Dr. Chandrajit Choudhury


Assistant Professor

National Institute of Technology (NIT) Silchar

Department of Electronics and Communications Engineering

Silchar, Assam, 788010, India

Email: chandrajit@ece.nits.ac.in

Phone: +91-9833726832

Date of Joining: 01/06/2018

Academic Experience: 5+ years (Teaching/ research), 5 years (Teaching assistant/ research)

Personal Webpage: http://ece.nits.ac.in/chandrajit/


  • Ph.D.:     IIT Powai, Mumbai, India, 2016
  • M.Tech.: IIT Kanpur, Kanpur, India, 2011
  • B.Tech.:  NIT Silchar, Silchar, India, 2006


  • June 2016 – Feb 2017:  Asstt. Professor (Adhoc), MCT, RGIT Versova, Mumbai.
  • Feb 2017 – May 2018Temporary faculty, ECE Deptt. NIT Silchar.
  • June 2018- Present:    Asst. Professor, ECE Deptt. NIT Silchar.


  • Image processing: deconvolution problems, image restoration blind and non-bind, hand written text recognition, facial expression and identity recognition, compressive sensing. Light-field image compression.
  • Medical Image processing: Mammogram image processing, X-ray image processing for pneumonia detection, endoscopic image processing for gastro-intestinal disease diagnosis.
  • Computer vision: Multi-view geometry, 3D reconstruction, Light-field imaging, Indian-traffic video analysis for anomaly detection.
  • Machine-learning: Kernel based learning, hybrid learning techniques, deep learning techniques like, CNN, Auto-encoders, adversarial learning, Attention-Map, Vision Transformers for text recognition, semi-supervised and self-supervised learning for novel object detection.


Chandrajit Choudhury obtained his Bachelor of Technology from NIT Silchar in 2006. He obtained his Masters of Technology from IIT Kanpur in 2011, and Ph.D. from IIt Powai, Mumbai in 2016.

He joined IBM India Pvt. Ltd., after his B.Tech. in 2006. He briefly served as an adhoc faculty in MCT, RGIT Mumbai, for almost seven months. He joined National Institute of Technology (NIT), Silchar on February 2017 as a temporary faculty. In 2018 he joined NIT Silchar as assistant professor.

His research interest, in broad sense, lies in computer vision and machine learning domain. His PhD. research was on light field image compressiong and 3D image reconstruction using mutli-view geometry. This work included topics like multi-view geometry, compressive sensing, sparse signal processing, L1 optimization, Learning Dictionaries for better signal representation. Besides these he has also worked in vision problems like, grayscale to color image coversion, underwater image restoration, image de-blurring, offline handwritten text recognition, Self-supervised object detection, Facial pose, expression and identity recognition. He has a little bit experience in medical image processing in breast cancer detection and gastro intestinal cancer detection. He has experience in machine learning algorithms like random-forest, kernel based methods, hybrid learning methods and deep learning based methods like: auto-encoders, CNN, GAN, Self-su[prevised learning, Vision Transformers. 



International Journals

  1. Sagar Deep Deb, Manish Sharma, Chandrajit Choudhury, Fazal Ahmed Talukdar, Rabul Hussain Laskar, “Facial Expression Classification using Multi-scale histogram of gradients.” International Journal of Image processing and Pattern Recognition. Vol.6 no.1, 2020.
  2. Poonam Sonar,Udhav Bhosle, Chandrajit Choudhury. “Comparative Study of Different Machine Learning Classifiers for Mammograms and Brain MRI Images”. International Journal in Image Mining 2017.

 International Conferences

  1.  Varun Kumar, Alankrita Kakati, Mousumi Das, Aarhisreshtha Mahanta, Puli Gangadhara, Chandrajit Choudhury and Fazal Talukdar, “Modelling India road traffic using concepts of fluid flow and Reynolds number for anomaly detection”, MVAI 2021, IIITDM Jabalpur.
  2. Dibyakanti Mahapatra, Chandrajit Choudhury, Ram Kumar Karsh, “Generator Based Methods for Off-Line Handwritten Character Recognition”. IEEE, ACTS, 2021.
  3. Mriganka Nath, Chandrajit Choudhury, “Automatic Detection of Pneumonia from Chest XRays Using Deep Learning”, MIND 2020, Springer.
  4. Dibyakanti Mahapatra, Chandrajit Choudhury, Ram Kumar Karsh, “Handwritten Character Recognition Using KNN and SVM Based Classifier over Feature Vector from Autoencoder”, MIND 2020, Springer.
  5. Poonam Sonar,Udhav Bhosle, Chandrajit Choudhury. “Mammography Classification Using Modified Hybrid SVM-KNN”. ICSPC 2017 (Best Paper Award).
  6. Chandrajit Choudhury, Y Tarun, A Rajwade, Subhasis Chaudhuri.“Low bit rate compression of video and Light field data using coded snapshot and learned dictionaries.” MMSP 2015.
  7. Chandrajit Choudhury, Subhasis Chaudhuri, “Disparity based compression technique for focused plenoptic images” ICVGIP 2014.
  8. Chandrajit Choudhury, Amaldev V and Subhasis Chaudhuri. “Multi-epipolar plane image based 3D reconstruction using robust surface fitting”. ICVGIP 2014.

National Conferences

  1. Sagar Deep Deb, Chandraiit Choudhury, Manish Sharma, Fazal Ahmed Talukdar, Rabul Hussain Laskar, “Frontal Facial Expression Recognition”. NCC 2020, IIT Kharagpur, IEEE.


Best Paper award in ICSPC 2017 , for the submission: “Mammography Classification Using Modified Hybrid SVM-KNN”, Poonam Sonar, Udhav Bhosle, Chandrajit Choudhury..

Ph.D. Scholars Guided

  1. Sandhya M (Part-time, On going)
  2. Sutrishna Paul (Full-time, On going)

M.Tech. Scholars Guided

  1. Dibyakanti Mahapatra, 2019-2020 Thesis: Handwritten Character Recognition using Deep Learning architectures.
  2. Rajesh Das, 2020-2021 Thesis: Optical Flow estimation for self-driving vehicles.
  3. Rupankar Das (Ongoing) 2021-Present Thesis: Under water image restoration using deep learning.

B.Tech. Projects

  1. Pose estimation with respect to yaw and pitch variation for video and still images 2019.
  2. Object Tracking and Movement prediction 2020
  3. Converting grayscale images to color images using GAN’s. 2021
  4. Gastro-intestinal cancer detection. (Ongoing)
  5. Handwritten word recognition. (Ongoing)
  6. Novel object detection using self-supervised learning. (Ongoing)

Workshops Organized

  1. One week training program in biomedical imaging and image processing, July 2018.
  2. One week webinar on recent trends in signal processing and communications, September 2020.