Saimunur Rahman

Research & Development Engineer
Advanced Robotic Vision (ARV)
Automated Board Inspection (ABI)
Vitrox Innovation Centre and Headquarter
No. 85-A, Lintang Bayan Lepas 11
Bayan Lepas Industrial Park, Phase 4
11900 Bayan Lepas, Penang, Malaysia

saimun1992 [AT] gmail [DOT] com
(+60)1125427344, (+880)1710277388
saimunurrahman saimunur.rahman
Saimunur_Rahman SaimunurRahman

Hi! I am with the Advanced Robotic Vision (ARV) team of Vitrox Corporation. I lead the machine learning (ML) sub-group in the ARV, Automated Board Inspection (ABI). I have completed my master's (by Research) in Computer Vision and Machine Learning from Visual Processing (ViPr) Laboratory, Faculty of Computing and Informatics, Multimedia University, Cyberjaya Campus under supervision of Dr. John See Su Yang and Dr. Chiung Ching (Peter) Ho. My master degree work was on sptio-temporal feature learning based activity recognition in low quality videos. I was a Graduate Research Assistant and Researcher at ViPr Laboratory, Multimedia University and a Lecturer in Information Technology at International Islamic University Chittagong.

My primary research interests are in designing effective and efficient algorithms for visual recognition tasks in the field of Computer Vision, Pattern Recognition and Machine Learning (both traditional feature learning and deep learning).

Human Activity Recognition in Low Quality Videos Using Spatio-Temporal Features

Saimunur Rahman

Master's (by Research) thesis, Multimedia University
June 2016

A dissertation on a generic pipeline and spatio-temporal feature representations for human activity recognition in low quality videos. State-of-the-art results were achieved on nine low quality versions/subsets of publicly available datasets.

Deep CNN Object Features for Improved Action Recognition in Low Quality Videos

Saimunur Rahman, John See and Chiung Ching Ho

International Conference on Computational Science and Engineering (ICCSE 2016)
November 28-30, Kota Kinabalu, Malaysia, 2016

An image-trained CNN feature utilization method for improving the performance of STIP based traditional HOG and HOF features for action recognition in low quality videos.

Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video

Saimunur Rahman and John See

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016)
March 20-25, Shanghai, China, 2016

A mid-level feature bank comprises of shape, motion and discriminative textural features for action recognition in low quality videos.

Leveraging Textural Features for Recognizing Actions in Low Quality Videos

Saimunur Rahman, John See and Chiung Ching Ho

International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP)
February 2-3, Penang, Malaysia, 2016

Proposed BSIF-TOP descriptor for improving shape and motion features for action recognition in low quality videos.

On the effects of Low Quality Videos in Human Action Recognition

John See and Saimunur Rahman

International Conference on Digital Image Computing: Techniques and Applications (DICTA)
October 23-25, Adelaide, Australia, 2015

Introduced a LBP utilization method for action recogntion in low quality videos

Action Recognition by Jointly using Shape, Motion and Texture Features in Low Quality Videos

Saimunur Rahman, John See and Chiung Ching Ho

IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
October 19-21, Kuala Lampur, Malaysia, 2015

Introduced a nobel joint feature based method for action recogntion in low quality videos

An Web-Based Automated Conference Management System

Saimunur Rahman

Bachelor (Engineering) Final Year Project Report, International Islamic University Chittagong
January 2014

A final year project on Web-Based Automated Conference Management System. This project later used for organizing one national-level and one international-level conference.