• 한국정보전자통신기술학회논문지
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• 제2권3호
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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.

• 한국정보전자통신기술학회논문지
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• 제6권2호
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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.

• 한국정보전자통신기술학회논문지
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• 제7권1호
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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.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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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.

• 한국의학물리학회지:의학물리
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• 제10권3호
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• pp.115-123
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• 1999

2 차원 심초음파는 좌심실벽 운동을 실시간으로 보여줄 수 있어 국소 좌심실벽 운동장애를 진단하는 데 널리 사용되고 있다. 심초음파를 통하여 국소 좌심실벽 운동기능을 평가하고 정량화하기 위한 많은 연구들이 진행되어왔다. 본 논문에서는 국소 좌심실벽 운동장애의 진단을 위한 좌심실벽 운동 추 적 및 정량적 분석 알고리듬을 제안하였다. 정상 피검자들과 국소 좌심실벽 운동이상 환자로부터 얻은 심초음파 흉골연단축단면 영상들이 알고리듬의 시험을 위해 사용되었다. 자동화된 경계선 검출과 좌심실내벽 윤곽선 연결 알고리듬을 각 프레임들에 적용하였고 영역분할에 기초한 정량적 분석을 수행하였으며 운동량을 의미하는 칼라가 프레임 및 영역별로 심초음파 영상들 위에 덧씌워졌고 칼라화된 영상들이 동영상으로 구현되었다. 제안된 알고리듬은 좌심실벽 운동장애의 자동화된 정량적 진단을 제공하였다.