Mohamed Amine Marnissi
Papers
2
Total Citations
23
H-Index
2
About
Mohamed Amine Marnissi is a researcher advancing the field of computer vision, with a primary focus on thermal imaging and object detection. His work addresses critical challenges in infrared-based perception, particularly for pedestrian detection in low-visibility environments. Marnissi’s most cited paper, “Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection” (2021, 16 citations), introduces a novel GAN-based approach to improve contrast and detail in thermal images, directly enhancing detection accuracy for applications in video surveillance, robotics, and night vision. Building on this, his 2022 study “Feature distribution alignments for object detection in the thermal domain” (7 citations) tackles domain adaptation, aligning feature distributions to boost model robustness across varying thermal conditions. These contributions are vital for autonomous systems and security technologies, where reliable thermal perception is essential. Marnissi’s work demonstrates a clear trajectory from image enhancement to domain alignment, showcasing his impact on making thermal computer vision more practical and effective. His research continues to inspire advancements in real-world, low-light sensing.
Research Focus
Key Achievements
Top Papers
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- 2