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Mahmoud Afifi
Researcher at York University
Publications - Â 58
Citations - Â 1122
Mahmoud Afifi is an academic researcher from York University. The author has contributed to research in topics: Color balance & Color constancy. The author has an hindex of 13, co-authored 58 publications receiving 596 citations. Previous affiliations of Mahmoud Afifi include Adobe Systems & Samsung.
Papers
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Journal ArticleDOI
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
TL;DR: In this paper, the combination of isolated facial components and a contextual feature called foggy face is used to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class.
Proceedings ArticleDOI
When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images
TL;DR: This paper introduces a k-nearest neighbor strategy that is able to compute a nonlinear color mapping function to correct the image's colors and shows the method is highly effective and generalizes well to camera models not in the training set.
Journal ArticleDOI
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
Mahmoud Afifi,Mahmoud Afifi +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images, which is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification.
Proceedings ArticleDOI
Deep White-Balance Editing
Mahmoud Afifi,Michael S. Brown +1 more
TL;DR: A deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance for sRGB images that are rendered with the incorrect white balance is introduced.
Proceedings ArticleDOI
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed,Mahmoud Afifi,Radu Timofte,Michael S. Brown,Yue Cao,Zhilu Zhang,Wangmeng Zuo,Xiaoling Zhang,Jiye Liu,Wendong Chen,Changyuan Wen,Meng Liu,Shuailin Lv,Yunchao Zhang,Zhihong Pan,Baopu Li,Teng Xi,Yanwen Fan,Xiyu Yu,Gang Zhang,Jingtuo Liu,Han Junyu,Errui Ding,Songhyun Yu,Bumjun Park,Jechang Jeong,Shuai Liu,Ziyao Zong,Nan Nan,Chenghua Li,Zengli Yang,Long Bao,Shuangquan Wang,Dongwoon Bai,Jungwon Lee,Youngjung Kim,Kyeongha Rho,Changyeop Shin,Sungho Kim,Pengliang Tang,Yiyun Zhao,Yuqian Zhou,Yuchen Fan,Thomas S. Huang,Zhihao Li,Nisarg A. Shah,Wei Liu,Qiong Yan,Yuzhi Zhao,Marcin Mozejko,Tomasz Latkowski,Lukasz Treszczotko,Michal Szafraniuk,Krzysztof Trojanowski,Yanhong Wu,Pablo Navarrete Michelini,Fengshuo Hu,Yunhua Lu,Sujin Kim,Wonjin Kim,Jaayeon Lee,Jang-Hwan Choi,Magauiya Zhussip,Azamat Khassenov,Jong Hyun Kim,Hwechul Cho,Priya Kansal,Sabari Nathan,Zhangyu Ye,Xiwen Lu,Yaqi Wu,Jiangxin Yang,Yanlong Cao,Siliang Tang,Yanpeng Cao,Matteo Maggioni,Ioannis Marras,Thomas Tanay,Gregory G. Slabaugh,Youliang Yan,Myungjoo Kang,Han-Soo Choi,Kyungmin Song,Shusong Xu,Xiaomu Lu,Tingniao Wang,Chunxia Lei,Bin Liu,Rajat Gupta,Vineet Kumar +89 more
TL;DR: This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.