Authors:
Michal Krumnikl
;
Eduard Sojka
and
Jan Gaura
Affiliation:
VŠB - Technical University of Ostrava, Czech Republic
Keyword(s):
Fuzzy C-Means, Segmentation, Stereo Matching, Disparity.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Clustering
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Fuzzy Logic
;
Health Engineering and Technology Applications
;
Image Understanding
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
This paper presents an extension to the popular fuzzy c-means clustering method by introducing an additional disparity cue. The creation of the clusters is driven by a degree of the stereo match and thus is able to separate the objects based on their different colour and spatial depth. In contrast to the other popular approaches, the clustering is not performed on the individual input images, but on the stereo pair, and takes into account the matching properties. The algorithm is capable of producing the segmentations, as well as the disparity
maps. The results of this algorithm show that the proposed method can improve the segmentation, under the condition of having the stereo image pair of the segmented scene.