The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 24, 2017

Filed:

Apr. 24, 2008
Applicants:

H. Sebastian Seung, Cambridge, MA (US);

Joseph F. Murray, Boston, CA (US);

Viren Jain, Cambridge, MA (US);

Srinivas C. Turaga, Cambridge, MA (US);

Moritz Helmstaedter, Heidelberg, DE;

Winfried Denk, Heidelberg, DE;

Inventors:

H. Sebastian Seung, Cambridge, MA (US);

Joseph F. Murray, Boston, CA (US);

Viren Jain, Cambridge, MA (US);

Srinivas C. Turaga, Cambridge, MA (US);

Moritz Helmstaedter, Heidelberg, DE;

Winfried Denk, Heidelberg, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/40 (2006.01); G06T 5/00 (2006.01); G06K 9/34 (2006.01); G06K 9/46 (2006.01); G06T 7/11 (2017.01);
U.S. Cl.
CPC ...
G06T 5/001 (2013.01); G06K 9/342 (2013.01); G06K 9/4628 (2013.01); G06T 7/11 (2017.01); G06T 2207/20084 (2013.01);
Abstract

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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