Search Results for author: M. Omair Ahmad

Found 5 papers, 2 papers with code

DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR

1 code implementation12 Sep 2022 Nima Masoumi, Hassan Rivaz, M. Omair Ahmad, Yiming Xiao

Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN).

Image Registration

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

no code implementations1 Nov 2018 Mohammad Tariqul Islam, Dipayan Saha, S. M. Mahbubur Rahman, M. Omair Ahmad, M. N. S. Swamy

However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal.

Denoising

Online multi-object tracking via robust collaborative model and sample selection

1 code implementation Computer Vision and Image Understanding 2017 Mohamed A. Naiel, M. Omair Ahmad, M.N.S. Swamy, Jongwoo Lim, Ming-Hsuan Yang

For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker.

Multi-Object Tracking Object +3

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