N
Nader Karimi
Researcher at Isfahan University of Technology
Publications - 184
Citations - 2721
Nader Karimi is an academic researcher from Isfahan University of Technology. The author has contributed to research in topics: Image processing & Convolutional neural network. The author has an hindex of 22, co-authored 184 publications receiving 1813 citations. Previous affiliations of Nader Karimi include McMaster University.
Papers
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Proceedings ArticleDOI
Melanoma detection by analysis of clinical images using convolutional neural network
Ebrahim Nasr-Esfahani,Shadrokh Samavi,Nader Karimi,S.M.R. Soroushmehr,Mohammad H. Jafari,Kevin R. Ward,Kayvan Najarian +6 more
TL;DR: Experimental results show that the proposed method for detection of melanoma lesions is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.
Journal ArticleDOI
Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area
TL;DR: This paper proposes to use variations in silhouette area that are obtained from only one camera to find the silhouette, and shows that the proposed feature is view invariant.
Proceedings ArticleDOI
Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network
Mojtaba Akbari,Majid Mohrekesh,Ebrahim Nasr-Esfahani,S. M. Reza Soroushmehr,Nader Karimi,Shadrokh Samavi,Kayvan Najarian +6 more
TL;DR: Wang et al. as discussed by the authors proposed a polyp segmentation method based on the convolutional neural network, which performed a novel image patch selection method in the training phase of the network and performed effective post-processing on the probability map that is produced by the network.
Proceedings ArticleDOI
Skin lesion segmentation in clinical images using deep learning
Mohammad H. Jafari,Nader Karimi,Ebrahim Nasr-Esfahani,Shadrokh Samavi,S.M.R. Soroushmehr,Kevin R. Ward,Kayvan Najarian +6 more
TL;DR: The experimental results show that the proposed method for accurate extraction of lesion region can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.
Journal ArticleDOI
ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks
TL;DR: A deep end-to-end diffusion watermarking framework (ReDMark) which can learn a new watermarked algorithm in any desired transform space and highlight the superiority of the proposed framework in terms of imperceptibility, robustness and speed.