Ranjini Surendran
Papers
2
Total Citations
11
H-Index
2
About
Ranjini Surendran is a researcher at the forefront of computer vision and artificial intelligence, with a specialized focus on scene understanding and indoor scene recognition. Her work addresses one of the most challenging problems in robotics and computer vision: enabling machines to perceive and interpret complex visual environments. Surendran’s major contributions include developing an attention-based approach for indoor scene recognition that combines feature selection-based transfer learning with a deep liquid state machine, a novel framework that enhances classification accuracy while reducing computational overhead. Her 2023 paper on this topic has garnered 7 citations, reflecting its growing influence in the field. Additionally, her comprehensive 2020 review on scene understanding using deep neural networks—covering objects, actions, and events—has earned 4 citations, serving as a valuable resource for researchers exploring the intersection of perception and action. Through her innovative methodologies and synthetic reviews, Surendran is advancing the capabilities of autonomous systems, making her work essential reading for students and researchers interested in deep learning applications for visual intelligence.
Research Focus
Key Achievements
Top Papers
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- 2