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Santanu Chaudhury

Researcher at Indian Institute of Technology, Jodhpur

Publications -  389
Citations -  4361

Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Deep learning. The author has an hindex of 28, co-authored 380 publications receiving 3691 citations. Previous affiliations of Santanu Chaudhury include Central Electronics Engineering Research Institute & Indian Institute of Technology Delhi.

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Proceedings ArticleDOI

Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation

TL;DR: This paper presents a novel low-cost hybrid Kinect-variety based content generation scheme for 3DTV displays and demonstrates that proposed robust integration provides guarantees on the completeness and consistency of the algorithm.
Proceedings ArticleDOI

Robust Image Colorization Using Self Attention Based Progressive Generative Adversarial Network

TL;DR: A Robust Image Colorization using self-attention based Progressive Generative Adversarial Network (RIC-SPGAN) which consists of residual encoder-decoder (RED) network and a Self-att attention based progressive Generative network (SP-GAN) in a cascaded form to perform the denoising and colorization of the image.
Proceedings ArticleDOI

Word image based latent semantic indexing for conceptual querying in document image databases

TL;DR: It is shown through extensive experiments on a large database that use of LSA for document images provides improvements in retrieval precision as is the case with electronic text documents.
Book ChapterDOI

An Intellectual Journey in History: Preserving Indian Cultural Heritage

TL;DR: A multimedia ontology encoded in the Multimedia Web Ontology Language (MOWL) is used to illustrate this paradigm by correlating the digital artefacts with their history as well their living context in today's world.
Book ChapterDOI

Unlocking the Mechanism of Devanagari Letter Identification Using Eye Tracking

TL;DR: Upon understanding the level of distortion acceptable for correct letter recognition and the processes involved in the identification of the letters, the OCR can be made more robust and the gap between human reading and machine reading can be narrowed down.