• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1865-1867
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• 2007

We propse a novel corner detection algorithm for locating 2D Data Matrix barcode in an image. The proposed method accumulates weight for each cross point defined by every combination of edge points in the image, and detects the corner point of the barcode L-pattern by determining the location of the highest accumulated weight. By designing the weight considering the connectivity of two lines around the cross point, we were able to detect the corner of L-pattern even for the cases that the lines of L-patterns are short. In the experiments, the proposed method showed improved performance compared with the conventional Hough transform based method in terms of detectability and computation time.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.781-784
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAx clusterng algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action. But current segmentation methods based edge extraction methods generally need the mask information for the algebraic model, and take long run times at mask operation, wheras the proposed algorithm has single operation ccording to active searching of fuzzy worms. In addition, we also genetic min-max clustering using genetic algorithm to complete clustering and fuzyz searching on grey-histogram of image for the optimum solution, which can automatically determine the size of rnages and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1284-1287
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• 2002

A fast algorithm for a circular pattern recognition from a binary edge image is proposed in this paper. The implementation of this algorithm onto an FPGA is designed using Verilog-HDL where a target device is Altera EPF10K100ARC240-3. For a 256 ${\times}$ 256-pixe1 binary edge image assuming a real watermelon in a greenhouse, improved circuit performance of the proposed design was confirmed.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.759-764
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• 1998

We propose a novel algorithm capable of recognizing the road lane by image processing. Considering the fact that the direction and location of road lane are maintained similarly in successive images we formulate a function to represent the property. However, as noises play the role of making a lot of similar patterns appear and disappear in the road image, keeping of robustness in the lane detection has been known a difficult work. To overcome this problem, we introduce the following three ideas: 1) design of a function based on an edge direction and magnitude, 2) construction of a recursive filter to estimate the function recursively for successive images, 3) principal axis-based line fitting. These concepts enhance the adaptability to cope with the random environment of traffic scene and eventually lead to the reliable detection of a road lane.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.115-122
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• 2008

Wu and Tsai's pixel-value differencing method and Chang and Tseng's side-match method are based on the theory that the number of bits which can be embedded is determined by the degree of the pixel's smoothness, or its proximity to the edge of the image. If pixels are located in the edge area, they may tolerate larger changes than those in smooth areas. However, both methods are subject to the fall off the boundary problem(FOBP). This study proposes a new scheme that can solve the FOBP. The experimental results demonstrate that the proposed method resolves the problem, and achieves a higher image quality index value than other methods.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.259-264
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• 2007

According to the development and wide use of broad band internet etc., diverse application technologies using large capacity data such as images have been progressed and in these systems, for accurate acquisition and precise applications of an original signal, the degradation phenomenon generated in the transmission process etc. should be removed. Noises have become known as the main cause of the degradation phenomenon and especially Gaussian noise represents characteristics occurring dependently in image signals and degrades detail information such as edge. In this paper, we removed Gaussian noise using a subdivided nonlinear function according to a threshold value and analyzed the histogram acquired from an edge image to establish a threshold value adaptively, and strengthened detail information of image by using the postprocessing. In simulation results, the proposed method represented excellent performance from comparison of MSE with existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1677-1684
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• 1996

The theory of optimal stack filtering has been used in difference of estimates(DoE) approach to the detection of intensity edges in noisy image. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates produces the estimated edge map. In this paper, the DoE approach is modified by imposing a symmetry condition of the data used to train the two stack filers. Under this condition, the stack filters obtained are duals of each other. Only one filter must therefore be trained;the other is simply its dual. They also produce statistially unbiased estimates. This new technique is called the symmetric Difference of Estimates (SDoE) approach.