Paper accepted at ICONIP 2023
Our paper titled “Stereoential Net: Deep Network for Learning Building Height Using Stereo Imagery” accepted at ICONIP 2023.
This work was an outcome of PhD Thesis by Sana Jabbar
More info: Click here
Our paper titled “Stereoential Net: Deep Network for Learning Building Height Using Stereo Imagery” accepted at ICONIP 2023.
This work was an outcome of PhD Thesis by Sana Jabbar
More info: Click here
Our paper titled “Neural Network Pruning Through Constrained Reinforcement Learning” accepted at ICPR 2022. This work was an outcome of MS Thesis by Shehryar Malik More info: Click here
Our NRPU proposal on “Analyzing and Forecasting Socio-economic development using Satellite Imagery of Districts of Punjab” has been approved for funding by HEC. Acceptance rate among CS & IT related proposals in NRPU call for 2017-18 was only 3% and the overall acceptance rate was only 28%. Our is amongĀ 1104 accepted proposals out of total…
Learning Socio-economic Indicators from Remote Sensing Data Thursday 12 Sep, 2019 at 03:30 am in CS Smart Room 9-105 SBASSE. Abstract Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty and slavery,…
Our paper titled “Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation” accepted at IEEE Transactions on Geoscience and Remote Sensing. This work was an outcome of PhD Thesis work by Sana Jabbar More info: Click here
Maheen Rashid, a CV Lab research assistant since 2011, who earlier also did her senior project in our lab, has received a Fulbright scholarship and has joined Robotics Institute at Carnegie Mellon University for her Ph.D. studies. Maheen worked on Single View Reconstruction during her research assistantship here, and has coauthored our ECCV 2012 paper…
Arif Mahmood successfully defended his Ph.D. thesis on May 27, 2011 at LUMS, Lahore. Thesis Abstract: Template matching is frequently used in Digital Image Processing, Machine Vision, Remote Sensing and Pattern Recognition, and a large number of template matching algorithms have been proposed in literature. The performance of these algorithms may be evaluated from the…