H
Hossein Rahmani
Researcher at Iran University of Science and Technology
Publications - 106
Citations - 2719
Hossein Rahmani is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 18, co-authored 82 publications receiving 1970 citations. Previous affiliations of Hossein Rahmani include Lancaster University & Maastricht University.
Papers
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Book
A Guide to Convolutional Neural Networks for Computer Vision
TL;DR: This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision, providing a comprehensive introduction to CNNs.
Journal ArticleDOI
Learning a Deep Model for Human Action Recognition from Novel Viewpoints
TL;DR: A Robust Non-Linear Knowledge Transfer Model (R-NKTM) is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views.
Proceedings ArticleDOI
3D Action Recognition from Novel Viewpoints
Hossein Rahmani,Ajmal Mian +1 more
TL;DR: The proposed human pose representation model is able to generalize to real depth images of unseen poses without the need for re-training or fine-tuning and dramatically outperforms existing state-of-the-art in action recognition.
Proceedings ArticleDOI
Learning a non-linear knowledge transfer model for cross-view action recognition
Hossein Rahmani,Ajmal Mian +1 more
TL;DR: The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view.
Book ChapterDOI
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
TL;DR: In this article, the Histogram of Oriented Principal Components (HOPC) is used to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences.