• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.31-38
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• 2009

A process of identifying moving objects from data is typical task in many computer vision applications. In this paper, we propose a motion segmentation method that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modelling. To demonstrate the effectiveness of proposed approach, we tested it gray-scale video data as well as RGB color space.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.81-87
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• 2013

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.61-67
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• 2014

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter(AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.579-582
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• 1997

The two dimnesional echocardiography is widely used to evaluate regional wall motion abnormaility, because of its abilities to depict left ventricluar wall motion. A new method, color kinesis is a technology or echocardiographic assessment of left ventricular wall motion. In this paper, we proposed a algorithm or color kinesis which is based on acoustic quantification and automatically detects endocardial motion during systole on a frame-by-frame basis. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracing algorithm was applied to each frames, quantitative analysis based on segmentation was performed, and pre-defined color overlays superimposed on the gray scale images. Segmental analysis of color kinesis provided automated, quantitative diagnosis of regional wall motion abnormality.

• Progress in Medical Physics
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• v.10 no.3
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• pp.115-123
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• 1999

The two dimensional echocardiography is widely used to evaluate regional wall motion abnormality, because of its abilities to depict left ventricular wall motion. A number of researches have been processed for evaluation and quantitative analysis of left ventricular wall motion functions. In this paper, we proposed an algorithm which detects automatically and analyze quantitatively endocardial wall motion during systole. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracking algorithm was applied to each frames, quantitative analysis based on segmentation was performed, pre-defined color overlays superimposed on the gray scale images, and the images was animated. The proposed algorithm provided automated, quantitative diagnosis of regional wall motion abnormality.