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Zhong Qin Zheng, 673113 Paulskirk Dr, Ellicott City, MD 21042

Zhong Zheng Phones & Addresses

Ellicott City, MD   

224 Broadway, Baltimore, MD 21231    410-3276639    410-5639661    410-8507847   

226 Broadway, Baltimore, MD 21231    410-5225521    410-5639661   

Brooklyn, NY   

New York, NY   

Work

Company: Asian gourmet Sep 2008 Position: Manager

Education

School / High School: Rutgers University, Rutgers business School New Brunswick- New Brunswick, NJ May 2013 Specialities: Bachelor of Science in Finance

Mentions for Zhong Qin Zheng

Career records & work history

License Records

Zhong Zheng

Licenses:
License #: 68329 - Expired
Category: Engineers
Issued Date: Jul 25, 2008
Effective Date: Mar 14, 2017
Expiration Date: Feb 28, 2017
Type: Professional Engineer

Zhong Zheng resumes & CV records

Resumes

Zhong Zheng Photo 37

Zhong Zheng

Zhong Zheng Photo 38

Zhong Zheng

Zhong Zheng Photo 39

Zhong Wei Zheng

Zhong Zheng Photo 40

General Manager

Work:

General Manager
Zhong Zheng Photo 41

Zhong Zheng

Location:
United States
Zhong Zheng Photo 42

Zhong Zheng - Long Valley, NJ

Work:
Asian Gourmet Sep 2008 to 2000
Manager
Metro Grill - Flanders, NJ Sep 2008 to 2011
Assistant Sushi Chef
Education:
Rutgers University, Rutgers business School New Brunswick - New Brunswick, NJ May 2013
Bachelor of Science in Finance

Publications & IP owners

Us Patents

Computer Vision Systems And Methods For Blind Localization Of Image Forgery

US Patent:
2021000, Jan 7, 2021
Filed:
Jul 2, 2020
Appl. No.:
16/919840
Inventors:
- Jersey City NJ, US
Zhong Zheng - Jersey City NJ, US
Terrance E. Boult - Colorado Springs CO, US
Maneesh Kumar Singh - Lawrenceville NJ, US
Assignee:
Insurance Services Office, Inc. - Jersey City NJ
International Classification:
G06K 9/62
G06K 9/00
G06K 9/40
G06K 9/46
G06N 3/08
G06N 3/04
Abstract:
Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.

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