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公开(公告)号：US20130340507A1
公开(公告)日：2013-12-26
申请号：US13527769
申请日：2012-06-20
IPC分类号： G01N11/16
CPC分类号： G01N11/16 , G01N2011/0046
摘要： A viscosity measuring instrument for measuring ceramic slurries, e.g. in a casting tank, with a probe having an elbow and presenting a transducer part in substantial alignment with a flow direction of the ceramic slurry relative to the active part and a barrier orthogonal to such relative movement direction is interposed in front of the transducer part by one or more stand-off rods to form a partial enclosure that moderates flow to the active part and provides a long term stable measuring capability.
摘要翻译： 用于测量陶瓷浆料的粘度测量仪器，例如 在铸造槽中，具有肘部的探针并且相对于活性部分呈现与陶瓷浆料的流动方向基本对准的换能器部分，并且与该相对运动方向垂直的障碍物通过 一个或多个分离杆以形成部分外壳，其缓和流向有源部件的流动并提供长期稳定的测量能力。
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公开(公告)号：US09799098B2
公开(公告)日：2017-10-24
申请号：US12597406
申请日：2008-04-24
申请人： H. Sebastian Seung , Joseph F. Murray , Viren Jain , Srinivas C. Turaga , Moritz Helmstaedter , Winfried Denk
发明人： H. Sebastian Seung , Joseph F. Murray , Viren Jain , Srinivas C. Turaga , Moritz Helmstaedter , Winfried Denk
CPC分类号： G06T5/001 , G06K9/342 , G06K9/4628 , G06T7/11 , G06T2207/20084
摘要： Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.
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公开(公告)号：US20100183217A1
公开(公告)日：2010-07-22
申请号：US12597406
申请日：2008-04-24
申请人： H. Sebastian Seung , Joseph F. Murray , Viren Jain , Srinivas C. Turaga , Moritz Helmstaedter , Winfried Denk
发明人： H. Sebastian Seung , Joseph F. Murray , Viren Jain , Srinivas C. Turaga , Moritz Helmstaedter , Winfried Denk
IPC分类号： G06K9/62
CPC分类号： G06T5/001 , G06K9/342 , G06K9/4628 , G06T7/11 , G06T2207/20084
摘要： Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.
摘要翻译： 识别图像中的对象是一个困难的问题，特别是在原始图像嘈杂或具有颜色或灰度梯度窄的区域的情况下。 已经鉴定了采用卷积网络的技术，以自动和快速的方式识别这些图像中的对象。 一个示例性实施例训练包括多层滤波器的卷积网络。 过滤器通过学习进行训练，并且以连续的层布置并产生具有与原始图像至少相同分辨率的图像。 根据原始图像或期望的图像标记来对滤波器进行训练; 报告原始图像中识别的对象的图像标签，并可用于分割。 该技术可以应用于例如神经电路或电子显微镜的图像。 相同的技术也可以应用于照片或视频的校正。
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公开(公告)号：US3439929A
公开(公告)日：1969-04-22
申请号：US3439929D
申请日：1968-05-09
申请人： JOSEPH F MURRAY
发明人： MURRAY JOSEPH F
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