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公开(公告)号：US09665962B2
公开(公告)日：2017-05-30
申请号：US14812841
申请日：2015-07-29
发明人： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
CPC分类号： G06T11/60 , G06K9/4671 , G06T5/002 , G06T5/005 , G06T7/11 , G06T2207/10004 , G06T2207/20081
摘要： Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
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公开(公告)号：US10134165B2
公开(公告)日：2018-11-20
申请号：US15597911
申请日：2017-05-17
发明人： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
摘要： Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
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公开(公告)号：US20170249769A1
公开(公告)日：2017-08-31
申请号：US15597911
申请日：2017-05-17
发明人： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
CPC分类号： G06T11/60 , G06K9/4671 , G06T5/002 , G06T5/005 , G06T7/11 , G06T2207/10004 , G06T2207/20081
摘要： Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
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公开(公告)号：US20170032551A1
公开(公告)日：2017-02-02
申请号：US14812841
申请日：2015-07-29
发明人： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
CPC分类号： G06T11/60 , G06K9/4671 , G06T5/002 , G06T5/005 , G06T7/11 , G06T2207/10004 , G06T2207/20081
摘要： Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
摘要翻译： 描述图像牵引器检测和处理技术。 在一个或多个实现中，数字媒体环境被配置用于图像牵引器检测，其包括自动检测图像内的一个或多个位置，并且不需要包括一个或多个可能被考虑的干扰物的一个或多个计算设备的用户干预 由使用者分心图像内的内容。 所述检测包括由所述一个或多个计算装置从所述图像形成多个片段，并且计算所述多个片段中的每一个的分数，所述片段指示相应的所述片段被认为是所述图像内的牵引器的相对似然性。 使用使用机器学习训练的牵引器模型来应用计算，该干扰模型应用于具有地面真实牵引器位置的多个图像。
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