럴 남성 패션에 사용된 핑크색의 특성과 패션이미지 분석)

  • Hong, YunJung;Joo, Mi Young
    • Journal of Fashion Business
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    • v.24 no.5
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    • pp.52-71
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    • 2020
  • This study examines the color characteristics of the usage of the color pink in menswear by analyzing its usage status and method. It involves an empirical research method establishing the frame of the study through a document study centered on trend, gender neutral considerations, and the utilization of the color pink in men's fashion, by analyzing the characteristics of color and tone by extracting the pink color shown in menswear collections as well as analyzing and categorizing the fashion image and genderless characteristics. Analyzing the color and tone of the pink color shown indicate that bright, light and pale tones had higher proportions. Pink color can also be said to be utilized as a design element that gives off a younger and more vital color image in menswear. Further, the use of brighter and softer pink colors can be interpreted as reflecting modern society's demands of masculinity to change into a more sophisticated and soft image. To analyze the characteristics of the color pink utilized in gender neutral fashion, fashion images were presented as the analysis standard. An image grouping technique was used to classify pink while utilizing genderless types-fashion style. The result showed that even with the same pink color, the fashion image can vary with different methods of expression in terms of clothes and styling. The results of this study can serve as basic data for planning fashion design concepts as it analyzed pink-using fashion images and the genderless concept type.

A Setting of Initial Cluster Centers and Color Image Segmentation Using Superpixels and Fuzzy C-means(FCM) Algorithm (슈퍼픽셀과 FCM을 이용한 클러스터 초기값 설정 및 칼라영상분할)

  • Lee, Jeong-Hwan
    • Journal of Korea Multimedia Society
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    • v.15 no.6
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    • pp.761-769
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    • 2012
  • In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.