• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1531-1540
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• 2016

In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.436-438
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• 2019

Air hockey robots are machine vision systems that allow users to play hockey balls through the camera. The position detection of the hockey ball is realized by using the color information of the ball using OpenCV library. It senses the position of the hockey ball, predicts its trajectory, and sends the result to the ARM Cortex-M board. The ARM Cortex-M board controls a 2- Axis Cartesian Coordinate Robot to run an air hockey game. Depending on the strategy of the air hockey robot, it can operate in defensive, offensive, defensive and offensive mode. In this paper, we describe a vision system development and trajectory prediction system and propose a new method to control a biaxial orthogonal robot in an air hockey game.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.65-72
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• 2009

This paper presents a vision based localization method for indoor mobile robots using the types of vertices from a monocular image. In the images captured from a camera of a robot, the types of vertices are determined by searching vertical edges and their branch edges with a geometric constraints. For obtaining correspondence between the comers of a 2-D map and the vertex of images, the type of vertices and geometrical constraints induced from a geometric analysis. The vertices are matched with the comers by a heuristic method using the type and position of the vertices and the comers. With the matched pairs, nonlinear equations derived from the perspective and rigid transformations are produced. The pose of the robot is computed by solving the equations using a least-squares optimization technique. Experimental results show that the proposed localization method is effective and applicable to the localization of indoor environments.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.640-655
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• 2014

Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.25-28
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• 2019

Purpose In patients with unusual kidney position after $^{99m}Tc-DTPA$ renal dynamic imaging study, the GFR(Glomerular Filtration Rate) values are significantly different according to the depth of the kidney. Thus, we tried to compare the difference of the GFR values between the depth measurement methods and in-vitro test. 30 adult patients who were subjected to renal study. 27 patients were in usual position and 3 patients were in unusual. $555{\pm}37MBq$ of $^{99m}Tc-DTPA$ was administrated to all patients. GE infinia gamma camera was used. GFR values were obtained in-vivo(gates method) and in-vitro(blood). The kidney depth in-vivo was calculated by three methods(tonnensen, manual, taylor). In-vitro, GFR was performed by blood test. Differences in the mean values of GFR and correlation between depth and GFR values were evaluated using the SPSS 12.0 statistical program. The GFR values for 27 patients with kidney in the usual position are as follows(1.tonnensen 2.manual 3.taylor 4.invitro); $69.3{\pm}4.2$, $88.2{\pm}5.6$, $77.8{\pm}4.3$, $82.2{\pm}5.8ml/min$. The three unusual cases are as follows, first(congenital renal anomaly): 66.4, 101.24, 69.07, 94.8 ml/min. second(transplantation kidney): 12.22, 29.99, 19.36, 23.5 ml/min. third(horseshoe kidney): 37.37, 93.54, 35.9, 92.5 ml/min. There was a difference between tonnensen and manual in the usual position of the kidney(p<0.05). There was no significant difference between the other methods. However, there was a significant difference in case of the unusual position of the kidneys. Correlation analysis between both kidney depth and GFR value shows person correlation as follows; Rt kidney: 0.298, Lt kidney: 0.322. When compared with the GFR values in-vitro test, it was useful to calculate the GFR value by measuring the kidney depth using a manual formula in the unusual position of the kidneys. GFR values and kidney depth were significantly related.