Researches Vision and Machine Learning (VML) Research Group

Vision and Machine Learning (VML) Research Group


The Vision and Machine Learning (VML) Group is dedicated to conducting highly impactful research on the cutting edge of computer vision, machine learning, their application areas as well as real-time software and hardware implementations. Both fundamental and applied research is carried out. The group consists of experts in object tracking, detection, recognition and segmentation, scene understanding (recognition, layout estimation), stereo and multi-view vision, structure from motion, shape from texture, unsupervised and supervised learning methods, and augmented reality.


Name Role Research Area Email
Dr Naeem Ul Islam Head Computer Vision, Signal Processing​ [email protected]
Dr. Ahmad Rauf Subhani Associate Head Computer Vision, Signal Processing​ [email protected]
Dr Usman Ali Member Embedded Systems, Adaptive filtering, Machine learning [email protected]
Dr Muwahida Liaquat Member Control Systems, Image Processing [email protected]
Ms. Sobia Hayee Member Control Systems, Image Processing [email protected]


  1. Farkhanda Aziz, Azhar Ul-Haq, Shahzor Ahmad, Yousef Mahmoud, Marium Jalal and Usman Ali, “A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays”. IEEE Access, 2020.
  2. Shahzor Ahmad and Loong-Fah Cheong: Robust Detection and Affine Rectification of Planar Homogeneous Texture for Scene Understanding. International Journal of Computer Vision, 2018.
  3. Shahzor Ahmad and Loong-Fah Cheong. Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding. In Proc. 14th European Conference on Computer Vision (ECCV), Amsterdam, the Netherlands, October 2016.
  4. Mohammad Bilal Malik and Usman Ali, “Adaptive Thresholding using Particle Filter for Tracking Small and Low Contrast Objects”, IEEE 10th International Conference on Information Sciences, Signal Processing and their Applications, 2010.
  5. Usman Ali, Mohammad Bilal Malik and Khalid Munawar, “FPGA/Soft-Processor Based Real-Time Object Tracking System”, IEEE 5th Southern Programmable Logic Conference, pp. 33-37, 2009.

MS Thesis Alumni

Name Role Thesis Title
Syed Sheharyar Anwar MS Student 3D Reconstruction from 2D DICOM Images with an Interactive GUI
Muhammad Yasir MS Student Vehicle Make and Model Classification for Overhead Traffic Analytics
Farkhanda Aziz MS Student A Novel Machine Learning (ML) Based Approach for Fault Classification in Photovoltaic (PV) Array

Funded Projects

Project Title Funded Agency & Grant Details
Automatic Number Plate Recognition HEC (SRGP-1611), 2017
Smart Traffic Profiling for Intelligent Road Transportation HEC (TDF03-219), 2018

Contact Person

Dr Naeem Ul Islam

Head VML Research Group

Email: [email protected]