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Official Website of Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, India. / Research / Thematic Areas / Computer Vision and Image Processing

Research → Specializations → Computer Vision and Image Processing

Digital image processing is the use of computer algorithms to perform image processing on digital images. It allows a much more comprehensive range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Computer vision is an interdisciplinary field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images.

The research in this area is focused on image processing and computer vision, vision and machine learning, mathematical modeling and analysis of images, low-level image processing algorithms, medical image analysis and processing, features extraction and selection, pattern recognition and classification, video surveillance, and video processing.

Sub Areas under Computer Vision:

  • Image Processing, Computer Vision
  • Computer Vision and machine learning
  • Medical image analysis
  • Low-level Image analysis
  • Pattern recognition, Pattern Classification
  • Video Surveillance


  • Dr Gargi Srivastava
  • Dr Rahul Kumar

Recent Publications

  • Srivastava, Gargi; Srivastava, Rajeev; A survey on automatic image captioning International Conference on Mathematics and Computing 74-83 2018 Springer, Singapore
  • Srivastava, Gargi; Srivastava, Rajeev; Modification of Gradient Vector Flow using Directional Contrast for Salient Object Detection IEEE MultiMedia 26 4 Jul-16 2019 IEEE
  • Srivastava, Gargi; Srivastava, Rajeev; Salient Object Detection using Background Subtraction, Gabor Filters, Objectness, and Minimum Directional Backgroundness Journal of Visual Communication and Image Representation 62 330-339 2019 Elsevier
  • Srivastava, Gargi; Srivastava, Rajeev; An efficient modification of generalized gradient vector flow using directional contrast for salient object detection and intelligent scene analysis Multimedia Tools and Applications 79 19 13599-13619 2020 Springer
  • Srivastava, Gargi; Srivastava, Rajeev; User-interactive salient object detection using YOLOv2, lazy snapping, and Gabor filters Machine Vision and Applications 31 17 2020, Springer.
  • Srivastava, Gargi; Srivastava, Rajeev; Design, Analysis, and Implementation of Efficient Framework for Image Annotation ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16 3 2020 ACM
  • Srivastava, Gargi; Srivastava, Rajeev; Annotation of images using local binary pattern and local derivative pattern after salient object detection using minimum directional contrast and gradient vector flow Signal, Image and Video Processing 2020 Springer
  • Rahul kumar, Ridhi Arora, Vipul Bansal, Vinodh J Sahayasheela, Himanshu Buckchash, Javed Imran, Narayanan Narayanan, Ganesh N Pandian, and Balasubramanian Raman, “Classification of COVID-19 from Chest X-ray Images Using Deep Features and Correlation Coefficient”, Multimedia Tools and Applications, Vol. 81, pp. 27631–27655, 2022, Springer, (Status: Published, SCIE, IF: 2.577, Quartile: Q2).
  • Tanveer, A. H. Rashid, Rahul Kumar, and Balasubramanian Raman, “Parkinson’s disease diagnosis using neural networks: survey and comprehensive evaluation”, in Information Processing & Management, Vol. 59, No. 3, 2022, Elsevier, (Status: Published, SCI, IF: 7.466, Quartile: Q1)
  • Ridhi Arora, Vipul Bansal, Himanshu Buckchash, Rahul kumar, Vinodh J Sahayasheela, Narayanan Narayanan, Ganesh N Pandian, and Balasubramanian Raman, “AI-based Diagnosis of COVID19 Patients using X-ray Scans with Stochastic Ensemble of CNNs”, Physical and Engineering Sciences in Medicine, vol. 44, pp.1257–1271, 2021, Springer, (Status: Published, SCIE, IF: 7.099, Quartile: Q1).
  • Ankur Gupta, Rahul Kumar, Harkirat Singh Arora and Balasubramanian Raman, “C-CADZ: Computational Intelligence System for Coronary Artery Disease Detection Using Z-Alizadeh Sani Dataset”, Applied Intelligence, Vol. 52, pp. 2436–2464, 2022, Springer, (Status: Published, SCI, IF: 5.019, Quartile: Q2).
  • Rahul Kumar, Ankur Gupta, Harkirat Singh Arora and Balasubramanian Raman, “CBSN: Comparative Measures of Normalization Techniques for Brain Tumor Segmentation Using SRCNet”, Multimedia Tools and Applications, Vol. 81, pp. 13203–13235, 2022, Springer, (Status: Published, SCIE, IF: 2.577, Quartile: Q2).
  • Rahul Kumar, Ankur Gupta, Harkirat Singh Arora and Balasubramanian Raman, “IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT Based Fusion of MRI Sequences”, ACM Transactions on Internet Technology (ACM ToIT), Vol. 22, No. 1, Article No.: 9, pp 1-30, 2021, (Status: Published, SCIE, IF: 3.989, Quartile: Q1).
  • Rahul Kumar, Ankur Gupta, Harkirat Singh Arora, Ganesh Namasivayam Pandian and Balasubramanian Raman, “CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features”, IEEE Access, vol. 8, pp. 79440-79458, 2020, (Status: Published, SCIE, IF: 3.476, Quartile: Q2).