Kunlong C Gu

US Patent:

7657086, Feb 2, 2010

Filed:

Jan 31, 2006

Appl. No.:

11/342588

Inventors:

Kunlong Gu - Sunnyvale CA, US

Assignee:

Fujifilm Corporation - Tokyo

International Classification:

G06K 9/62
G06K 9/46
G06K 9/66

US Classification:

382159, 382190, 382228, 382118

Abstract:

A method and an apparatus automatically detect eyeglasses in an image. The method according to one embodiment accesses digital image data representing an image including a face; detects eyeglasses in the image by using nose ridge masking; and outputs a decision about presence or absence of eyeglasses in the image.

US Patent:

8582840, Nov 12, 2013

Filed:

May 5, 2008

Appl. No.:

12/149566

Inventors:

Kunlong Gu - Belmont CA, US
Akira Hasegawa - Saratoga CA, US
Huzefa Neemuchwala - Sunnyvale CA, US

Assignee:

FUJIFILM Corporation - Tokyo

International Classification:

G06K 9/00

US Classification:

382128, 378 37

Abstract:

Methods and apparatuses perform thickness compensation in anatomical images. The method according to one embodiment accesses digital image data representing an image including a breast; estimates thickness of the breast at multiple locations inside the breast using an image data characteristic and a reference tissue in the breast; compensates thickness of the breast using a thickness model; and refines compensation of breast thickness from the compensating step.

US Patent:

20070177793, Aug 2, 2007

Filed:

Jan 31, 2006

Appl. No.:

11/342582

Inventors:

Kunlong Gu - Sunnyvale CA, US

International Classification:

G06K 9/62

US Classification:

382159000

Abstract:

A method and an apparatus automatically detect and remove eyeglasses from an image. The method according to one embodiment accesses digital image data representing an image including a face; detects eyeglasses in the image to produce a report about presence or absence of eyeglasses in the image; normalizes illumination of the image to obtain a normalized image; and removes eyeglasses from the normalized image to obtain a face image without eyeglasses.

US Patent:

20090060300, Mar 5, 2009

Filed:

Aug 30, 2007

Appl. No.:

11/896247

Inventors:

Huzefa Neemuchwala - Mountain View CA, US
Akira Hasegawa - Saratoga CA, US
Kunlong Gu - Belmont CA, US

International Classification:

G06K 9/00

US Classification:

382128

Abstract:

Methods and apparatuses align breast images. The method according to one embodiment accesses digital image data representing a first breast image including a left breast, and a second breast image including a right breast; removes from the first and second breast images artifacts not related to the left and right breasts; and aligns the left and right breasts using a similarity measure between the first and second breast images, the similarity measure depending on a relative position of the first and second breast images.

US Patent:

20230051565, Feb 16, 2023

Filed:

Aug 10, 2021

Appl. No.:

17/398436

Inventors:

- Mountain View CA, US
Timothy Yang - Redwood City CA, US
Kunlong Gu - Belmont CA, US
Marshall Friend Tappen - Bainbridge Island WA, US

International Classification:

G06K 9/62
G06N 3/04
G06K 9/00

Abstract:

A method for determining hard example sensor data inputs for training a task neural network is described. The task neural network is configured to receive a sensor data input and to generate a respective output for the sensor data input to perform a machine learning task. The method includes: receiving one or more sensor data inputs depicting a same scene of an environment, wherein the one or more sensor data inputs are taken during a predetermined time period; generating a plurality of predictions about a characteristic of an object of the scene; determining a level of inconsistency between the plurality of predictions; determining that the level of inconsistency exceeds a threshold level; and in response to the determining that the level of inconsistency exceeds a threshold level, determining that the one or more sensor data inputs comprise a hard example sensor data input.

US Patent:

20150169738, Jun 18, 2015

Filed:

Jan 13, 2012

Appl. No.:

13/350143

Inventors:

Thomas J. Duerig - Sunnyvale CA, US
Charles J. Rosenberg - Cupertino CA, US
Kunlong Gu - Belmont CA, US
Samy Bengio - Mountain View CA, US
Yun Zhou - San Jose CA, US

International Classification:

G06F 17/30

Abstract:

A method includes receiving a search query comprising one or more query terms, receiving image relevance models that each generate relevance measures of content feature values of images to a query, each image relevance model being a predictive model that has been trained based on content feature values of a set of training images, and each of the queries being a unique set of one or more query terms received by a search system as a query input, identifying an image relevance model for a different query that has been identified as similar to the received search query, and calculating a fractional adjustment multiplier for search results responsive to the received search query, the fractional adjustment multiplier being based on a relevance measure generated by the identified image relevance model for the different query and based on a degree of similarity between the different query and the received search query.

US Patent:

20150169999, Jun 18, 2015

Filed:

Feb 1, 2012

Appl. No.:

13/363979

Inventors:

Thomas J. Duerig - Sunnyvale CA, US
Jason E. Weston - New York NY, US
Charles J. Rosenberg - Cupertino CA, US
Kunlong Gu - Belmont CA, US
Samy Bengio - Mountain View CA, US

Assignee:

GOOGLE INC. - Mountain View CA

International Classification:

G06K 9/62
G06K 9/52
G06F 17/30

Abstract:

Methods, systems and apparatus for refining image relevance models. In general, one aspect includes receiving a trained image relevance model that generates relevance measures of content feature values of images to a query, identifying a first threshold number of common content feature values for the set of training images, the common content feature values being identified as a set of content feature values that are each shared by at least a portion of the training images, identifying a subset of the set of training images having a quantity of the common content feature values greater than a second threshold number of content features values, and generating a re-trained image relevance model based on content feature values of the set of training images, wherein content feature values of the subset of training images are weighted higher than content feature values of the training images not in the subset.