Saibal Ghosh
Papers
9
Total Citations
63
H-Index
6
About
Saibal Ghosh is a researcher specializing in computer vision, machine learning, and human-robot interaction, with a particular focus on dimensionality reduction techniques and their applications in intelligent robotic systems. His work centers on developing advanced variants of locality preserving projection (LPP) and manifold learning methods to address real-world challenges in vision sensor data processing, including illumination variation, sensor noise, and high-dimensional data complexity. Among his most notable contributions is his development of rough entropy-based granular features for 2-D LPP, his most-cited work with 18 citations, which preserves spatial neighborhood information in image-based pattern recognition. Ghosh has also made significant strides in robot navigation guidance, proposing bilateral LPP frameworks that enable robots to reliably interpret human visual cues in photometrically challenging environments. His research extends to probabilistic and Bayesian approaches, including Gaussian process regression with automatic relevance determination kernels for sonar-based human leg localization, cited 11 times. Across his growing publication record, Ghosh has accumulated over 60 citations, demonstrating increasing community recognition. His interdisciplinary work bridges robust statistical modeling, gesture and sign language recognition, and collaborative robotics, making meaningful contributions toward safer and more intelligent human-robot coexistence in dynamic real-world environments.
Research Focus
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