Papers
1
Total Citations
7
H-Index
1
About
Mariana Chan-Ley is a computer vision researcher whose work addresses foundational challenges in autonomous systems and robotic perception. Her primary research areas include camera self-localization, invariant property extraction, and visual-based navigation for uncalibrated systems. Her most cited work, "Self-localization of an uncalibrated camera through invariant properties and coded target location" (2020, 7 citations), tackles the critical problem of enabling a single camera to determine its position without prior calibration—a task many modern robotic devices still struggle with, often relying on non-visual sensors. Chan-Ley’s contribution lies in demonstrating how invariant geometric properties and coded targets can replace costly calibration procedures, offering a more flexible and cost-effective solution for autonomous navigation. This work has implications for robotics, augmented reality, and surveillance, where visual self-localization remains a bottleneck. Her research stands out for its potential to reduce hardware dependency, making artificial optical systems more accessible. Though her citation count is modest, her focus on a persistent problem in computer vision signals a promising trajectory for advancing autonomous visual systems.
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Top Papers
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