Florian Kainz

US Patent:

7945892, May 17, 2011

Filed:

Oct 20, 2008

Appl. No.:

12/254597

Inventors:

Florian Kainz - San Rafael CA, US
Charles Allen Kilpatrick - San Francisco CA, US

Assignee:

Lucasfilm Entertainment Company Ltd. - San Francisco CA

International Classification:

G06F 9/44

US Classification:

717105, 345440

Abstract:

Changes to the outputs of a dependency graph, which has attribute nodes and operator nodes, are detected by forming product proxies for the outputs of dependency graph nodes. The product proxies can be formed by entering information about the dependency graph into a hashing algorithm. Before and after product proxies are then used to determine if the outputs have changed.

US Patent:

20070080964, Apr 12, 2007

Filed:

Oct 7, 2005

Appl. No.:

11/245642

Inventors:

Florian Kainz - San Rafael CA, US
Charles Kilpatrick - San Francisco CA, US

International Classification:

G06T 11/20

US Classification:

345440000

Abstract:

Changes to the outputs of a dependency graph, which has attribute nodes and operator nodes, are detected by forming product proxies for the outputs of dependency graph nodes. The product proxies can be formed by entering information about the dependency graph into a hashing algorithm. Before and after product proxies are then used to determine if the outputs have changed.

US Patent:

20220230323, Jul 21, 2022

Filed:

Jul 15, 2019

Appl. No.:

17/617560

Inventors:

- Mountain View CA, US
Florian Kainz - San Rafael CA, US
Longqi Cai - Mountain View CA, US
Yael Pritch Knaan - Mountain View, ID

Assignee:

Google LLC - Mountain View CA

International Classification:

G06T 7/11
G06T 5/00
G06V 10/764

Abstract:

A device automatically segments an image into different regions and automatically adjusts perceived exposure-levels or other characteristics associated with each of the different regions, to produce pictures that exceed expectations for the type of optics and camera equipment being used and in some cases, the pictures even resemble other high-quality photography created using professional equipment and photo editing software. A machine-learned model is trained to automatically segment an image into distinct regions. The model outputs one or more masks that define the distinct regions. The mask(s) are refined using a guided filter or other technique to ensure that edges of the mask(s) conform to edges of objects depicted in the image. By applying the mask(s) to the image, the device can individually adjust respective characteristics of each of the different regions to produce a higher-quality picture of a scene.