M
Muhammad Imran Malik
Researcher at University of the Sciences
Publications - 93
Citations - 1625
Muhammad Imran Malik is an academic researcher from University of the Sciences. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 15, co-authored 66 publications receiving 1009 citations. Previous affiliations of Muhammad Imran Malik include National University of Sciences and Technology & National University of Science and Technology.
Papers
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Journal ArticleDOI
A Perspective Analysis of Handwritten Signature Technology
Moises Diaz,Miguel Ferrer,Donato Impedovo,Muhammad Imran Malik,Giuseppe Pirlo,Réjean Plamondon +5 more
TL;DR: A systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario is reported, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
Journal ArticleDOI
Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data
Shoaib Ahmed Siddiqui,Ahmad Salman,Muhammad Imran Malik,Faisal Shafait,Ajmal Mian,Mark R. Shortis,Euan S. Harvey +6 more
TL;DR: This research advocates that the development of automated classification systems which can identify fish from underwater video imagery is feasible and a cost-effective alternative to manual identification by humans.
Proceedings ArticleDOI
Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
Marcus Liwicki,Muhammad Imran Malik,C. Elisa van den Heuvel,Xiaohong Chen,Charles E.H. Berger,Reinoud D. Stoel,Michael Blumenstein,Bryan Found +7 more
TL;DR: A signature verification competition on datasets with two scripts (Dutch and Chinese) in which questions were asked to compare questioned signatures against a set of reference signatures and methods used by Forensic Handwriting Examiners (FHEs) were applied.
Journal ArticleDOI
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Muhammad Naseer Bajwa,Muhammad Naseer Bajwa,Muhammad Imran Malik,Shoaib Ahmed Siddiqui,Shoaib Ahmed Siddiqui,Andreas Dengel,Andreas Dengel,Faisal Shafait,Wolfgang Neumeier,Sheraz Ahmed +9 more
TL;DR: A two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous and a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization is developed.
Journal ArticleDOI
DeCNT: Deep Deformable CNN for Table Detection
TL;DR: The presented approach was able to surpass the state-of-the-art performance on both ICDAR-2013 and ICDar-2017 POD datasets with a F-measure of 0.994 and 0.968, respectively, indicating its effectiveness and superiority for the task of table detection.