Search

Igor A Tryndin

from Fremont, CA
Age ~50
Get Report
Also known as:
Igor T Tryndin, Igor Trydin, Igor N
Phone and address:
638 Boar Cir, Fremont, CA 94539
(510) 304-1123
Connected to:
Andrew K Tryner, 39Karolina Tryniszewski, 52Keith R Trynor, 44A Tryon, 33Alicha C Tryon, 34Barron Tryon, 47Casey L Tryon, 38Charles Tryon, 72Danielle R Tryon, 43Jacob G Tryon, 63

Igor Tryndin Phones & Addresses

  • 638 Boar Cir, Fremont, CA 94539 (510) 304-1123
  • Sunnyvale, CA
  • Mountain View, CA
  • Belmont, CA
  • South San Francisco, CA
  • Alameda, CA
  • 538 Isla Vista Ter, Sunnyvale, CA 94085

Work

Company: Nvidia Nov 2017 Position: Visual perception algorithms, principal engineer

Education

School / High School: Bauman Moscow State Technical University 1995 to 1997

Skills

Device Drivers • Debugging • Embedded Systems • C++ • Algorithms • C • Linux • Multithreading • Perl • Mobile Devices • Programming • Image Processing • Opengl • Arm • Embedded Software • Python • Soc • Firmware • Distributed Systems • High Performance Computing • Qt • Embedded Linux • Shell Scripting • Program Management • Digital Photography • Stl • Gpu • Cross Functional Team Leadership • Cross Platform Development • Digital Imaging • C/C++ Stl • Display Technology • Subversion • Linux Kernel • Strategic Planning • Performance Tuning • Hdr • Kernel Programming • Digital Cameras • Windbg • Mysql • Drupal • Git • Color Management • Gdb • Perforce • Cmake • Bitbake

Languages

English • Russian • Italian

Industries

Automotive

Resumes

Resumes

Igor Tryndin Photo 1

Visual Perception Algorithms, Principal Engineer

View page
Location:
638 Boar Cir, Fremont, CA 94539
Industry:
Automotive
Work:
Nvidia
Visual Perception Algorithms, Principal Engineer
Nvidia Aug 2014 - Nov 2017
Senior Camera Architect
Intel Corporation Jan 2012 - May 2014
Camera Engineer
Opelin Feb 2011 - Dec 2011
Engineer and Architect
Unified Color Technologies Jul 2007 - Feb 2011
Chief Technology Officer
Education:
Bauman Moscow State Technical University 1995 - 1997
Bauman Moscow State Technical University 1991 - 1995
Skills:
Device Drivers
Debugging
Embedded Systems
C++
Algorithms
C
Linux
Multithreading
Perl
Mobile Devices
Programming
Image Processing
Opengl
Arm
Embedded Software
Python
Soc
Firmware
Distributed Systems
High Performance Computing
Qt
Embedded Linux
Shell Scripting
Program Management
Digital Photography
Stl
Gpu
Cross Functional Team Leadership
Cross Platform Development
Digital Imaging
C/C++ Stl
Display Technology
Subversion
Linux Kernel
Strategic Planning
Performance Tuning
Hdr
Kernel Programming
Digital Cameras
Windbg
Mysql
Drupal
Git
Color Management
Gdb
Perforce
Cmake
Bitbake
Languages:
English
Russian
Italian

Publications

Us Patents

Image Reproduction Device Calibration

View page
US Patent:
20130027418, Jan 31, 2013
Filed:
Jul 27, 2011
Appl. No.:
13/191580
Inventors:
Igor Tryndin - Mountain View CA, US
Sergey Bezryadin - San Bruno CA, US
International Classification:
G09G 5/02
G09G 5/00
US Classification:
345589, 345581
Abstract:
Various examples within the present disclosure provide methods, apparatuses, and systems related to the calibration of image reproduction devices. Various image reproduction devices may communicate, determine calibration capabilities, and generate color profiles.

Intersection Detection And Classification In Autonomous Machine Applications

View page
US Patent:
20200293796, Sep 17, 2020
Filed:
Mar 10, 2020
Appl. No.:
16/814351
Inventors:
- Santa Clara CA, US
Berta Rodriguez Hervas - San Francisco CA, US
Hang Dou - Fremont CA, US
Igor Tryndin - Fremont CA, US
David Nister - Bellevue WA, US
Minwoo Park - Saratoga CA, US
Neda Cvijetic - East Palo Alto CA, US
Junghyun Kwon - San Jose CA, US
Trung Pham - Santa Clara CA, US
International Classification:
G06K 9/00
G06K 9/62
G06N 3/08
B60W 30/09
B60W 30/095
B60W 60/00
G08G 1/01
Abstract:
In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.
Control profile
Igor A Tryndin from Fremont, CA, age ~50 Get Report