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

IPC分类号： G06K9/40 ,  G06T5/00 ,  G06K9/34 ,  G06K9/46 ,  G06T7/11

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.

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。