Maryam Ghorbani

About

Maryam Ghorbani is a computer vision and robotics researcher whose work centers on advancing open-ended 3D object recognition for service robots operating in real-world, human-centric environments. Her research tackles one of the most persistent challenges in applied robotics: enabling machines to accurately and reliably identify objects under the demanding constraints of real-time performance and environmental variability. Ghorbani's most notable contributions investigate the interplay between shape features, color constancy, color spaces, and similarity measures in 3D object recognition pipelines. Her 2020 paper on this topic has garnered 18 citations, reflecting meaningful engagement from the robotics and computer vision communities, with a follow-up journal publication in 2021 further extending the work's reach. By systematically examining how these foundational visual features influence recognition performance, her research provides practical insights that can directly improve the perceptual capabilities of autonomous service robots. Her focus on open-ended learning — where systems must continually adapt to new objects without exhaustive retraining — positions her work at a forward-looking frontier of artificial intelligence and human-robot interaction. Ghorbani's contributions offer valuable guidance for researchers and engineers striving to build more robust, flexible robotic perception systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Investigating the Importance of Shape Features, Color Constancy, Color\n Spaces and Similarity Measures in Open-Ended 3D Object Recognition

   18 citations · 2020
2. 2
   Investigating the importance of shape features, color constancy, color spaces, and similarity measures in open-ended 3D object recognition

   2 citations · 2021

Key Collaborators

Contact & Links