Bikash Santra

Orcid: 0000-0002-6833-140X

Affiliations:
  • Indian Statistical Institute, Kolkata, India


According to our database1, Bikash Santra authored at least 14 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT.
CoRR, 2024

2023
Isolating Features of Object and Its State for Compositional Zero-Shot Learning.
IEEE Trans. Emerg. Top. Comput. Intell., October, 2023

Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas from CT Images.
Proceedings of the Applications of Medical Artificial Intelligence, 2023

2022
Part-based annotation-free fine-grained classification of images of retail products.
Pattern Recognit., 2022

Graph-based modelling of superpixels for automatic identification of empty shelves in supermarkets.
Pattern Recognit., 2022

Bi-Modal Compositional Network for Feature Disentanglement.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021
An end-to-end annotation-free machine vision system for detection of products on the rack.
Mach. Vis. Appl., 2021

2020
Graph-based non-maximal suppression for detecting products on the rack.
Pattern Recognit. Lett., 2020

Deterministic dropout for deep neural networks using composite random forest.
Pattern Recognit. Lett., 2020

2019
A comprehensive survey on computer vision based approaches for automatic identification of products in retail store.
Image Vis. Comput., 2019

2018
Anubhav: recognizing emotions through facial expression.
Vis. Comput., 2018

2017
A non-invasive approach for estimation of hemoglobin analyzing blood flow in palm.
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017

2016
Local dominant binary patterns for recognition of multi-view facial expressions.
Proceedings of the Tenth Indian Conference on Computer Vision, 2016

Local saliency-inspired binary patterns for automatic recognition of multi-view facial expression.
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016


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