S
Santanu Chaudhury
Researcher at Indian Institute of Technology Delhi
Publications - 17
Citations - 143
Santanu Chaudhury is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Context (language use) & Convolutional neural network. The author has an hindex of 6, co-authored 17 publications receiving 105 citations. Previous affiliations of Santanu Chaudhury include Indian Institute of Technology, Jodhpur & Council of Scientific and Industrial Research.
Papers
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Proceedings ArticleDOI
2D-3D CNN Based Architectures for Spectral Reconstruction from RGB Images
Sriharsha Koundinya,Himanshu Sharma,Manoj Sharma,Avinash Upadhyay,Raunak Manekar,Rudrabha Mukhopadhyay,Abhijit Karmakar,Santanu Chaudhury +7 more
TL;DR: This work proposes a 2D convolution neural network and a 3D convolved neural network based approaches for hyperspectral image reconstruction from RGB images that achieves very good performance in terms of MRAE and RMSE.
Proceedings ArticleDOI
An Ontology Based Personalized Garment Recommendation System
TL;DR: A novel method for content-based recommendation of media-rich commodities using probabilistic multimedia ontology that enables interpretation of media based and semantic product features in context of domain concepts is presented.
Proceedings ArticleDOI
An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition
Anupama Ray,Manoj Sharma,Avinash Upadhyay,Megh Makwana,Santanu Chaudhury,Akkshita Trivedi,Ajay Kumar Singh,Anil Kumar Saini +7 more
TL;DR: An end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition, using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution.
Proceedings ArticleDOI
Drought Stress Classification Using 3D Plant Models
TL;DR: This paper proposes a novel end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study, and shows that the network outperforms conventional methods.
Posted Content
Drought Stress Classification using 3D Plant Models
TL;DR: In this article, the authors proposed an end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study to overcome the high degree of self-similarities and self-occlusions in plant canopy.