Rajkumar K. K.

Work place: Department of Information Technology, Kannur University, Kannur, India

E-mail: rajatholy@yahoo.com

Website:

Research Interests: Computer Vision, Machine Learning, Data Mining, Big Data, Big Data Analytics, Data Analysis, Deep Learning

Biography

Rajkumar K. K. completed his PhD at MG University, Kerala, India, focusing on a specialization in Medical Image Processing. Currently, he holds position of a Professor at the Department of Information Technology, Kannur University, Kerala, India. Rajkumar has made significant contributions to the academic community, with over 25 of his research papers being published in various reputed International Journals. Additionally, he has been an active participant in several international conferences, where he presented his research findings. His research areas encompass a diverse range of topics, including Hyperspectral Image Processing, Computer Vision, Visual Cryptography, Machine Learning & Deep Learning, Data Mining, and Big Data Analytics.

Author Articles
Seamless Panoramic Image Stitching Based on Invariant Feature Detector and Image Blending

By Megha V. Rajkumar K. K.

DOI: https://doi.org/10.5815/ijigsp.2024.04.03, Pub. Date: 8 Aug. 2024

Image stitching is the method of creating a composite image from several images of the same scene. This paper addresses the issues of generating a seamless panoramic image from a series of photographs of the same scene by varying scale, orientation and illumination. A feature-based approach is proposed in this paper. Scale Invariant Feature Transform (SIFT) is used to detect key points in the image. SIFT is both a feature detector and descriptor. The common region between different images is identified by comparing the feature descriptors of each image. Brute-Force matcher with KNN algorithm is used for feature matching. The outliers in the matching features are eliminated by Random Sample Consensus (RANSAC) algorithm. To create seamless image, alpha blending operation is applied. Experiments are conducted on UDISD (Unsupervised Deep Image Stitching Data set). The overall performance of the proposed stitching method is evaluated based on metrics such as PSNR, SSIM, RMSE, MSE and UIQI, and the proposed stitching algorithm yields good result with seamless stitched image.

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