• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.375-382
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• 2016

In this paper, we presents a signatures verification by using the nonlinear quantization histogram of polar coordinate based on multi-dimensional adjacent pixel intensity difference. The multi-dimensional adjacent pixel intensity difference is calculated from an intensity difference between a pair of pixels in a horizontal, vertical, diagonal, and opposite diagonal directions centering around the reference pixel. The polar coordinate is converted from the rectangular coordinate by making a pair of horizontal and vertical difference, and diagonal and opposite diagonal difference, respectively. The nonlinear quantization histogram is also calculated from nonuniformly quantizing the polar coordinate value by using the Lloyd algorithm, which is the recursive method. The polar coordinate histogram of 4-directional intensity difference is applied not only for more considering the corelation between pixels but also for reducing the calculation load by decreasing the number of histogram. The nonlinear quantization is also applied not only to still more reflect an attribute of intensity variations between pixels but also to obtain the low level histogram. The proposed method has been applied to verified 90(3 persons * 30 signatures/person) images of 256*256 pixels based on a matching measures of city-block, Euclidean, ordinal value, and normalized cross-correlation coefficient. The experimental results show that the proposed method has a superior to the linear quantization histogram, and Euclidean distance is also the optimal matching measure.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.21-26
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• 2017

In this paper, an USB-OTG (Uiversal Serial Bus On-the-go) interface module was designed with the iris information for personal identification. The image recognition algorithm which was searching face and iris areas, was proposed with pixel differences from eye blinking after several facial images were captured and then detected without any activities like as pressing the button of smart phone. The region of pupil and iris could be fast involved with the proper iris area segmentation from the pixel value calculation of frame difference among the images which were detected with two adjacent open-eye and close-eye pictures. This proposed iris recognition could be fast processed with the proper grid size of the eye region, and designed with the frame difference between the adjacent images from the USB-OTG interface with this camera module with the restrict of searching area in face and iris location. As a result, the detection time of iris location can be reduced, and this module can be expected with eliminating the standby time of eye-open.

• Smart Media Journal
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• v.4 no.3
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• pp.50-55
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• 2015

In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.339-349
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• 2018

The massive green tide occurred every summer in the Yellow Sea since 2008, and many studies are being actively conducted to estimate the coverage of green tide through analysis of satellite imagery. However, there is no satellite images selection criterion for accurate coverage calculation of green tide. Therefore, this study aimed to find a suitable satellite image from for the comparison of the green tide coverage according to the spatial resolution of satellite image. In this study, Landsat ETM+, MODIS and GOCI images were used to coverage estimation and its spatial resolution is 30, 250 and 500 m, respectively. Green tide pixels were classified based on the NDVI algorithm, the difference of the green tide coverage was compared with threshold value. In addition, we estimate the proportion of the green tide in one pixel through the Linear Spectral Unmixing (LSU) method, and the effect of the difference of green tide ratio on the coverage calculation were evaluated. The result of green tide coverage from the calculation of the NDVI value, coverage of green tide usually overestimate with decreasing spatial resolution, maximum difference shows 1.5 times. In addition, most of the pixels were included in the group with less than 0.1 (10%) LSU value, and above 0.5 (50%) LSU value accounted for about 2% in all of three images. Even though classified as green tide from the NDVI result, it is considered to be overestimated because it is regarded as the same coverage even if green tide is not 100% filled in one pixel. Mixed-pixel problem seems to be more severe with spatial resolution decreases.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.1
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• pp.37-43
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• 2001

In this paper, we propose a new optical security technique using two phase masks based on interferometer. A binary random phase image is used as a reference image and the encrypted image is generated according to the phase difference between the reference image and the original image. If there is no phase difference of a same pixel position in two phase masks, interference intensity of the pixel has minimum value and if phase difference of a same pixel position in two phase masks is $\pi$, its interference intensity has maximum value. We can decrypt the original image by putting two phase masks on each of the two optical paths of the Mach-Zehnder interferometer. Computer simulation and the optical experiments show a good performance of the proposed optical security system.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2565-2574
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• 2000

The error diffusion method is good for reproducing continuous image to binary image. However the reproduction of edge characteristic is weak in power spectrum analysis of display error. In this paper. we present an edge-enhanced error-diffusion method which include pre-processing algorithm for edge characteristic enhancement. Pre-processing algorithm consists of the difference value between current pixel and local average of original image and weighting function of pre-filter. First. it is obtained the difference value between current pixel and the local average of peripheral pixels(5x5) in original image. Second, weighting function of pre-filter is composed by function including absolute value and sign of difference value. The improved Error diffusion algorithm using pre-processing algorithm, present a good result visually which edge characteristic is enhanced. The performance of the proposed algorithm is compared with that of the conventional edge-enhanced error diffusion by measuring the RAPSD of display error, the egde correlation and the local average accordance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.609-615
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• 2020

In the modern society, the use of digital equipment is increasing along with the 4th industrial revolution, and the importance of image and signal processing is increasing. At the same time, research on noise reduction is being actively conducted. In this paper, we propose a switching filter algorithm for random-valued impulse noise cancellation. The proposed algorithm obtains the threshold value by determining the noise level present in the image, and threshold value is compared with the difference between the input pixel value and the reference value, and is used in the weight switching process of the filter. The final output of the filter is estimated by applying a pixel weight and a modified weight median filter according to the switching, and obtains a final output by comparing the estimated value with the input pixel value. To evaluate the performance of the proposed algorithm, we compared it with the existing methods using simulation and PSNR.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.99-107
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• 2019

Retinex-based image enhancement is a technique that utilizes the property that the human visual characteristics are sensitive to the difference from the surrounding pixel value rather than the pixel value itself. These Retinex-based algorithms show different characteristics of the improved image depending on the applied color space or gamma correction. In this paper, we set eight different experimental conditions according to the application of color space and gamma correction, and analyze the objective and subjective performance of each Retinex based image enhancement algorithm and apply it to the implementation of Retinex based algorithm. In the case of gamma correction, quantitative low entropy images and low contrast images are obtained. The application of Retinex technique in HSI color space rather than RGB color space is found to be high in overall subjective image quality as well as maintaining color.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.556-566
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• 2001

This paper proposes a motion-adaptive de-interlacing algorithm based on EBMF(Edge Based Median Filter) and AMPDF(Adaptive Minimum Pixel Difference Fillet). To compensate 'motion missing'error, which is an important factor in motion-adaptive methods, we used AMPDF which estimates an accurate value using different thresholds after classifying the input image to 4 classes. To efficiently interpolate the moving diagonal edge, we also used EBMF which selects a candidate pixel according to the edge information. Finally, we, to increase the performance, adopted an adaptive interpolation after classifying the input image to moving region, stationary region, and boundary region. Simulation results showed that the proposed method provides better performance than the existing methods.