• Journal of IKEEE
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• v.19 no.2
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• pp.168-175
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• 2015

In this paper, we propose FPGA design technique for rotating LED display device which is capable of displaying videos with the use of the afterimage effect. The proposed technique is made up of image data correction process based on inverse gamma correction and error diffusion, block interleaving process, and data serial output process. The data correction process based on inverse gamma correction and error diffusion is an image data correction step in which image data received are corrected by inverse gamma correction process to convert the data into linear brightness characteristics, and by error diffusion process to reduce the brightness reduction phenomenon in low-gray-level which is caused by inverse gamma correction. In the block interleaving process, the data of the frames entered transversely are first saved in accordance with entrance order, and then only the longitudinal image data are read. The data serial output process is applied to convert the parallel data in a rotating location into serial data and send them to LED Driver IC, in order to send data which will be displayed on high-speedy rotating LED Bar. To evaluate the accuracy of the proposed FPGA design technique, this paper used XC6SLX45-FG484, a Spartan 6 family of Xilinx, as FPGA, and ISE 14.5 as a design tool. According to the evaluation analysis, it was found that goal values were consistent with simulation values in terms of accurate operation of inverse gamma and error diffusion correction, block interleaving operation, and serialized operation of image data.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.991-1000
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• 2007

A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.99-112
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• 2019

Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.227-234
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• 2015

In this paper, we studied possibility about application for CAD on diffuse liver disease through pixel texture analysis parameters(average gray level, skewness, entropy) which based statistical property brightness histogram and image analysis using brightness difference liver and kidney parenchyma. The experiment was set by ROI ($50{\times}50$ pixels) on liver ultrasound images.(non specific, fatty liver, liver cirrhosis) then, evaluated disease recognition rates using 4 types pixel texture analysis parameters and brightness gap liver and kidney parenchyma. As a results, disease recognition rates which contained average brightness, skewness, uniformity, entropy was scored 100%~96%, they were high. In brightness gap between liver and kidney parenchyma, non specific was $-1.129{\pm}12.410$ fatty liver was $33.182{\pm}11.826$, these were shown significantly difference, but liver cirrhosis was $-1.668{\pm}10.081$, that was somewhat small difference with non specific case. Consequently, pixel texture analysis parameter which scored high disease recognition rates and CAD which used brightness difference of parenchyma are very useful for detecting diffuse liver disease as well as these are possible to use clinical technique and minimize reading miss. Also, it helps to suggest correct diagnose and treatment.

• Progress in Medical Physics
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• v.11 no.1
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• pp.39-47
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• 2000

In Stereotactic Radiosurgery (SRS), there are three imaging methods of target localization, such as digital subtraction Angiography (DSA), computed tomography (CT), magnetic resonance imaging (MRI). Especially, DSA and MR images have a distortion effect generated by each modality. In this research, image properties of DSA were studied. A first essential condition in SRS is an accurate information of target locations, since high dose used to treat a patient may give a complication on critical organ and normal tissue. Hut previous localization program did not consider distortion effect which was caused by image intensifier (II) of DSA. A neurosurgeon could not have an accurate information of target locations to operate a patient. In this research, through distortion correction, we tried to calculate accurate target locations. We made a grid phantom to correct distortion, and a target phantom to evaluate localization algorithm. The grid phantom was set on the front of II, and DSA images were obtained. Distortion correction methods consist of two parts: 1. Bilinear transform for geometrical correction and bilinear interpolation for gray level correction. 2. Automatic detection method for calculating locations of grid crosses, fiducial markers, and target balls. Distortion was corrected by applying bilinear transform and bilinear interpolation to anterior-posterior and left-right image, and locations of target and fiducial markers were calculated by the program developed in this study. Localization errors were estimated by comparing target locations calculated in DSA images with absolute locations of target phantom. In the result, the error in average with and without distortion correction is $\pm$0.34 mm and $\pm$0.41 mm respectively. In conclusion, it could be verified that our localization algorithm has an improved accuracy and acceptability to patient treatment.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7324-7330
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• 2014

Color distortion of the dark images acquired under a low-light-level environment with a weak light source can be cause of the performance decreation of various vision systems. Therefore, recovering the original color of the images is an important process for enhancing the performance of the system. For this, this study proposes a color restoration method using a dichromatic reflection model. This paper assumes that the dark images can be classified into two parts affected by specular or diffuse reflection. Two different color constancy methods were then applied to the images to remove the effects of each reflection and two images were created as a result. The resulting images produced a one color-corrected image by combining with different weights according to the position in the images. For the performance evaluation, this paper used a synthesized image, and considered the Euclidean distance and angular error as an evaluation factor. In addition, a performance comparison was performed with the existing various color constancy method to achieve the objectivity of evaluation. The experimental results showed that the proposed method can be a more suitable solution for color restoration than the existing method.

