Robust shape detection using fuzzy clustering: practical applications

Problem of accurate shape detection in practical applications is considered. The work is motivated by several experimental studies in granular flow research, where in general, the objects imaged are spherical particles. A new algorithm called the divide and conquer noise fuzzy c-shells clustering (D&C-NFCS) is proposed, with these particular applications in mind, for detecting circles and ellipses in noisy images without requiring prior knowledge of the exact number of clusters. This unsupervised algorithm uses Hough transform (HT) based methods to provide very rough initial estimates of the cluster prototypes for use in the fuzzy c-shells (FCS) type algorithms. The results of HT are also used to segment the raw data so that the FCS algorithm can be applied to detect one cluster at a time. The concept of recently introduced noise clustering algorithm is also used to make the algorithm robust against noise. Results of this algorithm for several practical examples from granular flow experiments are shown demonstrating high speed and accuracy. When compared with the methods based on HT alone, this approach results in a significant improvement in detection accuracy. Cluster validity issues are also discussed.