Projects in my group are divided in five categories.
Inspired by Marr's quest for a general vision solution, we pose image parsing aa computing process that
infers the following representations from a single image under a common framework.
click the buttons/text to view projects
2, Video Parsing
Video parsing extends image parsing to larger scenes and longer duration to account for the interactions between agents and manipulable objects.
3, Modeling, Learning, Inference Algorithms, and Basic Theories
The progress of computer vision as a scientific discipline should be measured by
its development of models, algorithms, and theories, so that its problems and solutions
can be understood analytically.
Modeling and Learning |
Inference Algorithms |
Basic Theories |
- Minimax Entropy Learning
- Prior Learning
- Learning by Information Projection
- Learning Implicit and Explicit Manifolds
- Stochastic Image Grammar
- And-or-graph Learning
- PAC-learning of And-or graph models
- Energy landscape of non-convex optimization problems
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4, Real World Applications
Only when commercial requests are clearly presented, the progress shall be measured by
specific datasets and benchmarks.
We have produced commericial systems based on projects at our Lotus Hill Institute.
Persistent Surveillance |
Look at Humans |
Computational Photography and arts |
Aerial Image Understanding |
Intelligent Vehicle |
Background Modeling
Tracking in AoG
Actions
Events and Ontology
Image to Text (I2T)
PTZ Camera Tracking
Counting People |
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- Scene modeling
- Top-down/ Bottom-up Inference
- Driving Log (I2T)
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5, Core and Long Standing Issues in Human and Computer Vision
These are long-standing debates which can only be answered numerically using statistical and information theoretical approaches.
Issue I: Discriminative vs. Generative methods |
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Issue IV: Texture vs. Texton (similarly Context v.s. Hierarchy, Sketchable v.s. Non-Sketchable, Trackable v.s. Non-Trackable) |
Downloads (more downloads from my lab webpage)