• 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 KIISE:Software and Applications
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• v.31 no.3
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• pp.276-292
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• 2004

In this paper. we present methods that extract lung regions from chest EBT(electron beam tomography) images then segment the extracted lung region into lung lobes. We use histogram based thresholding and mathematical morphology for extracting lung regions. For detecting pulmonary fissures, we use edge detector and knowledge-based search method. We suggest this edge detector, which uses adaptive filter scale, to work very well for real edge and insensitive for edge by noise. Our experiments showed about 95% accuracy or higher in extracting lung regions and about 5 pixel distance error in detecting pulmonary fissures.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.575-591
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• 2013

Most of the edge detection methods available in literature are gradient based, which further apply thresholding, to find the final edge map in an image. In this paper, we propose a novel method that is based on fuzzy logic for edge detection in gray images without using the gradient and thresholding. Fuzzy logic is a mathematical logic that attempts to solve problems by assigning values to an imprecise spectrum of data in order to arrive at the most accurate conclusion possible. Here, the fuzzy logic is used to conclude whether a pixel is an edge pixel or not. The proposed technique begins by fuzzifying the gray values of a pixel into two fuzzy variables, namely the black and the white. Fuzzy rules are defined to find the edge pixels in the fuzzified image. The resultant edge map may contain some extraneous edges, which are further removed from the edge map by separately examining the intermediate intensity range pixels. Finally, the edge map is improved by finding some left out edge pixels by defining a new membership function for the pixels that have their entire 8-neighbourhood pixels classified as white. We have compared our proposed method with some of the existing standard edge detector operators that are available in the literature on image processing. The quantitative analysis of the proposed method is given in terms of entropy value.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of radiological science and technology
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• v.35 no.4
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• pp.309-314
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• 2012

Nowadays, digital Radiography (DR) systems are widely used in clinical sites and substitute the analog-film x-ray imaging systems. The resolution of DR images depends on several factors such as characteristic contrast and motion of the object, the focal spot size and the quality of x-ray beam, x-ray scattering, the performance of the DR detector (x-ray conversion efficiency, the intrinsic resolution). The DR detector is composed of an x-ray capturing element, a coupling element and a collecting element, which systematically affect the system resolution. Generally speaking, the resolution of a medical imaging system is the discrimination ability of anatomical structures. Modulation transfer function (MTF) is widely used for the quantification of the resolution performance for an imaging system. MTF is defined as the frequency response of the imaging system to the input of a point spread function and can be obtained by doing Fourier transform of a line spread function, which is extracted from a test image. In clinic, radiologic technologists, who are in charge of system maintenance and quality control, have to evaluate or make routine check on their imaging system. However, it is not an easy task for the radiologic technologists to measure MTF accurately due to lack of their engineering and mathematical backgrounds. The objective of this study is to develop and provide for radiologic technologists a medical system imaging evaluation tool, so that they can measure and quantify system performance easily.