• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.174-180
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• 2005

Points of sharp variations in image are the most important components when we analyze the features of image. And they include a variety of information about image's shape and location etc. So a lot of researches for detecting edges have been continued. Edge detection operators which were used at the early stage of the research were to utilize relations among neighboring pixels. These methods detect edge at all boundaries, therefore they perform edge detection twice about curves below some width such as line-edge. In the meantime, wavelet transform which is presented as a new technique of signal processing field provides multiscale edge detection and is being applied widely in many fields that analyze edge-like characteristic. Therefore, in this paper we detected line-edge with new 2-D wavelet function which is independent of line's width.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.89-96
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• 2009

In this paper, we propose an efficient deinterlacing algorithm which is an edge dependent interpolation based on edge direction refinement. The conventional edge dependent interpolation algorithms have a visually better performance than any other Intra-field deinterlacing algorithms. However they are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method detects edge direction of around interpolated pixel and refines the edge direction using weighted median filter. Simulation results have shown the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.85-88
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• 2001

Unlike rigid objects or This paper developes the algorithm for segmenting gaseous objects on an image plane. Unlike rigid objects or solid non-rigid objects, gaseous objects vary in density even within single-object regions and the edge intensity differs at different locations. So, an edge detector may detect only strong edges and detected edges may be an incomplete parts of an whole object's boundary. Due to this property of gaseous objects, it is not easy to distinguish the real edges of gaseous objects from the noisy-like edges such as leaves. Our algorithm uses two criteria of edge intensity and edge's line connectivity, then applies fuzzy set so as to obtain the proper threshold of the edge detector

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.72-80
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• 1996

In this paper, variable windowing mean filter to remove speckle noise and a measure to detect thin edge in ultrasonic images are proposed. Because ultrasonic images are corrupted by speckle noise showing a granular appearance, good edge detection is difficult. As a result, noise removing filter is needed in preprocessing stage. The speckle noise removing filter is based on mean filter whose window size is changed by the ratio of standard deviation to mean for image signal and noise signal in local area. And the measure expressed the difference of means between tow windows is used for detecting thin edge in filtered image. Results show that variable windowing mean filter removes speckle noise effectively, and proposed measure is useful in detecting thin edge.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.515-523
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• 2000

In this paper we detect edges using stutistical methods of the change-point problem. For this, we perform the hypothesis testing for differences in gray levels to see whether any $n\timesn$ subimage contains edge segments. The proposed method based on the twosample Kolmogorov-Smirnov test is introduced and the likelihood ratio test and the \VolfeSchechtman test for change-point problem arc also applied for edge detection. \Ve perform the experimental study to assess the performance of these methods in both noisy and uncontaminated sample noises.

• Convergence Security Journal
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• v.6 no.3
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• pp.29-35
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• 2006

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.303-308
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• 2012

One dimensional line edge detection sensor was investigated for the application to detect line or edge of wide sheet winding to cylindrical roller. Line edge detection sensor was realized using align-free 1:1 optical system having 1 convex lens and by processing sum or difference of signals from two photo-diodes in a bi-cell photodetector. Line width up to 0.1mm on object having various materials and colors was detectable, and the deviation of wound sheet was found to be under 0.2mm.

• Convergence Security Journal
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• v.7 no.3
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• pp.39-44
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• 2007

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.12-14
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• 2006

This paper presents a novel scene change detection method using intra prediction mode and edge direction in H.264/AVC. When scene change occurs, there are less temporal correlation between frames, most of macro-blocks encoded in intra mode. Using this property, the method calculates the percentage of intra mode blocks in each predictive frame in order to get candidates of scene change frame. To further find scene change, we obtain edge histogram of each candidates by using eight prediction direction of intra prediction mode in H.264/AVC. We detect scene change frames with $\iota^1$-norm of edge histograms. The experimental results show that the method is efficient and robust.