| GiB: A ame Theory nspired inarization Technique for Degraded Document Images S Bhowmik, R Sarkar, B Das, D Doermann IEEE Transactions on Image Processing 28 (3), 1443-1455, 2018 | 47 | 2018 |
| An adaptive foreground-background separation method for effective binarization of document images B Das, S Bhowmik, A Saha, R Sarkar Proceedings of the Eighth International Conference on Soft Computing and …, 2018 | 8 | 2018 |
| Online filtering over expanding graphs B Das, E Isufi 2022 56th Asilomar Conference on Signals, Systems, and Computers, 43-47, 2022 | 7 | 2022 |
| Learning expanding graphs for signal interpolation B Das, E Isufi ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 7 | 2022 |
| Graph filtering over expanding graphs B Das, E Isufi 2022 IEEE Data Science and Learning Workshop (DSLW), 1-8, 2022 | 7 | 2022 |
| Task-aware connectivity learning for incoming nodes over growing graphs B Das, A Hanjalic, E Isufi IEEE Transactions on Signal and Information Processing over Networks 8, 894-906, 2022 | 4 | 2022 |
| Tensor Graph Decomposition for Temporal Networks B Das, E Isufi ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 2 | 2024 |
| Online Edge Flow Prediction Over Expanding Simplicial Complexes M Yang, B Das, E Isufi ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 1 | 2023 |
| Online Graph Filtering Over Expanding Graphs B Das, E Isufi IEEE Transactions on Signal Processing, 2024 | | 2024 |
| Online Learning Of Expanding Graphs S Rey, B Das, E Isufi arXiv preprint arXiv:2409.08660, 2024 | | 2024 |
| Online Vector Autoregressive Models Over Expanding Graphs B Das, E Isufi ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | | 2023 |
| Active Semi-Supervised Learning for Diffusions on Graphs B Das, E Isufi, G Leus ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | | 2020 |
| Distributed Kernel-Based Reconstruction of Graph Signals B Das, E Isufi, G Leus Adversarial Loss for Semantic Segmentation of Aerial Imagery, 45, 0 | | |
