Umar Asif

Orcid: 0000-0001-5209-7084

According to our database1, Umar Asif authored at least 25 papers between 2011 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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On csauthors.net:

Bibliography

2023
DeepActsNet: A deep ensemble framework combining features from face, hands, and body for action recognition.
Pattern Recognit., July, 2023

2021
Preictal onset detection through unsupervised clustering for epileptic seizure prediction.
Proceedings of the IEEE International Conference on Digital Health, 2021

Towards Automated and Marker-Less Parkinson Disease Assessment: Predicting UPDRS Scores Using Sit-Stand Videos.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition.
CoRR, 2020

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification.
Proceedings of the Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019
SeizureNet: A Deep Convolutional Neural Network for Accurate Seizure Type Classification and Seizure Detection.
CoRR, 2019

Machine Learning for Seizure Type Classification: Setting the benchmark.
CoRR, 2019

Privacy Preserving Human Fall Detection using Video Data.
Proceedings of the Machine Learning for Health Workshop, 2019

Densely Supervised Grasp Detector (DSGD).
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling.
IEEE Trans. Pattern Anal. Mach. Intell., 2018

GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

EnsembleNet: Improving Grasp Detection using an Ensemble of Convolutional Neural Networks.
Proceedings of the British Machine Vision Conference 2018, 2018

2017
RGB-D Object Recognition and Grasp Detection Using Hierarchical Cascaded Forests.
IEEE Trans. Robotics, 2017

2016
Unsupervised segmentation of unknown objects in complex environments.
Auton. Robots, 2016

Simultaneous dense scene reconstruction and object labeling.
Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

2015
Discriminative feature learning for efficient RGB-D object recognition.
Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015

Efficient RGB-D object categorization using cascaded ensembles of randomized decision trees.
Proceedings of the IEEE International Conference on Robotics and Automation, 2015

2014
A model-free approach for the segmentation of unknown objects.
Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects.
Proceedings of the Computer Vision - ECCV 2014, 2014

2012
Motion Planning of a Walking Robot Using attitude Guidance.
Int. J. Robotics Autom., 2012

2011
Rapid Prototyping of a gait Generation method using Real-Time Hardware in Loop simulation.
Int. J. Model. Simul. Sci. Comput., 2011

Modeling, Simulation and Motion Cues Visualization of a Six-DOF Motion Platform for Micro-Manipulations.
Int. J. Intell. Mechatronics Robotics, 2011


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