Publications
Kiln-Net
Usman Nazir, Usman Khalid Mian, Muhammad Usman Sohail, Murtaza Taj and Momin Uppal Abstract: The availability of high-resolution satellite imagery has enabled several new applications. One such application is identification of brick kilns for the elimination of modern day slavery which is also one of UN’s Sustainable Development Goals (SDG). This requires automated analysis of […]
IEEE TGRS – A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching
Numan Khurshid, Mohbat Tharani, Murtaza Taj, and Faisal Qureshi Abstract: We propose a new method for remote sensing image matching. The proposed method uses encoder subnetwork of an autoencoder pre-trained on GTCrossView data to construct image features. A discriminator network trained on University of California Merced Land Use/Land Cover dataset (LandUse) and High-resolution Satellite Scene […]
ICIAP 2019 – Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval
Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019 Abstract Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory […]
Statistically Correlated Multi-task Learning for Autonomous Driving
Waseem Abbas, M. Fakhir Khan, Murtaza Taj, and Arif Mahmood Abstract Autonomous driving research is an emerging domain in computer vision and machine learning areas. Most existing methods perform Single Task Learning (STL) from one or more images while Multi-Task Learning (MTL) is more efficient due to the leverage of shared information between different tasks. […]
Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, Murtaza Taj International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16-21, 2019 Abstract This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within “Brick-Kiln-Belt” of South Asia. The framework is developed […]
ICASSP 2019 – Using 3D Residual Network For Spatio-Temporal Analysis Of Remote Sensing Data
Muhammad Ahmed Bhimra, Usman Nazir, Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network […]
ICASSP 2019 – Adaptively Weighted Multi-Task Learning Using Inverse Validation Loss
Waseem Abbas and Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract Multi-task learning aims to enhance the performance of a model by inductive transfer of information among tasks. However, joint optimization of multiple tasks is challenging due to unbalanced data ranges and variations in task difficulties […]
ICASSP 2019- Point Cloud Segmentation Using Hierarchical Tree for Architectural Models
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract Over the past few years, gathering massive volume of 3D data has become straightforward due to the proliferation of laser scanners and acquisition devices. Segmentation of such large data into meaningful segments, […]
PacificGraphics 2017 – Outdoor scene segmentation and reconstruction using LiDAR data
Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj The 25th Pacific Conference on Computer Graphics and Applications (Pacific Graphics 2017), Taipei, Taiwan, Oct 16 – 19, 2017 Abstract Recent advancements in 3D scanning technologies have paved way for generation of highly accurate 3D scenes in the form of point cloud data. For the segmentation and […]
CGI 2016 – Coarse-to-fine model fitting on point cloud
Reema Bajwa, Syed Rizwan Gilani, Murtaza Taj Short Paper Proceedings of the 33rd Computer Graphics International, Heraklion, Greece, June 28 – July 1, 2016 Abstract We present a coarse-to-fine model fitting approach that automatically generates a detailed CAD like model from a point cloud. We first developed a library of detailed parametric models for each […]
