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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6659| Title: | Learning Object-Independent Modes of Variation with Feature Flow Fields |
| Keywords: | AI Invariance Optical Flow Color Constancy Object Recognition image manifold |
| Issue Date: | 9-Oct-2013 |
| Description: | We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-2001-021 http://hdl.handle.net/1721.1/6659 |
| Appears in Collections: | MIT Items |
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