Davi Geiger, Archisman Rudra, Laurance Maloney
An image is often represented by a set of detected features. We get an enormous compression by representing images in this way. Fur(cid:173) thermore, we get a representation which is little affected by small amounts of noise in the image. However, features are typically chosen in an ad hoc manner. tures can be obtained using sufficient statistics. The idea of sparse data representation naturally arises. We treat the I-dimensional and 2-dimensional signal reconstruction problem to make our ideas concrete.
\Ve show how a good set of fea(cid:173)