• Investigative Magnetic Resonance Imaging
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• v.15 no.1
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• pp.32-40
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• 2011

Purpose : The purpose of this study was to investigate the microstructural changes according to aging on the thickness and signal intensity (SI) of the cortical gray matter (GM) and white matter (WM) on the T2-, fluid-attenuated inversion recovery (FLAIR) and T1-weighted MR images in normal subjects. Materials and Methods : The 10, 20, 30, 40, 50, 60, 70, 80 and 90 year age groups of men and women (each 10 individuals) who underwent routine brain MRI, including the T2-, FLAIR and T1-weighted images, were selected for this study. We measured the thickness and the SI of the cortical GM and WM at the postcentral gyrus, which has an even thickness at the level of centrum semiovale, on the axial scans and we calculated the mean values of the thickness ratio of the gray/white matter (TRGW) and the signal intensity ratio of the gray/white matter (SRGW), and we compared the ratios of each age group. Results : On the T2-weighted images, the TRGWs were 0.81 and 0.79 at the age of 10 and they were 0.73 and 0.71 at the age of 90 in the men and women, respectively. So, the GM thickness was decreased more than the WM thickness was with aging. On the FLAIR images, the TRGWs were 1.09 and 1.00 at the age of 10 and they were 1.11 and 0.95 at the age of 70 in the men and women, respectively. On the T1-weighted images, the TRGWs were 0.66 and 0.80 at the age of 10, and the ratio was changed to 0.90 and 0.78 at the age of 90 in the men and women, respectively. On the T2-weighted image, the SRGWs were 1.53 and 1.43 at the age of 10, and they were 1.23 and 1.27 at the age of 90 in the men and women, respectively. On the FLAIR images, the SRGWs were 1.23 and 1.25 at the age of 10 and they were 1.06 and 1.05 at the age of 90 in the men and women, respectively. On the T1-weighted images, the SRGWs were 0.86 and 0.85 at the age of 10, and they were 0.90 and 0.87 at the age of 90 in the men and women, respectively. Conclusion : We suggest that the age-related microstructural changes of the thickness and the SI of the cortical GM and WM on the T2-, FLAIR and T1-weighted images are unique, and so this knowledge will be helpful to differentiate neurodegenerative disease from normal aging of the brain.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1171-1176
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• 2003

In this paper, we propose an effective bar code detection algorithm using the feature analysis and the labeling. After computing the direction of pixels using four line operators, we obtain the histogram about the direction of pixels by a block unit. We calculate the difference between the maximum value and the minimum value of the histogram and consider the block that have the largest difference value as the block of the bar code region. We get the line passing by the bar code region with the selected block but detect blocks of interest to get the more accurate line. The largest difference value is used to decide the threshold value to obtain the binary image. After obtaining a binary image, we do the labeling about the binary image. Therefore, we find blocks of interest in the bar code region. We calculate the gradient and the center of the bar code with blocks of interest, and then get the line passing by the bar code and detect the bar code. As we obtain the gray level of the line passing by the bar code, we grasp the information of the bar code.

• Journal of radiological science and technology
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• v.40 no.2
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• pp.237-243
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• 2017

This study evaluated the applicability of computer-aided diagnosis by retrospective analysis of GLCM algorithm based on cytopathological diagnosis of normal and malignant nodules in thyroid ultrasound images. In the experiment, the recognition rate and ROC curve of thyroid malignant nodule were analyzed using 6 parameters of GLCM algorithm. Experimental results showed 97% energy, 93% contrast, 92% correlation, 92% homogeneity, 100% entropy and 100% variance. Statistical analysis showed that the area under the curve of each parameter was more than 0.947 (p = 0.001) in the ROC curve, which was significant in the recognition of thyroid malignant nodules. In the GLCM, the cut-off value of each parameter can be used to predict the disease through analysis of quantitative computer-aided diagnosis.