Konferenční příspěvek

Haindl M., Mikeš S.

Proceedings of the 18th International Conference on Pattern Recognition. ICPR 2006

IEEE Press

Hong Kong

2006

203-206

0-7695-2521-0

1051-4651

prof. Ing. Michal Haindl, DrSc. (ÚTIA - Oddělení rozpoznávání obrazů)

texture segmentation, unsupervised classification, image seg

A new unsupervised multispectral texture segmentation
method with unknown number of classes is presented. Multispectral
texture mosaics are locally represented by four
causal multispectral random field models recursively evaluated
for each pixel. The segmentation algorithm is based on
the underlying Gaussian mixture model and starts with an
over segmented initial estimation which is adaptively modified
until the optimal number of homogeneous texture segments
is reached. The performance of the presented method
is extensively tested on the Prague segmentation benchmark
using the commonest segmentation criteria and compares
favourably with several alternative texture segmentation
methods.