Ying H Li

US Patent:

7035435, Apr 25, 2006

Filed:

May 7, 2002

Appl. No.:

10/140511

Inventors:

Ying Li - Los Angeles CA, US
Tong Zhang - Mountain View CA, US
Daniel R. Tretter - San Jose CA, US

Assignee:

Hewlett-Packard Development Company, L.P. - Houston TX

International Classification:

G06K 1/00

US Classification:

382107, 382173, 348700, 7071041

Abstract:

A method and system for automatically summarizing a video document. The video document is decomposed into scenes, shots and frames, and an importance value is assigned to each scene, shot and frame. Keyframes are allocated among the shots based on the importance value of each shot. The allocated number of keyframes are then selected from each shot. The number of keyframes may be altered for greater or lesser detail in response to user input.

US Patent:

20180276822, Sep 27, 2018

Filed:

Mar 5, 2018

Appl. No.:

15/912262

Inventors:

- Oakland CA, US
Ying Li - Los Angeles CA, US
Jing Qin - Bozeman MT, US
Chi-Wei Chang - Los Angeles CA, US
Yi-Kai Lo - Los Angeles CA, US

Assignee:

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA

International Classification:

G06T 7/00
A61B 5/04
A61B 5/0478
A61B 5/00
A61B 5/0484
G06T 7/246
G06T 11/00
G06T 15/08

Abstract:

An ultra-dense electrode-based brain imaging system with high spatial and temporal resolution. A Sparsity and Smoothness enhanced Method Of Optimized electrical TomograpHy (s-SMOOTH) based reconstruction technique to improve the spatial resolution and localization accuracy of reconstructed brain images is described. Also described is a graph Fractional-Order Total Variation (gFOTV) based reconstruction technique to improve the spatial resolution and localization accuracy of reconstructed brain images.

US Patent:

20160249098, Aug 25, 2016

Filed:

Feb 20, 2015

Appl. No.:

14/628106

Inventors:

- Reston VA, US
Ramzi Joseph Nasr - Falls Church VA, US
Magid M. Abraham - Great Falls VA, US
Ying Li - Reston VA, US

International Classification:

H04N 21/442
H04N 21/24
H04N 21/25
H04N 21/258

Abstract:

The present disclosure describes techniques for projecting person-level viewership from household-level tuning events. One example method includes accessing tuning data representing television tuning events associated with particular households; accessing panelist viewing data representing television viewing events associated with panelists; and for at least one tuning event represented by the tuning data: accessing household member data associated with the tuning event; determining a member minutes value associated with each individual member of the particular household for the tuning event; and determining a fractional viewership value for each individual member of the particular household associated with the tuning event.