Nabila Zrira
Papers
4
Total Citations
32
H-Index
4
About
Nabila Zrira is a computer vision researcher whose work focuses on the intersection of 3D object recognition, deep learning, and robotic manipulation. Her research addresses fundamental challenges in enabling machines to perceive and interact with their environment, particularly through the development of robust categorization and recognition systems. Zrira’s most influential work, “Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features” (2018), has garnered 15 citations and demonstrates her expertise in applying deep belief networks to complex visual tasks. She has made significant contributions to 3D object recognition using point clouds and deep belief networks, a paper that has been cited 8 times and addresses critical applications in robotics, aerospace, and industrial automation. Her research on object recognition for robotic grasping, including a fast visual bag-of-words approach using SURF features and SVM classifiers (4 citations), showcases her commitment to real-time, practical solutions. Zrira’s work bridges the gap between theoretical computer vision and real-world robotic applications, establishing her as a researcher dedicated to advancing autonomous systems through innovative visual perception techniques.
Research Focus
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