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Aun Irtaza
Researcher at University of Engineering and Technology
Publications - 93
Citations - 1667
Aun Irtaza is an academic researcher from University of Engineering and Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 17, co-authored 74 publications receiving 784 citations. Previous affiliations of Aun Irtaza include University of Michigan & University of Engineering and Technology, Lahore.
Papers
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Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.
TL;DR: In contrast with state of the art systems, the RCNN is capable to compute deep features with amen representation of Melanoma, and hence improves the segmentation performance.
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Robust Human Activity Recognition Using Multimodal Feature-Level Fusion
Muhammad Ehatisham-ul-Haq,Ali Javed,Muhammad Awais Azam,Hafiz Malik,Aun Irtaza,Ik Hyun Lee,Muhammad Tariq Mahmood +6 more
TL;DR: The experimental results indicate that the proposed scheme achieves better recognition results as compared to the state of the art, and the feature-level fusion of RGB and inertial sensors provides the overall best performance for the proposed system.
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Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
TL;DR: This research work proposes an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos that achieves the maximum accuracy of 94.07%.
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Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images
TL;DR: The proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.
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Embedding neural networks for semantic association in content based image retrieval
TL;DR: To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis, and it is proved that the proposed method has performed better then all of the comparative systems.