• Title/Summary/Keyword: Cover image

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A Study on Large Surface TPS Reinforced Cover Glass Grinding Process Development (대면적 TPS 강화 Cover Glass Grinding 공정 개발에 관한 연구)

  • Lee, Taeho;Song, Jaichul;Lee, Seonhee
    • Journal of Satellite, Information and Communications
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    • v.10 no.3
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    • pp.48-52
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    • 2015
  • In this paper, we develops a domestic and foreign first reinforced cover glass grinding process for large surface TPS and resolves a problem of conventional cover glass grinding process through a research. Also, it is developed in priority to improve a productivity and processing quality emphasis. The development process develops a total process technology through a production and operation of demonstration unit. so, in this paper, we secured cover glass grinding process technology, image processing technology, mechanisms and control algorithms.

An Improved Interpolation Method using Pixel Difference Values for Effective Reversible Data Hiding (효과적인 가역 정보은닉을 위한 픽셀의 차이 값을 이용한 개선된 보간법)

  • Kim, Pyung Han;Jung, Ki Hyun;Yoon, Eun-Jun;Ryu, Kwan-Woo
    • Journal of Korea Multimedia Society
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    • v.24 no.6
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    • pp.768-788
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    • 2021
  • The reversible data hiding technique safely transmits secret data to the recipient from malicious attacks by third parties. In addition, this technique can completely restore the image used as a transmission medium for secret data. The reversible data hiding schemes have been proposed in various forms, and recently, the reversible data hiding schemes based on interpolation are actively researching. The reversible data hiding scheme based on the interpolation method expands the original image into the cover image and embed secret data. However, the existing interpolation-based reversible data hiding schemes did not embed secret data during the interpolation process. To improve this problem, this paper proposes embedding the first secret data during the image interpolation process and embedding the second secret data into the interpolated cover image. In the embedding process, the original image is divided into blocks without duplicates, and the maximum and minimum values are determined within each block. Three way searching based on the maximum value and two way searching based on the minimum value are performed. And, image interpolation is performed while embedding the first secret data using the PVD scheme. A stego image is created by embedding the second secret data using the maximum difference value and log function in the interpolated cover image. As a result, the proposed scheme embeds secret data twice. In particular, it is possible to embed secret data even during the interpolation process of an image that did not previously embed secret data. Experimental results show that the proposed scheme can transmit more secret data to the receiver while maintaining the image quality similar to other interpolation-based reversible data hiding schemes.

SEGMENTATION-BASED URBAN LAND COVER HAPPING FROM KOMPSAT EOC IMAGES

  • Florian P, Kressler;Kim, Youn-Soo;Klaus T, Steinnocher
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • 2003.04a
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    • pp.588-595
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    • 2003
  • High resolution panchromatic satellite images collected by sensors such as IRS-1C/D and KOMPSAT-1 have a spatial resolution of approximately 6 ${\times}$ 6 ㎡, making them very attractive for urban applications. However, the spectral information present in these images is very limited. In order to overcome this limitation, an object-oriented classification approach is used to identify basic land cover types in urban areas. Before an image can be classified it is segmented at different aggregation levels using a multiresolution segmentation approach. In the course of this segmentation various statistical as well as topological information is collected for each segment. Based on this information it is possible to classify image objects and to arrive at much better results than by looking only at single pixels. Using an image recorded by KOMPSAT-1 over the City of Vienna a land cover classification was carried out for two areas. One was used to set up the rules for the different land cover types. The second subset was classified based on these rules, only adjusting some of the functions governing the classification process.

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A Study of Land-Cover Classification Technique for Merging Image Using Fuzzy C-Mean Algorithm (Fuzzy C-Mean 알고리즘을 이용한 중합 영상의 토지피복분류기법 연구)

  • 신석효;안기원;양경주
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.22 no.2
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    • pp.171-178
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    • 2004
  • The advantage of the remote sensing is extraction the information of wide area rapidly. Such advantage is the resource and environment are quick and efficient method to grasps accurately method through the land cover classification of wide area. Accordingly this study was presented more better land cover classification method through an algorithm development. We accomplished FCM(Fuzzy C-Mean) classification technique with MLC (Maximum Likelihood classification) technique to be general land cover classification method in the content of research. And evaluated the accuracy assessment of two classification method. This study is used to the high-resolution(6.6m) Electro-Optical Camera(EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1(KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer(MODIS) image data(36 bands).

