| Guide to convolutional neural networks HH Aghdam, EJ Heravi New York, NY: Springer 10 (978-973), 51, 2017 | 477 | 2017 |
| Active learning for deep detection neural networks HH Aghdam, A Gonzalez-Garcia, J Weijer, AM López Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 142 | 2019 |
| A practical approach for detection and classification of traffic signs using convolutional neural networks HH Aghdam, EJ Heravi, D Puig Robotics and autonomous systems 84, 97-112, 2016 | 113 | 2016 |
| Temporal coherence for active learning in videos J Zolfaghari Bengar, A Gonzalez-Garcia, G Villalonga, B Raducanu, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 48 | 2019 |
| A modified simulated annealing algorithm for static task scheduling in grid computing AAP Kazem, AM Rahmani, HH Aghdam 2008 International Conference on Computer Science and Information Technology …, 2008 | 45 | 2008 |
| An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods EJ Heravi, HH Aghdam, D Puig Pattern Recognition Letters 105, 50-58, 2018 | 39 | 2018 |
| Convolutional neural networks H Habibi Aghdam, E Jahani Heravi, H Habibi Aghdam, E Jahani Heravi Guide to Convolutional Neural Networks: A Practical Application to Traffic …, 2017 | 37 | 2017 |
| A Practical and Highly Optimized Convolutional Neural Network for Classifying Traffic Signs in Real-Time HH Aghdam, EJ Heravi, D Puig International Journal of Computer Vision (IJCV), 1-24, 2016 | 31 | 2016 |
| Convolutional neural networks FD ACHMAD, BN Prastowo Technology 3 (15), 155-160, 2014 | 29 | 2014 |
| Classification of Foods Using Spatial Pyramid Convolutional Neural Network. EJ Heravi, HH Aghdam, D Puig CCIA, 163-168, 2016 | 28 | 2016 |
| Recognizing traffic signs using a practical deep neural network HH Aghdam, EJ Heravi, D Puig Robot 2015: Second Iberian Robotics Conference: Advances in Robotics, Volume …, 2015 | 25 | 2015 |
| Guide to convolutional neural networks: a practical application to traffic-sign detection and classification EJ Heravi, HH Aghdam Springer, Berlin, 2017 | 24 | 2017 |
| A probabilistic approach for breast boundary extraction in mammograms HH Aghdam, D Puig, A Solanas Computational and Mathematical Methods in Medicine 2013, 2013 | 17 | 2013 |
| Analyzing the Stability of Convolutional Neural Networks against Image Degradation. HH Aghdam, EJ Heravi, D Puig VISIGRAPP (4: VISAPP), 370-382, 2016 | 14 | 2016 |
| Weijer, J. vd; and López, AM 2019. Active learning for deep detection neural networks HH Aghdam, A Gonzalez-Garcia Proceedings of the IEEE/CVF International Conference on Computer Vision …, 0 | 11 | |
| Classification of foods by transferring knowledge from ImageNet dataset EJ Heravi, HH Aghdam, D Puig Ninth International Conference on Machine Vision (ICMV 2016) 10341, 441-445, 2017 | 10 | 2017 |
| A unified framework for coarse-to-fine recognition of traffic signs using bayesian network and visual attributes HH Aghdam, EJ Heravi, D Puig International Conference on Computer Vision Theory and Applications 2, 87-96, 2015 | 10 | 2015 |
| Rad: Realtime and accurate 3D object detection on embedded systems HH Aghdam, EJ Heravi, SS Demilew, R Laganiere Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 8 | 2021 |
| Localizing pipe inspection robot using visual odometry HH Aghdam, HA Kadir, MR Arshad, M Zaman 2014 IEEE International Conference on Control System, Computing and …, 2014 | 8 | 2014 |
| Toward an optimal convolutional neural network for traffic sign recognition HH Aghdam, EJ Heravi, D Puig Eighth International Conference on Machine Vision (ICMV 2015) 9875, 108-112, 2015 | 6 | 2015 |
