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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: Muhammad Ahmed Bhimra
Spatio-Temporal Analysis of Landuse-Landcover Change Using Satellite Imagery Thursday 28 Feb, 2019 at 10:00 am in Smart Room 9-105 SBASSE. Abstract We propose an approach to recognize large scale, rapid spatio-temporal analysis of satellite remote sensing data. This technique can be used to measure longitudinal changes and yearly changes. Most of the existing methods either…
AAAI2023 – Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection
Usman Nazir, Wadood Islam, Sara Khalid, Murtaza Taj Abstract: Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean…
Ijaz Akhter
Ijaz Akhter is a Ph.D. candidate in the Department of Computer Science, Lahore University of Management Sciences. Research Interests My research interests span the broad areas of Computer Vision and Graphics. More specifically I am interested in exploring the geometric properties of nonrigid object. The geometric relations between a camera and rigid world are well-known…
Summer Internships 2019
Content Based Image Retrieval Using Hand Crafted Features Asim Waheed, Khawaja Umair Ul Hassan The project involved solving the Cross-View image matching problem between Satellite view images and Street view images. Many hand-crafted features were calculated, such as Histogram, HOG, Bag of Visual Words and VLAD using SIFT and SURF descriptors. The compiled features would…
BMVC2021 – Teacher-Class Network: A Neural Network Compression Mechanism
Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj Abstract: To reduce the overwhelming size of Deep Neural Networks, teacher-student techniques aim to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called the teacher-class network consisting of a single teacher…