公开(公告)号：US20240355107A1

公开(公告)日：2024-10-24

申请号：US18684883

申请日：2021-08-23

Applicant: Google LLC

Inventor： Orly Liba ,  Michael Garth Milne ,  Navin Padman Sarma ,  Doron Kukliansky ,  Huizhong Chen ,  Yael Pritch Knaan

IPC: G06V10/82 ,  G06T5/60 ,  G06V10/46

CPC classification number: G06V10/82 ,  G06T5/60 ,  G06V10/462 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06V2201/07 ,  G06V2201/10

Abstract: A method includes receiving training data comprising a plurality of images. one or more identified objects in each of the plurality of images. and a detection score associated with each of the one or more identified objects. wherein the detection score for an object is indicative of a degree to which a portion of an image corresponds to the object. The method also includes training a neural network based on the training data to predict a distractor score for at least one object of the one or more identified objects in an input image, wherein the at least one object is selected based on an associated detection score, and wherein the distractor score for the at least one object is indicative of a perceived visual distraction caused by a presence of the at least one object in the input image. The method additionally includes outputting the trained neural network.

公开(公告)号：US20230360182A1

公开(公告)日：2023-11-09

申请号：US18028930

申请日：2021-05-17

Applicant: Google LLC

Inventor： Sean Ryan Francesco Fanello ,  Yun-Ta Tsai ,  Rohit Kumar Pandey ,  Paul Debevec ,  Michael Milne ,  Chloe LeGendre ,  Jonathan Tilton Barron ,  Christoph Rhemann ,  Sofien Bouaziz ,  Navin Padman Sarma

IPC: G06T5/00 ,  G06T7/60 ,  G06T7/70 ,  G06T15/50

CPC classification number: G06T5/009 ,  G06T7/60 ,  G06T7/70 ,  G06T15/506 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20092 ,  G06T2207/20208 ,  G06T2207/30201

Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. An example method includes applying a geometry model to an input image to determine a surface orientation map indicative of a distribution of lighting on an object based on a surface geometry. The method further includes applying an environmental light estimation model to the input image to determine a direction of synthetic lighting to be applied to the input image. The method also includes applying, based on the surface orientation map and the direction of synthetic lighting, a light energy model to determine a quotient image indicative of an amount of light energy to be applied to each pixel of the input image. The method additionally includes enhancing, based on the quotient image, a portion of the input image. One or more neural networks can be trained to perform one or more of the aforementioned aspects.