Relevant publications from the lab on vision.
Techniques inspired by biological vision are the state of the art in various application domains
We seek to use biological inspirations and hierarchical architectures to attack the problem of achieving better recognition performance.
Specific projects currently include
Most feedforward visual models contain engineered or pre-built spatial arrangement, e.g. in the form of receptive fields of the complex cells. We ask if it is possible to learn these arrangements using sensory stimulus.
It is well known that class-specific visual data, such as face images of a person under various transformations, lies on a low dimensional manifold embedded in high dimensional space. We ask if it is possible to use hierarchical models to help find functions which project a new sensory input onto class-specific manifolds.