• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.76-83
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• 2010

In this paper, we propose a method that converts a inputted real image to a image feeling like printmaking. That is, this method converts a inputted real image to man made rubber printmaking style image using image processing techniques such as spatial filters, image bit-block transfer, etc. The process is as follows. First, after detecting edges in source image, we get the first image by deleting noise lines and points, then by sharpening. Secondly, we get second image using the similar method to the first image. Finally, we blend the first and the second image by logical AND operation This processing enables us to represent rubber panel and knife effects. Also, the proposed method shows that double edge detecting is effective in enhancing line-width and removing the tiny lines.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.73-84
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• 1995

New morphological positioning algorithm and inspection method are presented and compared with a method by means of the Hough transform. The positioning algorithm is the process of finding the center and the rotated angle of the surface mounted device (SMD). The inspection method is capable of detecting the location of broken or bent leads. In order to obtain the center and the orientation of the SMD rapidly, the Hough transform method utilizes feature points (concave points) and is executed on a DSP board. The proposed morphological method is implemented by using the morphological skeleton subsets, and an ultimate orientation is decided by the Hit-or-Miss transform (HMT). In the inspection process, two inspection methods also are presented. The first method utilizes the morphological methods, i.e., opening and closing. It is performed before the positioning process and called an initial inspection. The second method follows the positioning process and is performed by an inspection of intersections of rulers and the lead edge (or the skeleton). It is a ruling technique which is referred to as a detailed inspection. We find the morphological approach is preciser and faster than the Hough approach by the comparison of the proposed algorithms.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.79-85
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• 2017

In this paper, several optimizations are proposed to enhance the quality of lane detection algorithms in automotive applications. Considering the diagonal directions of lanes, the proposed limited Hough transform newly introduces image-splitting and angle-limiting schemes that relax the number of possible angles at the line voting process. In addition, unnecessary edges along the horizontal and vertical directions are pre-defined and removed during the edge detection procedures, increasing the detecting accuracy remarkably. Simulation results shows that the proposed lane recognition algorithm achieves an accuracy of more than 90% and a computing speed of 92 frame/sec, which are superior to the results from the previous algorithms.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.86-93
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• 2017

In this paper, we propose a novel defect detection method using component tree representations of scanning electron microscopy (SEM) images. The component tree contains rich information about the topological structure of images such as the stiffness of intensity changes, area, and volume of the lobes. This information can be used effectively in detecting suspicious defect areas. A quasi-linear algorithm is available for constructing the component tree and computing these attributes. In this paper, we modify the original component tree algorithm to be suitable for our defect detection application. First, we exclude pixels that are near the ground level during the initial stage of component tree construction. Next, we detect significant lobes based on multiple attributes and edge information. Our experiments performed with actual SEM wafer images show promising results. For a $1000{\times}1000$ image, the proposed algorithm performed the whole process in 1.36 seconds.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.418-422
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• 2005

In this paper, a novel algorithm capable of detecting generic obstacles on a flat surface is proposed. The algorithm fundamentally exploits a distortion phenomena taken place in remapping process of original stereo images to a virtual top-view. Based on the distortion phenomena, we construct stereo polar histograms of edge maps, detect peaks on them, and search for matched peaks on both histograms using a Dynamic Programming (DP). Eventually, the searched corresponding peaks lead to estimate obstacles' positions. The advantages of the proposed algorithm are that it is not largely affected by an intensity difference between a pair of stereo images and does not depend on the typical stereo matching methodologies. Furthermore, the algorithm identifies the obstacles' positions quite robustly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.266-270
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• 2004

The image interpolation is one of an image preprocessing process to heighten a resolution. The conventional image interpolation used much to concept that it put in other pixel to select the nearest value in a pixel simply, and use much the temporal object interpolation techniques to do the image interpolation by detecting motion in a moving picture presently. In this paper, it is proposed the image interpolation techniques using the fuzzy wavelet base function. This is applied to embody a correct edge image and a natural image when expand part of the still image by applying the fuzzy wavelet base function coefficient to the conventional B-spline function. And the proposal algorithm in this paper is confirmed to improve about 1.2831 than the image applying the conventional B-spline function through the computer simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.55-62
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• 1998

Matching two images is an essential step for many computer vision applications. A new approach to the scale and rotation invariant scene matching, using linear features, is presented. Scene or model images are described by a set of linear features approximating edge information, which can be obtained by the conventional edge detection, thinning, and piecewise linear approximation. A set of candidate parameters are hypothesized by mapping the angular difference and a new distance measure to the Hough space and by detecting maximally consistent points. These hypotheses are verified by a fast linear feature matching algorithm composed of a single-step relaxation and a Hough technique. The proposed method is shown to be much faster than the conventional one where the relaxation process is repeated until convergence, while providing matching performance robust to the random alteration of the linear features, without a priori information on the geometrical transformation parameters.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.19-25
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• 2005

Nowadays, it is possible to construct the DEMs of urban area effectively and economically by LiDAR system. But the data from LiDAR system has form of DSM which is included various objects as trees and buildings. So the preprocess is necessary to extract the DEMs from LiDAR DSMs for particular purpose as effects analysis of man-made objects for flood prediction. As this study is for extracting DEM from LiDAR DSM of urban area, we detected the edges of various objects using edge detecting algorithm of image process. And, we tried mean value filtering, median value filtering and minimum value filtering or detected edges instead of interpolation method which is used in the previous study and could be modified the source data. it could minimize the modification of source data, and the extracting process of DEMs from DSMs could be simplified and automated.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.705-711
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• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.