• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.133-141
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• 2005

An edge is where the intensity of an image moves from a low value to high value or vice versa. The edges tell where objects are. their shape and size. and something about their texture. Many traditional edge operators are derivative based and perform reasonably well for simple noise-free images. In recent, statistical edge detectors for complex images with noises have been described. This paper compares and analysis the performance of statistical edge detectors based on the T test and Wilcoxon test, and mathematical edge detectors based on Sobel operator, and the well-known Canny detector and Wavelet transformation detector, and provides the implementation of these edge detectors using Java on the web.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.313-318
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• 2016

Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.549-555
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• 2006

An edge detection is an very important area in image processing and computer vision, General edge detection methods (Robert mask, Sobel mask, Kirsh mask etc) are a good performance to detect step edge in a image but are no good performance to detect step edge in a noses image. We suggested a step edge detection method based on image information measure and neutral network. Using these essential properties of step edges, which are directional and structural and whose gray level distribution in neighborhood, as a input vector to the BP neutral network we get the good result of proposed algorithm. And also we get the satisfactory experimental result using rose image and cell images an experimental and analysing image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.672-674
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• 2011

Edge contains important information to analyze the characteristics of the images. Edge detection has been a lot utilized in areas such as computer vision and image processing etc.. And sobel, prewitt, roberts are representative methods among the initially used methods. Although these conventional methods easily detect large change points of slope of brightness, optional directional edge detection abilities of conventional edge detection are not efficient. Therefore, in this paper, to complement the shortcomings of existing edge detection method, we proposed adaptive edge detection method considering direction information. through simulatioopn, we verify abilities of our method.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.129-140
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• 2003

This study was to develop a fruit sorting system using statistical image processing. Histogram was used to compare fruit colors to standard fruit color and edge detector using Wilcoxon test was used to calculate an accurate geometrical characteristics of fruit including perimeter, area, major axis and minor axis length and roundness. The experimental result obtained from using our system for sorting apples was presented.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.359-371
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• 2011

Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1518-1523
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• 2012

Due to development of visual media in various industrial sectors, the importance of image processing is increasing. Among the various image processing areas, edge detection is utilized widely for various fields such as object recognition, object segmentation, the medical and other industries. Edge includes the critical factors of images like size, direction and location. Then conventional methods such as Sobel, Prewitt, Roberts and Laplacian are proposed to detect edge. However, edge detection property of these methods is declined when they are applied to the image which corrupted by AWGN(Additive White Gaussian Noise). Therefore, an algorithm using modified weighted filter is proposed in this paper and our method has excellent property on edge detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.770-772
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• 2013

Edges on the images are widely used in preprocessing in various areas including recognition and detection of the object. As generally known edge detection methods, there is a method using mask and these methods are Sobel, Prewitt, Roberts, Laplacian operator and etc. Implementation of these existing edge detection methods is simple. However, when processing the impulse noise added images, the properties of edge detection is not sufficient. Accordingly, in order to compensate for the weakness of existing edge detection methods and to detect strong edges on the images which were damaged by impulse noise, the edge detection algorithm using transformed mask was proposed in this paper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.782-784
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• 2013

As digital image processing technologies are developing, edges are being utilized in various areas. In the existing edge detection methods, there are mask methods which utilize Sobel, Prewitt, Roberts, Laplacian operator etc. To realize these existing edge detection methods is simple. But, in case that AWGN(additive white Gaussian noise) added images are processed, edge detection characteristics are slightly insufficient. Therefore, the edge detection algorithm using the standard deviation of local mask was suggested in this paper to compensate for the drawbacks in the existing detection methods and the suggested algorithm in AWGN environments showed excellent edge detection characteristics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.921-923
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• 2015

Edge in the image includes the size, direction and location of objects. The existing detection methods for detecting this edge is a method using Sobel, Prewitt, Roberts and Laplacian, etc. These existing methods use a fixed weighted mask in order to detect the edge and have somewhat insufficient edge detection characteristics. Therefore in this paper, an algorithm that detects the edge by applying the grey level converting function according to the pixel distribution of local mask was proposed.