• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.725-735
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• 2016

High-resolution satellite images are used in the fields of mapping, natural disaster forecasting, agriculture, ocean-based industries, infrastructure, and environment, and there is a progressive increase in the development and demand for the applications of high-resolution satellite images. Users of the satellite images desire accurate quality of the provided satellite images. Moreover, the distinguishability of each image captured by an actual satellite varies according to the atmospheric environment and solar angle at the captured region, the satellite velocity and capture angle, and the system noise. Hence , NIIRS must be measured for all captured images. There is a significant deficiency in professional human resources and time resources available to measure the NIIRS of few hundred images that are transmitted daily. Currently, NIIRS is measured every few months or even few years to assess the aging of the satellite as well as to verify and calibrate it [3]. Therefore, we develop an algorithm that can measure the national image interpretability rating scales (NIIRS) of a typical satellite image rather than an artificial target satellite image, in order to automatically assess its quality. In this study, the criteria for automatic edge region extraction are derived based on the previous works on manual edge region extraction [4][5], and consequently, we propose an algorithm that can extract the edge region. Moreover, RER and H are calculated from the extracted edge region for automatic edge region extraction. The average NIIRS value was measured to be 3.6342±0.15321 (2 standard deviations) from the automatic measurement experiment on a typical satellite image, which is similar to the result extracted from the artificial target.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.125-133
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• 2008

As society develops, the importance of safety for individuals and facilities in public places is getting higher. Not only the areas such as the existing parking lot, bank and factory which require security or crime prevention but also individual houses as well as general institutions have the trend to increase investment in guard and security. This study suggests face feature extract and the method to simply divide face region and head region that are import for face recognition by using color transform. First of all, it is to divide face region by using color transform of Y image of YIQ image and head image after dividing head region with K image among CMYK image about input image. Then, it is to extract features of face by using labeling after Log calculation to head image. The clearly divided head and face region can easily classify the shape of head and face and simply find features. When the algorism of the suggested method is utilized, it is expected that security related facilities that require importance can use it effectively to guard or recognize people.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.853-856
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• 2004

Image data processing on the region of interest (ROI) for providing the primary information is needed the view of saving search time and bandwidth over image communications related to web browsing, image database, and telemedicine, etc. Hence, the issue on extracting the region of interest is drawing a plenty of attention for the communication environment with a relatively low bandwidth such as mobile internet. In this paper, we propose a improved standard Maxshift method. The proposed algorithm compress image that includes multiple ROI using Maxshift method in Part 1 of JPEG2000. Simulation results show that proposed method increases PSNR vs. compression ratio performance above the Maxshift method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1207-1214
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• 2009

In this study, we propose a novel color-image segmentation algorithm to discern the teeth region utilizing RG intensity and its relevant RGB histogram features with resolving the variations of its maximum intensity in terms of peaks and valleys. Tooth candidates in a CCD image are first extracted by applying RGB color multi-threshold levels and consequently the successive morphological image operations and a Sobel-mask edge processing are performed to resolve the teeth region and its contour.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.187-193
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• 2021

In this paper, we propose an image watermarking technique that effectively hides confidential data in the LSB of image pixels by utilizing the characteristics of the image. In the proposed technique, the image is precisely divided into boundary surface and normal region other than the boundary surface and performs different processing. The boundary surface existing in the image is created by meeting different regions and contains important information of the image. One bit of confidential data is concealed in the LSB of the pixel at the boundary surface to preserve the characteristics of the boundary surface. In normal region other than the boundary surface, the pixel values are similar, and the change with the adjacent pixel values is smooth. Based on this property, even if the 2 bits of confidential data are hidden in the lower 2 bits of the pixel in the normal region, the difference cannot be visually distinguished. When confidential data is concealed in an image as described above, the amount of confidential data concealed in an image can be increased while maintaining excellent image quality. Concealing confidential data by applying the proposed method increases the amount of confidential data concealed by up to 84.6% compared to the existing method. The proposed technique can be effectively used for commercial image watermarking that hides copyright information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.866-872
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on run-length connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.59-62
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• 2009

In general, as a dynamic range of digital still camera is narrower than a real scene‘s, it is hard to represent the shadow region of scene. Thus, multi-scaled retinex algorithm is used to improve detail and local contrast of the shadow region in an image by dividing the image by its local average images through Gaussian filtering. However, if the chromatic distribution of the original image is not uniform and dominated by a certain chromaticity, the chromaticity of the local average image depends on the dominant chromaticity of original image, thereby the colors of the resulting image are shifted to a complement color to the dominant chromaticity. In this paper, a modified multi-scaled retinex method to reduce the influence of the dominant chromaticity is proposed. In multi-scaled retinex process, the local average images obtained by Gaussian filtering are divided by the average chromaticity values of the original image in order to reduce the influence of dominant chromaticity. Next, the chromaticity of illuminant is estimated in highlight region and the local average images are corrected by the estimated chromaticity of illuminant. In experiment, results show that the proposed method improved the local contrast and detail without color distortion.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.273-278
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• 2004

Feature identification is one of the largest issue in high spatial resolution satellite imagery. A popular method associated with this feature identification is image segmentation to produce image segments that are more likely to features interested. Here, it is, proposed that combination of edge extraction and region growing methods for image segments were used to improve the result of image segmentation. At the intial step, an image was segmented by edge detection method. The segments were assigned IDs, and polygon topology of segments were built. Based on the topology, the segments were tested their similarities with adjacent segments using multivariate analysis. The segments that have similar spectral characteristics were merged into a region. The test application shows that the segments composed of individual large, spectrally homogeneous structures, such as buildings and roads, were merged into more similar shape of structures.

• Journal of the Korea Computer Graphics Society
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• v.18 no.2
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• pp.9-15
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• 2012

In this paper, we propose a method to segment a color image into several meaningful regions. We suppose that the meaningful region has a set of colors with high frequency in the color image. To find these colors, the color image is represented as several sets of color points in RGB space. And when we use the density of points defined in this method, color belonging to a dense region of color points in RGB space refers to the color that appeared frequently in the image. Eventually, we can find meaningful regions by looking for regions with high density of color points using our level set function in RGB space. However, if a meaningful region does not have a contiguous region of the sufficient size in the image, this is not a meaningful region but meaningless region. Thus, the pixels in the meaningless region are assigned to the biggest meaningful region belonging to its neighboring pixels in the color image. Our method divides the color image into meaningful regions by applying the density of color points to level set function in RGB space. This is different from the existing level set method that is defined only in 2D image.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.814-818
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• 2004

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic and timing information besides has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing For off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods which effectively extracts skeleton through conditional test mask considering running time and quality. of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Watershed image conversion uses prewitt operator for gradient image conversion, extracts local minima considering 8-neighborhood pixel. And methods by using difference of mean value is used in region merging step, Converted watershed image by means of this methods separates effectively character region and background region applying to segmentation function. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.