• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.608-617
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• 2017

Methods of contrast enhancement have problem such as side effect of over-enhancement with non-gaussian histogram distribution, tradeoff enhancement efficiency against brightness preserving. In order to enhance contrast at various histogram distribution, segmentation to region with gaussian distribution and then enhance contrast each region. First, we segment an image into several regions using GMM(Gaussian Mixture Model)fitting by that k-mean clustering and EM(Expectation-Maximization) in $L^*a^*b^*$ color space. As a result region segmentation, we get the region map and probability map. Then we apply local contrast enhancement algorithm that mean shift to minimum overlapping of each region and preserve brightness histogram equalization. Experiment result show that proposed region based contrast enhancement method compare to the conventional method as AMBE(AbsoluteMean Brightness Error) and AE(Average Entropy), brightness is maintained and represented detail information.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.102-113
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• 1997

In this paepr, a new image segmentation method reducing efficiently contour information causing bottleneck problem at segmentatio-based very low bit rate codingis proposed, while preserving objective and subjective quality. It consists of 4-level hierarchical image segmentation based on mathematical morphology and 1-leve region merging structure using contast of two adjacent regions. For two adjacent region pairs at the fourth level included in each region of the thid level, contrast is calculated. Among the pairs of two adjacent regions with less value than threshold, two adjacent regions having the minimum contrast are merged first. After region merging, texture of the merged region is updated. The procedure is performed recursively for all the adjacent region pairs at the fourth level included in each region of the third level. Compared with the previous method, the objective and subjective image qualities are similar. But it reduces 46.65% texture information on the average by decreasing total region number to be tansmitted. Specially, it shows reduction of the 23.95% contour information of the average. Thus, it can improve efficiently the bottleneck problem at segementation-based very low bit rate coding.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.330-332
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• 1997

The absorption coefficient of contrast media was measured region of diagnostic X-ray. Relative values of absorption coefficient was found the largest peak in the range of $60{\sim}70kVp$ for sodium sulfate and 60kVp for iodine. Increasing the thickness of contrast media and patient, the values of absorption coefficient was rising.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.132-135
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• 1997

The absorption coefficient of contrast media was meassured region of diagnostic radiology. Relative values of absorption coefficient was fecund the largest peak in the range of 60 ∼ 70kVp for sodium sulfate and 60kVp for iodine. Increasing the thickness of contrast media and patient the values of absorption coefficient was rising.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.175-180
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• 1996

In this paper, a new image segmentation method based on merging of two low contrast neighbor regions iteratively is proposed. It is suitable for very low bit rate coding. The proposed method reduces efficiently contour information and preserves subjective and objective image quality. It consists of image segmentation using 4-level hierarchical structure based on mathematical morphology and 1-level region merging structure using the contrast difference of two adjacent neighbor regions. For each segmented region of the third level, two adjacent neighbor regions having low contrast difference value in fourth level based on contrast difference value is merged iteratively. It preserves image quality and shows the noticeable reduction of the contour information, so that it can improve the bottleneck problem of segmentation-based coding at very low bit rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1594-1600
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• 2005

Recently many researches have been studied in the human vision model to solve the Wblem of the machine vision. Starting from research on the human visual system, first, we investigate the mechanisms of retina through physiological and biological evidence. In retina, input data was processed information processing that was data reduction edge detection, and emphasizing region. The processed image was recognized by region. In this paper, we proposed retinal algorithms that process data reduction and edge detection by the wavelet transform and emphasize region contrast. In experiments, the proposed model simulates processing the retina outputs in the levels and compares with outputs.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.56-65
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• 2004

In this paper, we proposed the detection algorithm of microcalcification based on region growing method with contrast and edge sharpness in digital X-ray mammographic images. We extracted the local maximum pixel and watershed regions by using watershed algorithm. Then, we used the mean slope between local maximum and neighborhood pixels to extract microcalcification candidate pixels among local maximum pixels. During increasing threshold value to grow microcalcification region, at the maximum threshold value of the contrast and edge sharpness, the microcalcification area is decided. The regions of which area of grown candidate microcalfication region is larger than that of watershed region are excluded from microcalcifications. We showed the diagnosis algorithm can be used to aid diagnostic-radiologist in the early detection breast cancer.

• Imaging Science in Dentistry
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• v.46 no.3
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• pp.203-210
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• 2016

Purpose: The purpose of this study was to investigate appropriate contrast reference values (CRVs) by comparing the contrast in phantom and clinical images. Materials and Methods: Phantom contrast was measured using two methods: (1) counting the number of visible pits of different depths in an aluminum plate, and (2) obtaining the contrast-to-noise ratio (CNR) for 5 tissue-equivalent materials (porcelain, aluminum, polytetrafluoroethylene [PTFE], polyoxymethylene [POM], and polymethylmethacrylate [PMMA]). Four panoramic radiographs of the contrast phantom, embedded in the 4 different regions of the arch-form stand, and 1 real skull phantom image were obtained, post-processed, and compared. The clinical image quality evaluation chart was used to obtain the cut-off values of the phantom CRV corresponding to the criterion of being adequate for diagnosis. Results: The CRVs were obtained using 4 aluminum pits in the incisor and premolar region, 5 aluminum pits in the molar region, and 2 aluminum pits in the temporomandibular joint (TMJ) region. The CRVs obtained based on the CNR measured in the anterior region were: porcelain, 13.95; aluminum, 9.68; PTFE, 6.71; and POM, 1.79. The corresponding values in the premolar region were: porcelain, 14.22; aluminum, 8.82; PTFE, 5.95; and POM, 2.30. In the molar region, the following values were obtained: porcelain, 7.40; aluminum, 3.68; PTFE, 1.27; and POM, - 0.18. The CRVs for the TMJ region were: porcelain, 3.60; aluminum, 2.04; PTFE, 0.48; and POM, - 0.43. Conclusion: CRVs were determined for each part of the jaw using the CNR value and the number of pits observed in phantom images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.3-8
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• 1999

In this paper, we propose a new connected operator using morphological grayscale reconstruction for region-based coding. First, an effective method of reference-image creation is proposed, which is based on the size as well as the contrast. The conventional connected operators are good for removing small regions, but have a serious drawback for low-contrast regions that are larger than the structuring element. That is, when the conventional connected operators are applied to these regions. the simplification becomes less effective or several meaningful regions are merged to one region. To avoid this, the conventional geodesic dilation is modified to propose an adaptive operator. To reduce the effect of inappropriate propagation, pixels reconstructed to the original values are excluded in the dilation operation. Experimental results have shown that the proposed algorithm achieves better performance in terms of the reconstruction of flat zones. The picture quality has also been improved by about 7dB, compared to the conventional methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.525-532
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• 2018

Recently, due to the advancements of sensor network technologies and camera technologies, there are increasing needs to effectively monitor the environment in a region that is difficult to access by using the visual sensor network that combines these two technologies. Since the image captured by the visual sensor reflects the natural phenomenon as it is, the quality of the image may deteriorate depending on the weather or time. In this paper, we propose an algorithm to improve the contrast of images using the characteristics of images obtained from visual sensors. In the proposed method, we first set the region of interest and then analyzes the change of the color value of the region of interest according to the brightness value of the image. The contrast of an image is improved by using the high contrast image of the same object and the analysis information. It is shown by experimental results that the proposed method improves the contrast of an image by restoring the color components of the low contrast image simply and accurately.