• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.599-607
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• 2021

Inactive region means a region filled with invalid pixel values to represent a specific image. Generally, inactive regions are occurred when the non-rectangular formatted images are converted to the rectangular shaped image, especially when 3D images are represented in 2D format. Because these inactive regions highly degrade the compression efficiency, filtering approaches are often applied to the boundaries between active and inactive regions. However, the image characteristics are not carefully considered during filtering. In the proposed method, inactive regions are padded through reinforcement learning that can consider the compression process and the image characteristics. Experimental results show that the proposed method performs an average of 3.4% better than the conventional padding method.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.846-855
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• 2007

Significant feature extraction in cancer cell image analysis is an important process for grading cell carcinoma. In this study, we propose a method for 3D quantitative analysis of cell nuclei based upon digital image cytometry. First, we acquired volumetric renal cell carcinoma data for each grade using confocal laser scanning microscopy and segmented cell nuclei employing color features based upon a supervised teaming scheme. For 3D visualization, we used a contour-based method for surface rendering and a 3D texture mapping method for volume rendering. We then defined and extracted the 3D morphological features of cell nuclei. To evaluate what quantitative features of 3D analysis could contribute to diagnostic information, we analyzed the statistical significance of the extracted 3D features in each grade using an analysis of variance (ANOVA). Finally, we compared the 2D with the 3D features of cell nuclei and analyzed the correlations between them. We found statistically significant correlations between nuclear grade and 3D morphological features. The proposed method has potential for use as fundamental research in developing a new nuclear grading system for accurate diagnosis and prediction of prognosis.

• Journal of the Korean earth science society
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• v.36 no.4
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• pp.330-340
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• 2015

For this study, we developed an algorithm to estimate the total amount of clouds using sky image data from the Skyviewer equipped with CCD camera. Total cloud amount is estimated by removing mask areas of RGB (Red Green Blue) images, classifying images according to frequency distribution of GBR (Green Blue Ratio), and extracting cloud pixels from them by deciding RBR (Red Blue Ratio) threshold. Total cloud amount is also estimated by validity checks after removing sunlight area from those classified cloud pixels. In order to verify the accuracy of the algorithm that estimates total cloud amount, the research analyzed Bias, RMSE, and correlation coefficient compared to records of total cloud amount earned by human observation from the Gangwon Regional Meteorological Administration, which is in the closest vicinity of the observation site. The cases are selected four daily data from 0800 LST to 1700 LST for each season. The results of analysis showed that the Bias in total cloud amount estimated by the Skyviewer was an average of -0.8 tenth, and the RMSE was 1.6 tenths, indicating the difference in total cloud amount within 2 tenths. Also, correlation coefficient was very high, marking an average of over 0.91 in all cases, despite the distance between the two observation sites (about 4 km).

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.85-90
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• 1998

The most important step image processing is stereo matching process. That is finding pixels of 3 dimensional pair in the left and right image. There are two matching methods. One is an area based approach and the other is a feature based approach. An area based approach needs much calculation time. In the other hand, we have the advantage of calculation time in the feature based approach, but can not obtain matched data for all pixels in the image. In recent years, fuzzy image processing methods are developed to manage vagueness and noise in image and ambiguous, inconsistent knowledge in recognition step. In this paper, we propose a fuzzy stereo matching algorithm. This method converts brightness data of image to fuzzy membership value and processes an area based approach method for stereo matching algorithm. We experiment with some stereo images to validate effectiveness of this algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.32-39
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• 2009

In this paper, the method of region segmentation using genetic algorithm is proposed for an improvement of efficiency in moving picture coding. A genetic algorithm is the method that searches a large probing space using only a function value for a optimal combination consecutively. By progressing both motion presumption and region segmentation at once, we can assign the motion vector in a image to a small block or a pixel respectively, and transform the capacity of coding and a signal to noise rate into a problem of optimization. That is to say, there is close correlation between region segmentation and motion presumption in motion-compensated prediction coding. This is to optimize the capacity of coding and a S/N ratio. This is to arrange the motion vector in each block of picture according to the state of optimization. Therefore, we examined both the data type of genetic algorithm and the method of data processing to obtain the results of optimal region segmentation in this paper. And we confirmed the validity of a proposed method using the test pictures by means of computer simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.383-392
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• 2007

This study focuses on co-registration and band selection, which are one of the pre-processing steps to apply the change detection technique using hyperspectral images. We carried out automatic co-registration by using the SIFT algorithm which performance was already established in the computer vision fields, and selected the bands fur change detection by estimating the noise of image through the PIFs reflecting the radiometric consistency. The EM algorithm was also applied to select the band objectively. Hyperion images were used for the proposed techniques, and non-calibrated bands and striping noises contained in Hyperion image were removed. Throughout the results, we could develop the reliable co-registration procedure which coincided with accuracy within 0.2 pixels (RMSE) for change detection, and verified that band selection depending on the visual inspection could be objective by extracting the PIFs.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.105-111
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• 2018

This paper presents the analysis of image quality with FHD(Full HD) resolution camera equipped DIC(Digital Image Correlation) platform for the measurement of the architectural structure's relative displacement. DIC platform was designed based on i.MX6 of Freescale. Displacement measurement based on DIC method, the error is affected by image quality factors as pixel number, brightness, contrast, and SNR[dB](Signal to Noise Ratio). The effect were analyzed. The displacement of ROI(Region Of Interest) area within the image was measured by sub-pixel units based on DIC method. The non-contact telemetry property of DIC method, it can be used to long distance non-contact measurement. The various displacement results was measured and analyzed with the image quality factor adjustment according to the distance(25m, 35m, 50m).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1468-1473
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• 2018

The importance of communication and data processing is increasing with the advance of the Fourth Industrial Revolution. Hence, the importance of video and data processing technologies, which directly influence the accuracy and reliability of equipment, is also increasing. In this research report we propose an algorithm for calculating the final output by estimating the standard deviation and estimate required for removing AWGN while adapting to changes in the frequency factors of video. This algorithm calculates the final output by checking an estimated value against the effective pixel range, which is obtained from the standard deviation of mask factors. Subsequently, the weighted value is computed, taking into account the filter output. To evaluate the functionality of this algorithm, it is compared with the most-commonly used present method through simulation. The simulation results show that the important features of the image are preserved and efficient noise cancellation performance is demonstrated.