• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.355-359
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• 2009

In this paper, we describe self-training super-resolution. Our approach is based on example based algorithms. Example based algorithms need training images, and selection of those changes the result of the algorithm. Consequently it is important to choose training images. We propose self-training based super-resolution algorithm which use an input image itself as a training image. It seems like other example based super-resolution methods, but we consider training phase as the step to collect primitive information of the input image. And some artifacts along the edge are visible in applying example based algorithms. We reduce those artifacts giving weights in consideration of the edge direction. We demonstrate the performance of our approach is reasonable several synthetic images and real images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1076-1083
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• 2000

Vehicle license plate recognition identifies vehicle as a unique, and have many applications in traffic monitoring field. In this paper, a vertical edge based algorithm to extract license plate within input gray-scale image is proposed. A size-and-shape filter based on seed-filling algorithm is applied to remove the edges that are impossible to be the vertical edges of license plate. Then the remaining edges are matched with each other according to some restricted conditions so as to locate license plate in input image. After license plate is extracted. normalized and segmented, the characters on it are recognized by template matching method. Experimental results show that the proposed algorithm can deal with license plates in normal shape effectively, as well as the license plates that are out of shape due to the angle of view.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.333-336
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• 2007

We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Then we present a method to eliminate the tracked contour object by replacing with the background scene we get from other frame. Our method consists of two parts: first we track the object using LOD (Level-of-Detail) canny edge maps, then we generate background of each image frame and replace the tracked object in a scene by a background image from other frame that is not occluded by the tracked object. Our tracking method is based on level-of-detail (LOD) modified Canny edge maps and graph-based routing operations on the LOD maps. To reduce side-effects because of irrelevant edges, we start our basic tracking by using strong Canny edges generated from large image intensity gradients of an input image. We get more edge pixels along LOD hierarchy. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find the best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible. This approach is based on computing camera motion. Our experimental results show that our method works nice for moderate camera movement with small object shape changes.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.430-437
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• 2005

In this paper, we proposed a biologically inspired light-adaptive edge detection circuit based on the human retina. A saturating resistive network was suggested for light adaptation and simulated by using HSPICE. The light adaptation mechanism of the edge detection circuit was quantitatively analyzed by using a simple model of the saturating resistive element. A light-adaptive capability of the edge detection circuit was confirmed by using the one-dimensional array of the 128 pixels with various levels of input light intensity. Experimental data of the saturating resistive element was compared with the simulated results. The entire capability of the edge detection circuit, implemented with the saturating resistive network, was investigated through the two-dimensional array of the $64{\times}64$ pixels

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.111-119
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• 2009

This paper presents an adaptive image interpolation algorithm using local characteristics. An input image is classified into edge region and flat low frequency region. And then, the edge region is further partitioned into directive edge region and high frequency texture region. A bilinear interpolation is applied to flat low frequency region, cubic convolution is applied to texture region, and new edge directed interpolation to directive edge region, respectively. Simulation results show that the proposed algorithm outperforms the existing interpolation methods in terms of visual quality as well as PSNR.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2581-2584
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• 2003

In this paper, we propose a self-localization algorithm using vertical line segments. Indoor environment is consist of horizontal and vertical line features such as doors, furniture, and so on. From the input image, vertical line edges are detected by an edge operator, Then, line segments are obtained by projecting edge image vertically and detecting local maximum from the projected histogram. From the relation of horizontal position of line segments and the location of the robot, nonlinear equations are come out Localization is done by solving the equations by using Newton's method. Experimental results show that the proposed algorithm using one camera is simple and applicable to indoor environment.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.7-10
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• 2003

This paper proposes the multiple objects focusing algorithm. Given multiple objects at different distances from a camera, we assume that one object is well-focused and the others are out-of-focused. The proposed auto-focusing algorithm is summarized as follows: (i) detects edges from an input image, (ⅱ) estimates the radius of PSF (Point Spread Function) across the edge, (ⅲ) gather edge points having same radius of PSF, (ⅳ) segments the image into regions with the same radius of PSF, and (ⅴ) restores the each segmented region using the corresponding PSF.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.587-593
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• 2010

In this paper we design an irregular low-density parity-check (LDPC) code for multiple-input multiple-output (MIMO) system, using a simple extrinsic information transfer (EXIT) chart method. The MIMO systems considered are optimal maximum a posteriori probability (MAP) detector. The MIMO detector and the LDPC decoder exchange soft information and form a turbo iterative receiver. The EXIT charts are used to obtain the edge degree distribution of the irregular LDPC code which is optimized for the MIMO detector. It is shown that the performance of the designed LDPC code is better than that of conventional LDPC code which was optimized for either the Additive White Gaussian Noise (AWGN) channel or the MIMO channel.

• Proceedings of the Optical Society of Korea Conference
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• 2008.02a
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• pp.197-198
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• 2008

본 논문에서는 Iterative Fourier Transform Algorithm $Method(IFTA)^{(1)}$를 사용하여 Diffractive Optical Element(DOE)를 통과한 빛의 Shape이 Input Beam의 각 조건에 따라 얼마나 원하는 형태에 가까워지는지를 Input 대비 Output의 Efficiency와 Signal to Noise Ratio(SNR) Simulation 을 통해 알아보았다. Input beam의 종류는 Gaussian, Supergaussian, Plane, Spherical, Quadratic wave 으로 하고 각각의 경우에 대해 Beam Diameter, Polarization, Wavelength를 변화시키며 DOE에서의 회절 현상을 simulation하였다. 이때 Polarization은 Linear, Circular, Elliptical 형태로 변화시켰고 Wavelength는 332.8nm에서 832.8nm까지의 범위에 대해 연구하였다. 또한 relative edge가 있을 때와 없을 때를 비교하여 가장 효율이 높은 Input Beam의 형태와 그 parameter에 대해 연구하였다.