S. P. Maniraj

Papers

Total Citations

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About

S. P. Maniraj is a researcher focused on the intersection of computer vision and deep learning, with a particular emphasis on object boundary detection. His work addresses a critical challenge in autonomous systems—enabling machines to accurately perceive and segment their environment. In his highly regarded 2019 paper, "Object Boundary Detection using Neural Network in Deep Learning," Maniraj advances beyond traditional segmentation methods by proposing a neural network-based approach that generates partial object boundaries from image patches. This technique has direct applications in self-driving cars, domestic robotics, and other automated systems where precise environmental understanding is essential. With 3 citations, this work has already begun influencing subsequent research in the field. Maniraj’s contributions are particularly valuable for students and engineers working on real-time perception systems, as his methods offer a practical pathway to improving machine vision accuracy. His research continues to bridge the gap between theoretical deep learning models and their deployment in safety-critical, real-world applications.