An Improved Data Hiding Algorithm for Increasing Hidden Capacity

  • Dae-Jea Cho
    • International Journal of Internet, Broadcasting and Communication
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    • v.16 no.1
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    • pp.11-16
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    • 2024
  • Illegal manipulation and alteration of digital content are becoming a social issue. To address this problem, there is an increasing demand for the development of technologies to prevent the manipulation and alteration of digital content. This paper proposes a data hiding algorithm capable of embedding a larger amount of confidential data in the original cover image while minimizing the degradation of image quality in stego images. The algorithm presented in this paper analyzes the characteristics of the image to extract corner points and then uses a method to insert watermarks around these extracted corner points. Additionally, through experimentation, it has been proven that this algorithm can insert a greater amount of watermark without degrading the image quality compared to other existing algorithms.

Improving of land-cover map using IKONOS image data (IKONOS 영상자료를 이용한 토지피복도 개선)

  • 장동호;김만규
    • Spatial Information Research
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    • v.11 no.2
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    • pp.101-117
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    • 2003
  • High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

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Automatic selection method of ROI(region of interest) using land cover spatial data (토지피복 공간정보를 활용한 자동 훈련지역 선택 기법)

  • Cho, Ki-Hwan;Jeong, Jong-Chul
    • Journal of Cadastre & Land InformatiX
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    • v.48 no.2
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    • pp.171-183
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    • 2018
  • Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

Land Cover Classification Using Lidar and Optical Image (라이다와 광학영상을 이용한 토지피복분류)

  • Cho Woo-Sug;Chang Hwi-Jung;Kim Yu-Seok
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.24 no.1
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    • pp.139-145
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    • 2006
  • The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

Reversible Data Hiding Technique using Encryption Technique and Spatial Encryption Technique (암호화 기법 및 공간적인 암호화 기법을 사용한 가역 데이터 은닉기법)

  • Jung, Soo-Mok
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.1
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    • pp.632-639
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    • 2021
  • In this paper, we proposed a reversible data hiding technique that greatly enhances the security of confidential data by encrypting confidential data and then spatially encrypting the encrypted confidential data and hiding it in the cover image. When a result image is generated by hiding the encrypted confidential data in the cover image using a spatial encryption technique, the quality of the result image is very good, and the original cover image and the result image cannot be visually distinguished. Since the encrypted confidential data is spatially encrypted and concealed, it is not possible to know where the encrypted confidential data is concealed in the result image, and the encrypted confidential data cannot be extracted from the result image. Even if the encrypted confidential data is extracted, the original confidential data is not known because the confidential data is encrypted. Therefore, if confidential data is concealed in images using the proposed technique, the security of confidential data is greatly improved. The proposed technique can be effectively used in medical and military applications.

A Study on Object-Based Image Analysis Methods for Land Cover Classification in Agricultural Areas (농촌지역 토지피복분류를 위한 객체기반 영상분석기법 연구)

  • Kim, Hyun-Ok;Yeom, Jong-Min
    • Journal of the Korean Association of Geographic Information Studies
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    • v.15 no.4
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    • pp.26-41
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    • 2012
  • It is necessary to manage, forecast and prepare agricultural production based on accurate and up-to-date information in order to cope with the climate change and its impacts such as global warming, floods and droughts. This study examined the applicability as well as challenges of the object-based image analysis method for developing a land cover image classification algorithm, which can support the fast thematic mapping of wide agricultural areas on a regional scale. In order to test the applicability of RapidEye's multi-temporal spectral information for differentiating agricultural land cover types, the integration of other GIS data was minimized. Under this circumstance, the land cover classification accuracy at the study area of Kimje ($1300km^2$) was 80.3%. The geometric resolution of RapidEye, 6.5m showed the possibility to derive the spatial features of agricultural land use generally cultivated on a small scale in Korea. The object-based image analysis method can realize the expert knowledge in various ways during the classification process, so that the application of spectral image information can be optimized. An additional advantage is that the already developed classification algorithm can be stored, edited with variables in detail with regard to analytical purpose, and may be applied to other images as well as other regions. However, the segmentation process, which is fundamental for the object-based image classification, often cannot be explained quantitatively. Therefore, it is necessary to draw the best results based on expert's empirical and scientific knowledge.