Izvorni znanstveni članak
Recognition and Learning with Polymorphic Structural Components
Mark Burge
; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria
Wilhelm Burger
; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria
Wolfgang Mayr
; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria
Sažetak
We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples.
Ključne riječi
recognition; learning; polymorphic structural components; Gabor probe structural description
Hrčak ID:
150305
URI
Datum izdavanja:
30.3.1996.
Posjeta: 743 *