معرفی
Jalila Filali is a Researcher at Université Laval, affiliated with the Computer Vision and Systems Laboratory. Her work focuses on advancing computer vision techniques through ontology integration and deep learning models, with applications in environmental monitoring and wildlife tracking. She holds a post-doctoral position and specializes in image classification, object detection, and semantic annotation systems.
Her research interests include developing ontology-driven systems for image annotation, comparing machine learning models like HMAX and Bag-of-Visual-Words for large-scale classification, and applying YOLOv5 frameworks to analyze wildlife behavior via camera-collar videos. She has contributed to projects involving environmental information extraction in northern Quebec ecosystems.
Key technical trends in her publications include hybrid ontology-machine learning architectures, classifier fusion techniques, and the integration of visual features with semantic knowledge bases. She has explored both theoretical advancements in computer vision algorithms and practical implementations in ecological monitoring contexts.
Jalila Filali's work is supported by the Computer Vision and Systems Laboratory at Université Laval, where she contributes to interdisciplinary projects merging artificial intelligence with ecological research.
