• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2430-2436
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• 2013

This study suggests a method of detecting the still object, which becomes a cause of danger within the crossroad. The Inverse Perspective Transform was performed in order to make the object size consistent by being inputted the real-time image from CCTV that is installed within the crossroad. It established the detection area in the image with the perspective transform and generated the adaptative background image with the use of the moving information on object. The detection of the stop object was detected the candidate region of the stop object by using the background-image differential method. To grasp the appearance of truth on the detected candidate region, a method is proposed that uses the gradient information on image and EHD(Edge Histogram Descriptor). To examine performance of the suggested algorithm, it experimented by storing the images in the commuting time and the daytime through DVR, which is installed on the cross street. As a result of experiment, it could efficiently detect the stop vehicle within the detection region inside the crossroad. The processing speed is shown in 13~18 frame per second according to the area of the detection region, thereby being judged to likely have no problem about the real-time processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.29-35
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• 2010

This paper discusses a method that can enhance the exactness of depth estimation of an image by PCA(Principle Component Analysis) based on feature reduction through learning algorithm. In estimation of the depth of an image, hyphen such as energy of pixels and gradient of them are found, those selves and their relationship are used for depth estimation. In such a case, many features are obtained by various filter operations. If all of the obtained features are equally used without considering their contribution for depth estimation, The efficiency of depth estimation goes down. This paper proposes a method that can enhance the exactness of depth estimation of an image and its processing speed is considered as the contribution factor through PCA. The experiment shows that the proposed method(30% of an feature vector) is more exact(average 0.4%, maximum 2.5%) than using all of an image data in depth estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1345-1352
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• 2021

Most of weather radar systems are used to monitor the whole weather situation for the very wide and medium-to-long range area. However, as the likelihood of occurrence of the local weather hazards is increased in recent days, it is very important to detect these wether phenomena with a local weather radar. For this purpose, it is necessary to detect the fast varying low altitude weather conditions and the effect of the ground surface clutter is more evident. Therefore, in this paper, the newly suggested method is explained and analyzed for detection of weather hazards such as the gust and wind shear using the fluctuation of wind velocities and the gradient of wind velocities among range cells. It is shown that the suggested method can be used efficiently in the future for faster detection of weather change through the simple algorithm implementation and also the effect of the ground clutter can be minimized in the detection procedure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.1-8
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• 2005

In this paper, we propose a two-stage head tracking algorithm adequate for real-time active camera system having pan-tilt-zoom functions. In the color convergence stage, we first assume that the shape of a head is an ellipse and its model color histogram is acquired in advance. Then, the min-shift method is applied to roughly estimate a target position by examining the histogram similarity of the model and a candidate ellipse. To reflect the temporal change of object color and enhance the reliability of mean-shift based tracking, the target histogram obtained in the previous frame is considered to update the model histogram. In the updating process, to alleviate error-accumulation due to outliers in the target ellipse of the previous frame, the target histogram in the previous frame is obtained within an ellipse adaptively shrunken on the basis of the model histogram. In addition, to enhance tracking reliability further, we set the initial position closer to the true position by compensating the global motion, which is rapidly estimated on the basis of two 1-D projection datasets. In the subsequent stage, we refine the position and size of the ellipse obtained in the first stage by using shape information. Here, we define a robust shape-similarity function based on the gradient direction. Extensive experimental results proved that the proposed algorithm performs head hacking well, even when a person moves fast, the head size changes drastically, or the background has many clusters and distracting colors. Also, the propose algorithm can perform tracking with the processing speed of about 30 fps on a standard PC.

• Progress in Medical Physics
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• v.10 no.3
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• pp.133-140
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• 1999

In this paper, as a preliminary study for developing a full 3D electron dose calculation algorithm, We developed 2.5D electron dose calculation algorithm by extending 2D pencil-beam model to consider three dimensional geometry such as air-gap and obliquity appropriately. The dose calculation algorithm was implemented using the IDL5.2(Research Systems Inc., USA), For calculation of the Hogstrom's pencil-beam algorithm, the measured data of the central-axis depth-dose for 12 MeV(Siemens M6740) and the linear stopping power and the linear scattering power of water and air from ICRU report 35 was used. To evaluate the accuracy of the implemented program, we compared the calculated dose distribution with the film measurements in the three situations; the normal incident beam, the 45$^{\circ}$ oblique incident beam, and the beam incident on the pit-shaped phantom. As results, about 120 seconds had been required on the PC (Pentium III 450MHz) to calculate dose distribution of a single beam. It needs some optimizing methods to speed up the dose calculation. For the accuracy of dose calculation, in the case of the normal incident beam of the regular and irregular shaped field, at the rapid dose gradient region of penumbra, the errors were within $\pm$3 mm and the dose profiles were agreed within 5%. However, the discrepancy between the calculation and the measurement were about 10% for the oblique incident beam and the beam incident on the pit-shaped phantom. In conclusions, we expended 2D pencil-beam algorithm to take into account the three dimensional geometry of the patient. And also, as well as the dose calculation of irregular field, the irregular shaped body contour and the air-gap could be considered appropriately in the implemented program. In the near future, the more accurate algorithm will be implemented considering inhomogeneity correction using CT, and at that time, the program can be used as a tool for educational and research purpose. This study was supported by a grant (＃HMP-98-G-1-016) of the HAN(Highly Advanced National) Project, Ministry of Health & Welfare, R.O.K.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.261-274
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• 2004

The feature extraction using remotely sensed imagery has been recognized one of the important tasks in remote sensing applications. As the high-resolution imagery are widely used to the engineering purposes, need of more accurate feature information also is increasing. Especially, in case of the automatic extraction of linear feature such as road using mid or low-resolution imagery, several techniques was developed and applied in the mean time. But quantitatively comparative analysis of techniques and case studies for high-resolution imagery is rare. In this study, we implemented a computer program to perform and compare GDPA (Gradient Direction Profile Analysis) algorithm and Hough transformation. Also the results of applying two techniques to some images were compared with road centerline layers and boundary layers of digital map and presented. For quantitative comparison, the ranking method using commission error and omission error was used. As results, Hough transform had high accuracy over 20% on the average. As for execution speed, GDPA shows main advantage over Hough transform. But the accuracy was not remarkable difference between GDPA and Hough transform, when the noise removal was app]ied to the result of GDPA. In conclusion, it is expected that GDPA have more advantage than Hough transform in the application side.

• 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.