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:
Aug. 11, 2026

Filed:

Feb. 06, 2024
Applicant:

Koninklijke Philips N.v., Eindhoven, NL;

Inventors:

Jan Marek May, Moscow, RU;

Johannes Koepnick, Neumünster, DE;

Bernd Lundt, Flintbek, DE;

Assignee:

KONINKLIJKE PHILIPS N.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 11/60 (2026.01); G06V 10/74 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 11/60 (2013.01); G06V 10/761 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06T 2210/41 (2013.01);
Abstract

The invention concerns an image processing system configured for carrying out a computer-implemented method for generating a stitched image with a convolutional neural network (), a computer-implemented method for generating a stitched image with a convolutional neural network, and a method of training a convolutional neural network, for determining an image representation for image stitching. The training of the convolutional neural network comprises receiving image data, wherein the image data comprises at least two images (), overlapping each other in an overlapping area () in which the images overlapping each other in a target anatomy (), and wherein the overlapping area comprises an optimum displacement between the two images such that the two images can be correctly combined for image stitching, determining a cost function () of the displacement of the two images in the overlapping area, determining a deviation of the cost function from a reference cost function, and optimizing the CNN based on the deviation.


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