Search Results for author: Muhammad Usman Ghani

Found 6 papers, 1 papers with code

Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium

no code implementations31 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.

Image Reconstruction

A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis

no code implementations11 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.

Sentiment Analysis Word Embeddings

Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification

no code implementations3 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.

Automated Feature Engineering Classification +5

Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium

no code implementations31 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.

Computed Tomography (CT) Electron Microscopy

Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning

1 code implementation9 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.

Computed Tomography (CT) Image Reconstruction +2

Dendritic Spine Shape Analysis: A Clustering Perspective

no code implementations19 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.

General Classification

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