• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• Journal of Korean Society of Forest Science
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• v.96 no.1
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• pp.107-114
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• 2007

Establishments of the seedlings inside the natural forest from adjacent artificial forests would be an important factor in forest stand dynamics. This study was conducted to see the invasion of Korean pine (Pinus koraiensis) seedlings which is not native in this region, into the natural deciduous broad-leaved forest in Gwangneung, Korea. There is no mother tree at the I ha study site while the number of naturally regenerated P. koraiensis seedlings was 345 trees and 56% of them were clumped with more than two seedlings at each point. Applying the image segmentation method to IKONOS satellite image of January, 2003, the distance from the center of 1 ha study site to the nearest mother tree and plantation of Korean pine were 200 m and 270 m, respectively. The average height and root-collar diameter of the seedlings were 34 em and 7 mm, respectively and the age of 207 seedlings (60%) were below 5 years old. Most abundant range of soil moisture gradient and LAl (leaf area index) were from 16 to 20% and those of LAI were from 3.1 to 3.5. To understand the dynamics and seed dispersal pattern of Korean pine in the Gwangneung natural deciduous broad-leaved forests, additional studies not only long-term monitoring of growth and mortality of naturally regenerated Korean pine seedlings but also application of stable isotope analysis and molecular genetic techniques was recommended.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.89-98
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• 2005

Fingerprint segmentation and ridge orientation estimation algorithms with images from a mobile camera are proposed. The fingerprint images from a mobile camera are quite different from those from conventional sensor, called touch based sensor such as optical, capacitive, and thermal. For example, the images from a mobile camera are colored and the backgrounds or non-finger regions are very erratic depending on how the image capture time and place. Also the contrast between ridge and valley of a mobile camera image are lower than that of touch based sensor image. To segment fingerprint region, we first detect the initial region using color information and texture information. The LUT (Look Up Table) is used to model the color distribution of fingerprint images using manually segmented images and frequency information is extracted to discriminate between in focused fingerprint regions and out of focused background regions. With the detected initial region, the region growing algerian is executed to segment final fingerprint region. In fingerprint orientation estimation, the problem of gradient based method is very sensitive to outlier that occurred by scar and camera noise. To solve this problem, we propose a robust regression method that removes the outlier iteratively and effectively. In the experiments, we evaluated the result of the proposed fingerprint segmentation algerian using 600 manually segmented images and compared the orientation algorithms in terms of recognition accuracy.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.128-139
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• 2005

Moving object boundary is very important for moving object segmentation. But the moving object boundary shows broken boundary We invent a novel space-oriented boundary linking algorithm to link the broken boundary The boundary linking algorithm forms a quadrant around the terminating pixel in the broken boundary and searches forward other terminating pixel to link within a radius. The boundary linking algorithm guarantees shortest distance linking. We also register the background from image sequence. We construct two object masks, one from the result of boundary linking and the other from the registered background, and use these two complementary object masks together for moving object segmentation. We also suppress the moving cast shadow using Roberts gradient operator. The major advantages of the proposed algorithms are more accurate moving object segmentation and the segmentation of the object which has holes in its region using these two object masks. We experiment the algorithms using the standard MPEG-4 test sequences and real video sequence. The proposed algorithms are very efficient and can process QCIF image more than 48 fps and CIF image more than 19 fps using a 2.0GHz Pentium-4 computer.

• Journal of the Korean Magnetics Society
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• v.26 no.6
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• pp.219-225
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• 2016

In this study, for assessment of degenerative knee joint cartilage disease we acquired images by fat suppressed 3D spoiled gradient recalled (SPGR) and fat suppressed 3D $T2^*$ weighted imaging techniques. To do a quantitative evaluation, the knee joint cartilage was divided into medial femoral cartilage (MFC), medial tibial cartilage (MTC), lateral femoral cartilage (LFC), lateral femoral cartilage (LFC) and patella cartilage (Pat) to measure their respective signal intensity values, signal-to-noise ratio, and contrast-to-noise ratio. As for the measured values, statistical significance between two techniques was verified by using Mann-Whitney U-Test. To do a qualitative evaluation, two radiologists have examined images by techniques after which image artifact, cartilage surface, tissue contrast, and depiction of lesion distinguishing were evaluated based on 4-point scaling (1: bad, 2: appropriate, 3: good, 4: excellent), and based on the result, statistical significance was verified by using Kappa-value Test. 3.0T MR system and HD T/R 8ch knee array coil were used to acquire images. As a result of a quantitative analysis, based on SNR values measured by using two imaging techniques, MFC, LFC, LTC, and Pat showed statistical significance (p < 0.05), but MTC did not (p > 0.05). As a result of verifying statistical significance for measured CNR value, MFC, LFC, and Pat showed statistical significance (p < 0.05), while MTC and LTC did not show statistical significance (p > 0.05). As a result of a qualitative analysis, by comparing mean values for evaluated image items, 3D $T2^*$ weighted Image has indicated a slightly higher value. As for conformance verification between the two observers by using Kappa-value test, all evaluated items have indicated statistically significant results (p < 0.05). 3D $T2^*$ weighted technique holds a clinical value equal to or superior to 3D SPGR technique with respect to evaluating images, such as distinguishing knee joint cartilages, comparing nearby tissues contrast, and distinguishing lesions.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.167-172
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• 1999

Purpose : To evaluate the detection rate of hyperacute intracerebral hemorrhage in echo planar imaging (EPI) and other MR sequences. materials and Methods : Intracerebral hemorrhage was experimentally induced in ten rats. EPI, fast spin-echo (FSE) T2 weighted images, fluid attenuated inversion recovery (FLAIR), spin-echo (SE) T1 weighted images and gradient echo (GE) T1 weight ed images of rat's brains were obtained 2 hours after onset of intracerebral hemorrhage. EPI and FSE T2 images were additionally obtained 30 min and 1 hour after onset of hemorrhage in 3 and 6 rat, repeatedly, For objective visual assessment, discrimination between the lesion and normal brain parenchyma was evaluated on various MR sequences by three radiologists. For quantitative assessment, contrast-to-noise ratio (CNR) was calculated fro hemorrhage-normal brain parenchyma. Statistical analysis was performed usning the Wilcoxon-Ranks test. Results : EPI, FLAIR, and FSE T2 images showed high signal intensity lesions. The lesion discrimination was easier on EPI than on other sequences, and also EPI showed higher signal intensity for the subjective visual assessment. In quantitative evaluation, CNR of the hemorrhagic lesion versus normal brain parenchyma were higher on EPI and FLAIR images (p<0.01). There was no difference in CNR between EPI and FLAIR (p>0.10). On MR images obtained 30 minutes and 1 hour after the onset of intracerebral hemorrhage, the lesion detection was feasible on both EPI and FSE T2 images showing high signal intensity. Conclusion : EPI showed higher detection rate as compared with other MR sequences and could be useful in early detection and evaluation of intracerebral hemorrhage.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.93-101
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• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.9-16
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• 2006

This Paper proposes an adaptive error diffusioThis paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, and the MGD values are filled within a local window to merge the potential text segments. Isolated segments are then eliminated in the non-text region filtering process. After the left segmentation, a conventional error diffusion method is applied to the background, while the edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, the gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) prevents the printing of successive dots around the text region boundaries. The error diffusion algorithm with edge enhancement is extended to halftone color images to sharpen the tort regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, the additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. By using the proposed algorithm, the text of a scanned image is sharper than that with a conventional error diffusion without changing background.