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Ph.D student, ECE Dept, University of Maryland liyi AT umiacs DOT umd DOT edu |
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Education · Aug 2004 – present Computer Vision Lab, Advised by Prof. Yiannis Aloimonos and Dr. Cornelia Fermuller ·
Aug-2001 - Oct 2004 · Aug-1998
- Oct 2001 South China University of Technology, Guangzhou · Human movement analysis. · Visual perception: optical illusion, etc · Robotics · Computer vision · Neuroscience: Mirror neuron,
· Jan 2008, Future Faculty Fellow, A. James
Clark · 2nd place, 1st Semantic Robot Vision Challenge
(sponsored by NSF), AAAI2007, · Best student paper, 10th International Conference on Frontiers in Handwriting Recognition, 2006. |
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Social Signal Processing Social intelligence is the facet of our cognitive abilities that guides us through the complex web of our everyday interactions, whether these require us to be a respected colleague on the workplace, a careful parent at home, a leader in our community, or simply a person others like to have around in a moment of relaxation.
Coordinated Action Coordinated action is one of the basic abilities for social interactions. All animal species grouping for defensive, reproductive or hunting needs have evolved complex communicative behaviors to obtain coordinated action (Frith, 2008). Joint action in humans has been formalized in many ways and constitutes one of the most interesting and recent chapters of cognitive neuroscience (Sebanz, 2006). Yet the quantification of this ability remain somewhat elusive, especially outside controlled experimental settings. The main problem is related to the complexity of the topic due to the large space of parameters one might want to consider in a numerical analysis. Furthermore, the formal analyses of such issues require also the clear definition of some metrics to define the efficacy of inter-individual action coordination.
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| Parkinson and CDC | ||
| 2. Active Robot Vision and Visual Perception | ||
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Vision Based Robot Mobile robots must have the ability to navigate autonomously and recognize objects in a previously-unknown indoor environment in many tasks. However, state-of-the-art techniques in robotics and in computer vision are not used together to achieve this goal. In this paper, we present a complete system that segments objects and utilizes the online image datasets for object recognition during navigation. Equipped with a quad camera, the mobile platform explores an unknown indoor area using SLAM, and separates the object from the background actively. Shape-based matching is performed on a set of shape images and the detected objects are highlighted with a bounding box and semantic label
Visual perception of the intensity
The contrast effect and its counterpart, the assimilation effect, paradoxically co-exist in intensity perception. It is natural to assume that there is a single structural mechanism that accounts to both effects. However, despite there is a large body of work studying individual illusions, the link between them is not well understood. Here, we propose a computational model using L1 minimization. Analog to the Ganglion cell in the visual path, the intensity of the stimulus is compressed by simple local average filters at random positions. This process can be formulated using an underdetermined linear system. Assuming the signal is sparse in the Fourier domain, the decoder finds the sparse solution of this system in the reconstruction phase. Experiments show that the contrast and the assimilation effect are the artifacts during the signal reconstruction, which suggests that both effects are the two sides of the same coin
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3D silhouette consistency. CVPR 2007 (Joint work with Prof. David Jacobs) |
Shape
Recognition. BMVC 2007 |
Text line
segmentation. IEEE Trans
on PAMI 2007 |
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Language
Identification. ECCV 2008,
PR 2008 |
Airline
ticket recognition. IEEE Trans
on PAMI 2004 |
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CMSC 250 Co-Instructor |
Discrete Structures (with Dr. Fawzi Emad) This is the introduction to to the algorithm for undergraduates majoring in computer science. 70 students, 25% workload. |
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Last updated: Oct 2009 |
Under Construction |