M. Ahmed Bhimra
M. Ahmed Bhimra is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
M. Ahmed Bhimra is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
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….
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…
Sohaib Khan is Associate Professor and Department Chair of Computer Science at LUMS School of Science and Engineering, Lahore, Pakistan. His research interests broadly span the areas of image and video analysis, including estimating 3D from images, motion capture and multiple camera surveillance systems. Dr Khan earned his PhD degree in Computer Science in 2002…
Fezan Rasool is a PhD Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
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…
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…