Papers
1
Total Citations
9
H-Index
1
About
Yue Juan is a researcher in computer vision, with a primary focus on saliency detection and its applications in robotic perception. Her most cited work, "RGB-D Saliency Detection: Dataset and Algorithm for Robot Vision" (2018), addresses a critical gap in the field by introducing a dedicated RGB-D saliency detection dataset tailored for robot vision systems. As RGB-D sensors become increasingly integral to robotics, this contribution provides a foundational benchmark for developing and evaluating algorithms that enable robots to identify salient objects in complex, depth-enhanced environments. The paper has garnered 9 citations, reflecting its relevance in advancing vision-based robotic interaction. Juan’s work bridges the gap between traditional saliency detection and the practical demands of autonomous systems, emphasizing the importance of multimodal data (RGB and depth) for robust scene understanding. Her research is particularly valuable for students and researchers exploring robotic navigation, object manipulation, and human-robot interaction, offering both a dataset and algorithmic insights that support the next generation of intelligent, perceptive machines.
Research Focus
Key Achievements
Top Papers
- 1RGB-D Saliency Detection: Dataset and Algorithm for Robot Vision9 citations · 2018