최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
DataON 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
Edison 바로가기다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
Kafe 바로가기국가/구분 | United States(US) Patent 등록 |
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국제특허분류(IPC7판) |
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출원번호 | US-0962684 (2001-09-26) |
발명자 / 주소 |
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출원인 / 주소 |
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대리인 / 주소 |
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인용정보 | 피인용 횟수 : 677 인용 특허 : 11 |
A minimally invasive medical stapling device comprises a housing that contains spring-loaded staples for use in a minimally invasive medical stapling procedure. When the physician pulls a shutter the springs are sequentially released causing the staples to be fired. The device provides the user with
A minimally invasive medical stapling device comprises a housing that contains spring-loaded staples for use in a minimally invasive medical stapling procedure. When the physician pulls a shutter the springs are sequentially released causing the staples to be fired. The device provides the user with tactile and audible feedback as the staples are fired.
A minimally invasive medical stapling device comprises a housing that contains spring-loaded staples for use in a minimally invasive medical stapling procedure. When the physician pulls a shutter the springs are sequentially released causing the staples to be fired. The device provides the user with
A minimally invasive medical stapling device comprises a housing that contains spring-loaded staples for use in a minimally invasive medical stapling procedure. When the physician pulls a shutter the springs are sequentially released causing the staples to be fired. The device provides the user with tactile and audible feedback as the staples are fired. on modes, wherein said motion tracker and predictor utilizes said plurality of predefined motion modes. 17. An image processing system as in claim 16, wherein said predefined motion modes comprise: a mode of crashing; a mode of being stationary; and a mode of being human. 18. An image classification system as in claim 11, wherein said shape tracker and predictor comprises: an update shape predictor; an update covariance and gain matrices generator; an update shape estimator; and a combined shape estimate generator. 19. An image classification system as in claim 11, wherein said motion tracker and predictor comprises: an update motion predictor; an update covariance and gain matrices generator; an update motion estimator; and a combined motion estimate generator. 20. An image processing system as in claim 11, wherein said shape tracker and predictor determines a sideways tilt angle of the occupant. 21. An image processing system for use with an airbag deployment system having a seat, an occupant in the seat, a sensor for capturing occupant images, an airbag, an airbag controller, said image processing system comprising: a segmentation subsystem, including an ambient image and a segmented image, said segmentation subsystem generating said segmented image from said ambient image; an ellipse fitting subsystem, including an ellipse, said ellipse fitting subsystem representing said ambient image with said ellipse; a tracking and predicting subsystem, including a plurality of occupant characteristics, said tracking an predicting subsystem generating said plurality of occupant characteristics from said ellipse and an impact assessment subsystem, including an impact metric, said impact assessment subsystem generating said impact metric from said plurality of occupant characteristics. 22. An image processing system as recited in claim 21, said tracking and predicting subsystem further including a plurality of past predictions, wherein said plurality of past predictions are incorporated into said plurality of occupant characteristics. 23. An image processing system as recited in claim 22, said tracking and predicting subsystem applying a plurality of Kalman filter to incorporate said plurality of past predictions into said plurality of occupant characteristics. 24. A method for determining airbag deployment strength, comprising the steps of: applying a plurality of mathematical heuristics to a plurality of image characteristics to incorporate past measurements and past predictions into a plurality of updated occupant characteristic predictions, and calculating an impact metric representing the magnitude of the impact between the occupant and the airbag from the updated occupant characteristic predictions. 25. A method for determining airbag deployment strength as recited in claim 24, wherein the plurality of mathematical heuristics are Kalman filters. 26. A method for determining airbag deployment strength as recited in claim 24, wherein the impact metric is the kinetic energy of the occupant.
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