Deepsat: a learning framework for satellite imagery S Basu, S Ganguly, S Mukhopadhyay, R DiBiano, M Karki, R Nemani Proceedings of the 23rd SIGSPATIAL international conference on advances in …, 2015 | 410 | 2015 |
Deep neural networks for texture classification—A theoretical analysis S Basu, S Mukhopadhyay, M Karki, R DiBiano, S Ganguly, R Nemani, ... Neural Networks 97, 173-182, 2018 | 97 | 2018 |
Learning sparse feature representations using probabilistic quadtrees and deep belief nets S Basu, M Karki, S Ganguly, R DiBiano, S Mukhopadhyay, S Gayaka, ... Neural Processing Letters 45, 855-867, 2017 | 95 | 2017 |
Deepsat v2: feature augmented convolutional neural nets for satellite image classification Q Liu, S Basu, S Ganguly, S Mukhopadhyay, R DiBiano, M Karki, ... Remote Sensing Letters 11 (2), 156-165, 2020 | 53 | 2020 |
A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture S Basu, S Ganguly, RR Nemani, S Mukhopadhyay, G Zhang, C Milesi, ... IEEE Transactions on Geoscience and Remote Sensing 53 (10), 5690-5708, 2015 | 52 | 2015 |
A theoretical analysis of deep neural networks for texture classification S Basu, M Karki, S Mukhopadhyay, S Ganguly, R Nemani, R DiBiano, ... 2016 international joint conference on neural networks (IJCNN), 992-999, 2016 | 39 | 2016 |
Cactusnets: Layer applicability as a metric for transfer learning E Collier, R DiBiano, S Mukhopadhyay 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 23 | 2018 |
Combining context-aware design-specific data and building performance models to improve building performance predictions during design C Chokwitthaya, Y Zhu, R Dibiano, S Mukhopadhyay Automation in construction 107, 102917, 2019 | 22 | 2019 |
Automated diagnostics for manufacturing machinery based on well-regularized deep neural networks R DiBiano, S Mukhopadhyay Integration 58, 303-310, 2017 | 15 | 2017 |
Pixel-level reconstruction and classification for noisy handwritten bangla characters M Karki, Q Liu, R DiBiano, S Basu, S Mukhopadhyay 2018 16th International Conference on Frontiers in Handwriting Recognition …, 2018 | 13 | 2018 |
A machine learning algorithm to improve building performance modeling during design C Chokwitthaya, Y Zhu, R Dibiano, S Mukhopadhyay MethodsX 7, 100726, 2020 | 12 | 2020 |
Enhancing the prediction of artificial lighting control behavior using virtual reality (VR): a pilot study C Chokwitthaya, R Dibiano, S Saeidi, S Mukhopadhyay, Y Zhu Construction Research Congress 2018, 216-223, 2018 | 11 | 2018 |
Core sampling framework for pixel classification M Karki, R DiBiano, S Basu, S Mukhopadhyay Artificial Neural Networks and Machine Learning–ICANN 2017: 26th …, 2017 | 10 | 2017 |
Context-Aware Design of Cyber-Physical Human Systems (CPHS) S Mukhopadhyay, Q Liu, E Collier, Y Zhu, R Gudishala, C Chokwitthaya, ... 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS …, 2020 | 8 | 2020 |
Food image analysis for measuring food intake in free living conditions R Dibiano, BK Gunturk, CK Martin Medical Imaging 2013: Image Processing 8669, 1005-1014, 2013 | 8 | 2013 |
Computer implemented system and method for high performance visual tracking S Mukhopadhyay, S Basu, M Stagg, R DiBiano, M Karki, J Weltman US Patent 10,757,369, 2020 | 3 | 2020 |
A symbolic framework for recognizing activities in full motion surveillance videos M Karki, S Basu, R DiBiano, S Mukhopadhyay, J Weltman, M Stagg 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2016 | 2 | 2016 |
An agile framework for real-time motion tracking S Basu, R Dibiano, M Karki, M Stagg, J Weltman, S Mukhopadhyay, ... 2015 IEEE 39th Annual Computer Software and Applications Conference 3, 205-210, 2015 | 2 | 2015 |
Maptrack-a probabilistic real time tracking framework by integrating motion, appearance and position models S Basu, M Karki, M Stagg, R DiBiano, S Ganguly, S Mukhopadhyay International Conference on Computer Vision Theory and Applications 2, 567-574, 2015 | 2 | 2015 |
Using applicability to quantifying octave resonance in deep neural networks E Collier, R DiBiano, S Mukhopadhyay Neural Information Processing: 27th International Conference, ICONIP 2020 …, 2020 | 1 | 2020 |