יום שלישי, 21.2.2012, 11:30
חדר 1061, בניין מאייר, הפקולטה להנדסת חשמל
Sparse representation theory has been increasingly used in signal processing
and machine learning. In this work we introduce a hierarchical sparse modeling approach which integrates information from the image patch level to derive a mid-level invariant image and pattern representation. The proposed framework is based on a hierarchical architecture of dictionary learning for sparse coding in a cortical (log-polar) space, combined with a novel pooling operator which incorporates the Rapid transform and max
pooling to attain rotation and scale invariance.
The invariant sparse representation of patterns here presented- can be used in different
object recognition tasks. Promising results are obtained for three applications --
2D shapes classification, texture recognition and object detection.
joint work with Guillermo Sapiro, University of Minnesota.