Changes

===Rotation Invariant Simultaneous Clustering and Dictionary Learning===

===Rotation Invariant Simultaneous Clustering and Dictionary Learning===

Speaker: Yi-Chen Chen -- Data: October 13, 2011

Speaker: Yi-Chen Chen -- Date: October 13, 2011

We present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed approach is that it provides rotation invariant clustering which is useful in Content Based Image Retrieval (CBIR). We demonstrate through experimental results that the proposed rotation invariant clustering provides good retrieval performance than the standard Gabor-based method that has similar objectives.

We present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed approach is that it provides rotation invariant clustering which is useful in Content Based Image Retrieval (CBIR). We demonstrate through experimental results that the proposed rotation invariant clustering provides good retrieval performance than the standard Gabor-based method that has similar objectives.

===Deformation and Lighting Insensitive Face Recognition: From Optical Flow to Geodesics===

===Deformation and Lighting Insensitive Face Recognition: From Optical Flow to Geodesics===

Speaker: Anne Jorstad -- Data: October 20, 2011

Speaker: [http://www-users.math.umd.edu/~jorstad/ Anne Jorstad] -- Date: October 20, 2011

We seek to solve the face identification problem across variations in expression and lighting together in a single framework.  In order to understand variations in expression, a dense correspondence between images must be found, leading to algorithms similar to Optical Flow.  We present a new lighting-insensitive metric to drive this Optical Flow-like framework.  An extension of this work to the manifold of face images is then proposed, where a curve on the manifold represents the way a face might morph through time, allowing pixels to vary slowly as properties of the face change.  The length of the geodesic connecting a pair of faces defines their similarity for nearest neighbor matching.

We seek to solve the face identification problem across variations in expression and lighting together in a single framework.  In order to understand variations in expression, a dense correspondence between images must be found, leading to algorithms similar to Optical Flow.  We present a new lighting-insensitive metric to drive this Optical Flow-like framework.  An extension of this work to the manifold of face images is then proposed, where a curve on the manifold represents the way a face might morph through time, allowing pixels to vary slowly as properties of the face change.  The length of the geodesic connecting a pair of faces defines their similarity for nearest neighbor matching.