Rameswar Panda from Medford, MA

Rameswar Panda Phones & Addresses

Medford, MA
Cambridge, MA
Riverside, CA

Work

Company:

Ibm
Dec 17, 2018
Position:

Research scientist

Education

Degree:

Doctorates, Doctor of Philosophy
School / High School:

University of California, Riverside
1980 to 2019
Specialities:

Philosophy

Skills

Matlab • C • Computer Vision • Image Processing • C++ • Machine Learning • Artificial Neural Networks • Optimization • Multimedia • Video Analytics • Human Computer Interaction • Crowdsourcing • Graph Theory • Pattern Recognition • Artificial Intelligence • Algorithms • Latex • Linux • Programming • Python • Microsoft Office • Data Analysis • Signal Processing

Languages

Oriya • Hindi • English

Industries

Research

Resumes

Research Scientist

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Location:

22 Water St, Cambridge, MA 02141

Industry:

Research

Work:

Ibm
Research Scientist
Nec Laboratories America, Inc. Jun 2018 - Sep 2018
Research Intern
Adobe Jun 2017 - Nov 2017
Research Intern
Siemens Jun 2016 - Sep 2016
Research Intern
University of California, Riverside Jun 2016 - Sep 2016
Graduate Student Researcher

Education:

University of California, Riverside 1980 - 2019
Doctorates, Doctor of Philosophy, Philosophy Jadavpur University 2011 - 2013
Masters, Computer Engineering, Computer Science Silicon Institute of Technology
Bachelors, Bachelor of Technology University of California

Skills:

Matlab
C
Computer Vision
Image Processing
C++
Machine Learning
Artificial Neural Networks
Optimization
Multimedia
Video Analytics
Human Computer Interaction
Crowdsourcing
Graph Theory
Pattern Recognition
Artificial Intelligence
Algorithms
Latex
Linux
Programming
Python
Microsoft Office
Data Analysis
Signal Processing

Languages:

Oriya
Hindi
English

Us Patents

Method And System For Detecting Defects On Surface Of Object
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US Patent:

20190056333, Feb 21, 2019
Filed:

Aug 16, 2017
Appl. No.:

15/678335
Inventors:

- Orlando FL, US
Rameswar Panda - Riverside CA, US
Jan Ernst - Plainsboro NJ, US
Kevin P. Bailey - Chuluota FL, US
International Classification:

G01N 21/956
G06T 7/00
G06K 9/46
G06T 7/33
Abstract:

Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.