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Saif Iftekar Sayed
Saif Iftekar Sayed
Johnson & Johnson
Verified email at mavs.uta.edu
Title
Cited by
Cited by
Year
Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition
M Parchami, S Bashbaghi, E Granger, S Sayed
2017 14th IEEE International Conference on Advanced Video and Signal Based …, 2017
352017
Varm: Using virtual reality to program robotic manipulators
M Theofanidis, SI Sayed, A Lioulemes, F Makedon
Proceedings of the 10th International Conference on PErvasive Technologies …, 2017
272017
Hierarchical modeling for task recognition and action segmentation in weakly-labeled instructional videos
R Ghoddoosian, S Sayed, V Athitsos
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
192022
Kinematic estimation with neural networks for robotic manipulators
M Theofanidis, SI Sayed, J Cloud, J Brady, F Makedon
Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018
162018
Action duration prediction for segment-level alignment of weakly-labeled videos
R Ghoddoosian, S Sayed, V Athitsos
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
102021
A new dataset and approach for timestamp supervised action segmentation using human object interaction
S Sayed, R Ghoddoosian, B Trivedi, V Athitsos
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
52023
Cognitive assessment in children through motion capture and computer vision: the cross-your-body task
SI Sayed, K Tsiakas, M Bell, V Athitsos, F Makedon
Proceedings of the 6th international Workshop on Sensor-based Activity …, 2019
32019
Cross your body: a cognitive assessment system for children
S Sayed, V Athitsos
Advances in Visual Computing: 16th International Symposium, ISVC 2021 …, 2021
22021
Deep feature tracker: A novel application for deep convolutional neural networks
M Parchami, SI Sayed
arXiv preprint arXiv:2108.00105, 2021
12021
Understanding human actions: Cognitive assessment and action segmentation using human object interaction
SI Sayed
The University of Texas at Arlington, 2022
2022
iGait: Vision-based Low-Cost, Reliable Machine Learning Framework for Gait Abnormality Detection
S Sayed
2017
Quantization and Classification of Sedimentation of a Coastal Hydraulic Model using Image Processing Technique
S Sayed, MS Balan, P Prabhudesai, MS Shelke
Methodology 1 (4), 2011
2011
Supplementary Material: Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos
R Ghoddoosian, S Sayed, V Athitsos
Vision-Learning-Mining Lab, University of Texas at Arlington
R Ghoddoosian, S Sayed, V Athitsos
ADAPTIVE COLOR GAMMA CORRECTION FOR SEDIMENT CLASSIFICATION AND QUANTIZATION IN A RESERVOIR
S Sayed, P Prabhudesai, S Balan, S Shelke
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Articles 1–15