Publications

Full publication list can be accessed from the Google Scholar.

2024

Oishee Bintey Hoque, Samarth Swarup, Abhijin Adiga, Sayjro Kossi Nouwakpo, Madhav Marathe, “IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5460-5469

Oishee Bintey Hoque; Benjamin Hurt; Finn Mokrzycki; Anjali Mathew; Maryann Xue; Luka Gabitsinashvili; Haile Mokrzycki; Ranya Fischer; Nicholas Telesca; Lauren Aurelia Xue; Jacob Ritchie; J.D. Zamfirescu-Pereira; Mark E Whiting; Madhav Marathe, “COVID-19 non-pharmaceutical interventions: data annotation for rapidly changing local policy information”, Scientific Data - Nature, volume 10, Article number: 126 (2023).

Sifat Ahmed, Tonmoy Hossain, Oishee Bintey Hoque, Sujan Sarker, Sejuti Rahman, Faisal Muhammad Shah, “Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet) Approach”,SN Computer Science,2(4): 294. July 2021

Oishee Bintey Hoque, Mohammad Imrul Jubair, Al-Farabi Akash, Md Saiful Islam, “BdSL36: A Dataset for Bangladeshi Sign Letters Recognition”, In Proceedings of the Asian Conference on Computer Vision (ACCV), 2020 (ACCV MLCSA Workshop 2020) [Code & Dataset] [Presentation] [Demo Video] [PDF]
Oishee Bintey Hoque, Maisha Binte Rashid, K.M. Tawsik Zawad, “Autonomous Deblurring Images and Information Extraction from Documents Using CycleGAN and Mask RCNN”, In Proceedings of the 23rd International Conference on Computer and Information Technology, Dhaka, Bangladesh (ICCIT 2020), Dhaka, Bangladesh. [Presentation] [PDF]

Oishee Bintey Hoque, Md. Imrul Jubair, Md. Saiful Islam, Al-Farabi Akash, “Real Time Bangladeshi Sign Language Detection using Faster R-CNN”, In Proceedings of International Conference on Innovation in Engineering and Technology(ICIET), Dhaka, Bangladesh, 2018 (ICIET 2018). [Code & Dataset] [Presentation] [PDF] [Demo Video]

REAL-TIME BANGLADESHI SIGN LANGUAGE DETECTION USING FASTER R-CNN:
Accepted as a Full Paper in ICIET 2018. This is a real-time system which detects sign from a video and classifies the sign in the video. For the purpose of our work, we developed a robust dataset from scratch - BdSLImSet - which is available for further research. Our system is trained on Faster R-CNN and detects the sign with great accuracy. [Paper] [Dataset]
BANGLADESHI SIGN WORD RECOGNITION USING LSTM:
This is a system which detects sign words -sequence of actions- from a video. This works with sequential data and falls under the domain of action recognition. For the purpose of our work, we developed a robust dataset from scratch - BdSLVidSet - which is available for further research. This dataset is trained using CNN for feature extraction and LSTM to combine the sequential feature to recognize the words. Dataset