no code implementations • 31 Aug 2020 • Muhammad Usman Ghani, W. Clem Karl
In this work, a principled approach is presented allowing the unified integration of both data and image domain priors for improved image reconstruction.
no code implementations • 11 Mar 2020 • Faiza Memood, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Rehab Shehzadi, Muhammad Nabeel Asim
In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this paper for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove.
no code implementations • 3 Mar 2020 • Muhammad Nabeel Asim, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Sheraz Ahmad, Waqar Mahmood, Andreas Dengel
Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages.
no code implementations • 31 Aug 2019 • Muhammad Usman Ghani, W. Clem Karl
In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework.
1 code implementation • 9 Apr 2019 • Muhammad Usman Ghani, W. Clem Karl
The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts.
no code implementations • 19 Jul 2016 • Muhammad Usman Ghani, Ertunc Erdil, Sumeyra Demir Kanik, Ali Ozgur Argunsah, Anna Felicity Hobbiss, Inbal Israely, Devrim Unay, Tolga Tasdizen, Mujdat Cetin
We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem.