Adaptive pattern recognition system for scene segmentation
Toshiro Kubota · Optical Engineering · 1998
Robust pattern recognition within the Bayesian framework for scene segmentation/boundary detection is oftentimes hampered by the presence of textures within natural images. In order to improve segmentation/boundary detection on natural images, it is necessary to combine multiple features effectively. This paper introduces two algorithms for combining both color and texture features to assist boundary detection processes. One is to combine features through the surface processes and the other through the line processes. The algorithms can be generalized for combining any number of feature sets. Keywords: boundary detection, Bayesian model, texture, features, minimization 1 Introduction In this paper, color features are defined as features obtained by pixel-wise operations such as scaling and mean subtraction on the original data, while texture features are defined as features obtained by combination of pixel-wise operations and local operations such as averaging and linear filters. The...