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-0432013 (1989-11-06) |
발명자 / 주소 |
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출원인 / 주소 |
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인용정보 | 피인용 횟수 : 353 인용 특허 : 0 |
A time series of successive relatively high-resolution frames of image data, any frame of which may or may not include a graphical representation of one or more predetermined specific members (e.g., particular known persons) of a given generic class (e.g. human beings), is examined in order to recog
A time series of successive relatively high-resolution frames of image data, any frame of which may or may not include a graphical representation of one or more predetermined specific members (e.g., particular known persons) of a given generic class (e.g. human beings), is examined in order to recognize the identity of a specific member if that member\s image is included in the time series. The frames of image data may be examined in real time at various resolutions, starting with a relatively low resolution, to detect whether some earlier-occurring frame includes any of a group of image features possessed by an image of a member of the given class. The image location of a detected image feature is stored and then used in a later-occurring, higher resolution frame to direct the examination only to the image region of the stored location in order to (1) verify the detection of the aforesaid image feature, and (2) detect one or more other of the group of image features, if any is present in that image region of the frame being examined. By repeating this type of examination for later and later occurring frames, the accumulated detected features can first reliably recognize the detected image region to be an image of a generic object of the given class, and later can reliably recognize the detected image region to be an image of a certain specific member of the given class.
A dynamic image-processing method for recognizing objects of a given class graphically represented in a time series of successive relatively high-resolution frames of image data; said method being responsive to (A) a stored program for controlling said image-processing and for specifying a set of de
A dynamic image-processing method for recognizing objects of a given class graphically represented in a time series of successive relatively high-resolution frames of image data; said method being responsive to (A) a stored program for controlling said image-processing and for specifying a set of decision criteria, and (B) stored data; wherein objects of said given class all possess a group of known generic attributes which, taken as a whole, distinguish objects of said given class from objects not of said given class; and wherein said stored data initially defines a limited number of separate features related to said group of generic attributes, any of which initially stored features is likely to be present in a sequence of one or more successive frames of image data if an object of said given class is graphically represented in that sequence; said method comprising the steps of: a) under the control of said stored program and in response to at least a first one of said separate features initially defined by said stored data, making a first determination in accordance with said decision criteria as to a first probability that one or more relatively early-occurring frames of said time series may include as part of said image data thereof at least said first one of said separate features of objects of said given class; b) in response to said first probability being at least equal to a first predetermined threshold value, adding data defining at least the relative location of said part within the relatively early-occurring frames of said sequence to said stored data, thereby enhancing said stored data; c) under the control of said stored program and in response to said enhanced stored data, making a second determination in accordance with said decision criteria as to a second probability that one or more relatively later-occurring frames of said time series verifies said part as including at least a second one of said separate features in addition to said first feature; d) in response to said second probability being above a second predetermined threshold, recognizing said part as being a graphical representation of an object of said given class; e) in response to said second probability being below a third predetermined threshold which third predetermined threshold is significantly below said second predetermined threshold, recognizing said part as not being a graphical representation of an object of said given class; f) in response to said second probability being in a range between said second and third probabilities, adding further data defined by the verification of step c) to said stored data, thereby further enhancing said stored data; and g) if said second probability is in said range, recursively repeating steps c) and f) for relatively later and later occurring sequences of one or more frames of said time series until the probability determined by such repeated step d) either rises above said second predetermined threshold or falls below said third predetermined threshold.
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