Shalini Gupta

US Patent:

8639050, Jan 28, 2014

Filed:

Oct 19, 2011

Appl. No.:

13/276896

Inventors:

Shalini Gupta - San Francisco CA, US
Rajesh Narasimha - Plano TX, US
Aziz Umit Batur - Dallas TX, US

Assignee:

Texas Instruments Incorporated - Dallas TX

International Classification:

G06K 9/40

US Classification:

382261, 348607

Abstract:

Dynamic adjustment of noise filter strengths for use with dynamic range enhancement of images is performed to produce better quality images by adapting dynamically to the image noise profile. Global and local brightness and contrast enhancement (GLBCE) is performed on a digital image to form an enhanced image. The GLBCE applies local gain values to the digital image based on local intensity values. A GLBCE gain versus intensity curve is determined for the enhanced image. A set of noise filter thresholds is adjusted in response to the GLBCE gain versus intensity curve to form a set of dynamically adjusted noise filter thresholds. The enhanced image is noise filtered using the set of dynamically adjusted noise filter thresholds to form a noise filtered enhanced image.

US Patent:

20060211022, Sep 21, 2006

Filed:

Feb 23, 2006

Appl. No.:

11/361415

Inventors:

Shuqian Jing - Palo Alto CA, US
Francesca Civoli - Newbury Park CA, US
Shalini Gupta - Newbury Park CA, US
Daniel Halperin - Calabasas CA, US
Jason Pennucci - Santa Monica CA, US
Steven Swanson - Moorpark CA, US
Yan Yu - Thousand Oaks CA, US

Assignee:

Amgen Inc. - Thousand Oaks CA

International Classification:

C12Q 1/68
G01N 33/53

US Classification:

435006000, 435007100

Abstract:

The present invention relates to methods of detecting compounds that affect the activity of a therapeutic substance or composition administered to a subject, and to reagents for use in such methods.

US Patent:

20180341333, Nov 29, 2018

Filed:

Jul 24, 2018

Appl. No.:

16/044239

Inventors:

Pavlo MOLCHANOV - Santa Clara CA, US
Shalini GUPTA - Santa Clara CA, US
Kihwan KIM - Sunnyvale CA, US
Kari PULLI - Palo Alto CA, US

International Classification:

G06F 3/01
G06N 3/08
G06N 3/04
G06K 9/78
G06K 9/62
B60R 11/04
G01S 13/58
G01S 13/42
G01S 7/41
G01S 7/35
G06K 9/00

Abstract:

An apparatus and method for gesture detection and recognition. The apparatus includes a processing element, a radar sensor, a depth sensor, and an optical sensor. The radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and recognition. The processing element is further configured to detect and recognize a hand gesture based on data acquired with the radar sensor, the depth sensor, and the optical sensor.

US Patent:

20180075611, Mar 15, 2018

Filed:

Nov 27, 2017

Appl. No.:

15/823370

Inventors:

- Santa CA, US
Shalini GUPTA - San Francisco CA, US
Iuri FROSIO - San Jose CA, US
Nagilla Dikpal REDDY - Palo Alto CA, US
Jan KAUTZ - Lexington MA, US

International Classification:

G06T 7/507
G06T 7/70

Abstract:

One embodiment of the present invention sets forth a technique for estimating a head pose of a user. The technique includes acquiring depth data associated with a head of the user and initializing each particle included in a set of particles with a different candidate head pose. The technique further includes performing one or more optimization passes that include performing at least one iterative closest point (ICP) iteration for each particle and performing at least one particle swarm optimization (PSO) iteration. Each ICP iteration includes rendering the three-dimensional reference model based on the candidate head pose associated with the particle and comparing the three-dimensional reference model to the depth data. Each PSO iteration comprises updating a global best head pose associated with the set of particles and modifying at least one candidate head pose. The technique further includes modifying a shape of the three-dimensional reference model based on depth data.

US Patent:

20170060254, Mar 2, 2017

Filed:

Mar 3, 2016

Appl. No.:

15/060525

Inventors:

- Santa Clara CA, US
Shalini GUPTA - San Francisco CA, US
Kihwan KIM - Sunnyvale CA, US
Kari PULLI - Palo Alto CA, US

International Classification:

G06F 3/01
G06K 9/62
B60R 11/04
G06N 3/04
G06N 3/08
G06K 9/00
G06K 9/78

Abstract:

An apparatus and method for gesture detection and recognition. The apparatus includes a processing element, a radar sensor, a depth sensor, and an optical sensor. The radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and recognition. The processing element is further configured to detect and recognize a hand gesture based on data acquired with the radar sensor, the depth sensor, and the optical sensor.

US Patent:

20170046827, Feb 16, 2017

Filed:

Aug 12, 2015

Appl. No.:

14/825129

Inventors:

- Santa Clara CA, US
Shalini GUPTA - San Francisco CA, US
Iuri FROSIO - San Jose CA, US
Nagilla Dikpal REDDY - Palo Alto CA, US
Jan KAUTZ - Lexington MA, US

International Classification:

G06T 7/00

Abstract:

One embodiment of the present invention sets forth a technique for estimating a head pose of a user. The technique includes acquiring depth data associated with a head of the user and initializing each particle included in a set of particles with a different candidate head pose. The technique further includes performing one or more optimization passes that include performing at least one iterative closest point (ICP) iteration for each particle and performing at least one particle swarm optimization (PSO) iteration. Each ICP iteration includes rendering the three-dimensional reference model based on the candidate head pose associated with the particle and comparing the three-dimensional reference model to the depth data. Each PSO iteration comprises updating a global best head pose associated with the set of particles and modifying at least one candidate head pose. The technique further includes modifying a shape of the three-dimensional reference model based on depth data.

US Patent:

20160259037, Sep 8, 2016

Filed:

Mar 3, 2016

Appl. No.:

15/060545

Inventors:

- Santa Clara CA, US
Shalini GUPTA - San Francisco CA, US
Kihwan KIM - Sunnyvale CA, US
Kari PULLI - Palo Alto CA, US

International Classification:

G01S 7/41
G01S 13/56
G01S 7/35
G01S 13/42

Abstract:

An apparatus and method for radar based gesture detection. The apparatus includes a processing element and a transmitter configured to transmit radar signals. The transmitter is coupled to the processing element. The apparatus further includes a plurality of receivers configured to receive radar signal reflections, where the plurality of receivers is coupled to the processing element. The transmitter and plurality of receivers are configured for short range radar and the processing element is configured to detect a hand gesture based on the radar signal reflections received by the plurality of receivers.

US Patent:

20140270364, Sep 18, 2014

Filed:

Sep 17, 2013

Appl. No.:

14/029633

Inventors:

- Santa Clara CA, US
Shalini GUPTA - San Francisco CA, US
Elif ALBUZ - Sunnyvale CA, US

Assignee:

NVIDIA CORPORATION - Santa Clara CA

International Classification:

G06K 9/00
G06K 9/62

US Classification:

382103

Abstract:

In one embodiment of the present invention, a graphics processing unit (GPU) is configured to detect an object in an image using a random forest classifier that includes multiple, identically structured decision trees. Notably, the application of each of the decision trees is independent of the application of the other decision trees. In operation, the GPU partitions the image into subsets of pixels, and associates an execution thread with each of the pixels in the subset of pixels. The GPU then causes each of the execution threads to apply the random forest classifier to the associated pixel, thereby determining a likelihood that the pixel corresponds to the object. Advantageously, such a distributed approach to object detection more fully leverages the parallel architecture of the PPU than conventional approaches. In particular, the PPU performs object detection more efficiently using the random forest classifier than using a cascaded classifier.