Bhavneet Kaur
Papers
2
Total Citations
13
H-Index
2
About
Dr. Bhavneet Kaur is a computer vision researcher whose work focuses on advancing image processing techniques through intelligent thresholding and computational optimization. Her primary research areas include fog elimination in digital images, multicolor detection, and global optimum thresholding for enhanced image analysis. Dr. Kaur’s major contributions include the development of FEMT (Fog Elimination using Multiple Thresholds), a novel computational approach that effectively removes fog from images, improving clarity for applications in surveillance, autonomous navigation, and remote sensing. She has also pioneered an efficient method for multicolor detection using global optimum thresholding, enabling more accurate and robust image segmentation. Her work has garnered attention, with her most-cited paper, “FEMT: a computational approach for fog elimination using multiple thresholds,” receiving 7 citations, and her multicolor detection study earning 6 citations. These achievements highlight her impact in addressing real-world challenges in image analysis. Dr. Kaur’s research continues to inspire students and researchers exploring the intersection of computational algorithms and visual data processing, offering practical solutions for clearer, more reliable image interpretation in complex environments.
Research Focus
Key Achievements
Top Papers
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- 2