41 citations · 52 across the 10 of their papers we have counts for
11 papers · 1 filter
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
Manashi Chakraborty, Aritri Chakraborty, Prabir Kumar Biswas +1
This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previ…
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
Manashi Chakraborty, Mayukh Roy, Prabir Kumar Biswas +1
In this paper, we present a texture aware lightweight deep learning framework for iris recognition. Our contributions are primarily three fold. Firstly, to address the dearth of la…
Segmentation of Lumen and External Elastic Laminae in Intravascular Ultrasound Images using Ultrasonic Backscattering Physics Initialized Multiscale Random Walks
Debarghya China, Pabitra Mitra, Debdoot Sheet
Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for loc…
SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
Sumanth Nandamuri, Debarghya China, Pabitra Mitra +1
Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of org…
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Debarghya China, Francis Tom, Sumanth Nandamuri +4
Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the abili…
Visual Attention for Behavioral Cloning in Autonomous Driving
Sourav Pal, Tharun Mohandoss, Pabitra Mitra
The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervi…