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Usman Nazir
Usman Nazir is a PhD Student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
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MS Thesis Defense: Mohbat Tharani
Challenging the dogma of Relevance Feedback in Content based Image Retrieval Systems with Deep Learning May, 2018 at 3:00 pm in Smart Room 9-105 SBASSE. Abstract Association of images to their content based similar images in a database, is quite a fascinating challenge specially on social media platform where billions of tagged and untagged images…
Water Quality Assessment Using Visual Sensors
Mohbat Tharani, Abdul Wahab Amin, Fezan Rasool, Muhammad Maaz, Murtaza Taj, Abubakr Muhammad Summary In the absence of an effective trash management framework, trash is often illegally dumped in rivers and canals running through urban areas, contaminating freshwater and blocking drainage lines which result in urban floods. When this contaminated water reaches agricultural fields, it…
ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning
Shehryar Malik, Muhammad Umair Haider*, Omer Iqbal, Murtaza Taj Abstract: Network pruning reduces the size of neural networks by removing (pruning) neurons such that the performance drop is minimal. Traditional pruning approaches focus on designing metrics to quantify the usefulness of a neuron which is often quite tedious and sub-optimal. More recent approaches have instead…
Ali Hassan
Ali Hassan is a Research Assistant in Computer Vision Lab (cvlab ) at LUMS Syed Babar Ali School of Science and Engineering. Research Interest My main areas of interest are Computer Vision, Machine Learning and Robotics. I am primarily interested in Human Motion Analysis. I am currently working on “Human Body Pose Estimation from Monocular…
IEEETGRS2024 – Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation
Sana Jabbar and Murtaza Taj Abstract: Accurate estimation of building heights is crucial for effective urban planning and resource management as it provides essential geometric information about the urban landscape. Many end-to-end deep learning-based networks have been proposed for image-to-height mapping using high-resolution nonoptical and optical remote sensing imagery. In this study, we develop a…