Paras Maharjan

Papers

Total Citations

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About

Paras Maharjan is a computer vision researcher whose work focuses on advancing classical feature detection and extraction methods for modern applications. His most cited contribution, the "Fast LoG SIFT Keypoint Detector" (2023), reimagines the iconic Scale-Invariant Feature Transform (SIFT) by integrating a Laplacian of Gaussian (LoG) approach to accelerate keypoint detection while preserving SIFT’s renowned invariance to scale, rotation, noise, and illumination changes. This innovation addresses a critical need for efficient, robust feature extraction in real-time systems, such as autonomous navigation and image stitching. With 2 citations, this paper has already sparked interest in optimizing legacy algorithms for contemporary hardware. Maharjan’s work bridges the gap between foundational computer vision techniques and the demands of high-speed, resource-constrained environments. By improving the computational efficiency of SIFT without sacrificing its reliability, he provides a practical tool for researchers and engineers seeking to deploy classical methods in cutting-edge applications. His research underscores a commitment to making robust vision algorithms more accessible and performant, positioning him as a thoughtful contributor to the ongoing evolution of feature detection in the field.