Image Segmentation Algorithm Based on
Boltzmann Theory
Zhuang Jian, Yu Qing, Wang Sun'an
(School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China)
Abstract: Aiming at image segmentation algorithms problems, such as difficult auto
processing, weak normalizing, etc., based on the Boltzmann theory and the Metropolis rule,
a novel image segmentation algorithm is proposed, where the one-pixel-wide segmented
contours remaining continuous can be acquired quickly without setting pre-thresholds.
Simultaneously, the segmentation from rough to detail can be realized by adjusting the
temperature parameter to avoid the over-segmentation. The segmentation time is increased
with the reducing temperature due to the augment of the cluster¡¯s number. The problem of
the pixel repeated computing can be solved with parallel computation. The experimental
results indicate the better performance of efficiency, automatization and normalization of
the proposed algorithm than the K-means and rival penalize controlled competitive learning
algorithms.
Keywords: image segmentation; image processing; Boltzmann theory; robot vision