A multiresolution-fuzzy method for robust threshold selection in image segmentation
Threshold selection represents an essential topic in computer vision. The degradation of classical threshold selection methods when dealing with images with changing conditions limits the utilization of these methods in vision applications in unstructured environments. This paper presents a threshold selection method that can be easily adapted to the particularities of a vision application. The method analyses coarse-to-fine approximation descriptions of the histogram of the image. At each level of resolution the histogram descriptions are decomposed in histogram modes, some of which are classified as object modes by a fuzzy supervising system designed to adapt the analysis to the particularities of the application. The method shows high robustness to changes in illumination conditions and image noise. Some examples and its utilization in two applications are presented.
threshold selection multi-resolution analysis computer vision systems