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

Faculty

• Dr Gargi Srivastava
• Dr Nirbhay Kumar Tagore

Recent Publications

• J Singh, G Srivastava and S Agarwal, "Metaverse: What, Why and Where?," 2023 IEEE Smart World Congress (SWC), Portsmouth, United Kingdom, 2023, pp. 1-6, doi: 10.1109/SWC57546.2023.10448621.
• S Agarwal and G Srivastava, "Recent Advancements in Structural Health Monitoring using Optical Sensor," 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT), Dehradun, India, 2023, pp. 110-114, doi: 10.1109/DICCT56244.2023.10110052.
• Agarwal, S, Srivastava, G and Prajapati, Y K Dual band Vis-IR absorber using bismuth based helical metamaterial surface. Opt Quant Electron 54, 772 (2022). https://doi.org/10.1007/s11082-022-04185-7
• 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