Statistical morphological skeleton for representing and coding noisy shapes

作     者：Foresti, GL Regazzoni, CS 

作者机构：Univ Udine Dept Math & Comp Sci I-33100 Udine Italy Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy 

出 版 物：《IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING》 (IEE Proc Vision Image Signal Proc)

年 卷 期：1999年第146卷第2期

页      面：85-92页

核心收录：

主　　题：successive iterations shape representation statistical analysis signal detection skeleton extraction visual-based surveillance application noisy shapes coding parametrised binary morphological operators probability feature extraction robustness noisy binary images image representation Computer vision and image processing techniques image coding surveillance scheduling strategy Interpolation and function approximation (numerical analysis) stochastic optimisation probabilistic interpretation skeletonisation shape descriptor Other topics in statistics Image and video coding image thinning iterative methods mathematical morphology statistical morphological skeleton change-detection method 

摘      要：A new shape descriptor obtained by skeletonisation of noisy binary images is presented. Skeleton extraction is performed by using an algorithm based on a new class of parametrised binary morphological operators, taking into account statistical aspects. Parameters are adaptively selected during the successive iterations of the skeletonisation operation to regulate the characteristics of the shape descriptor. A probabilistic interpretation of the scheduling strategy used for parameters is proposed by analogy to stochastic optimisation techniques. Skeletonisation results on patterns extracted by a change-detection method in a visual-based surveillance application are reported. Results show the greater robustness of the proposed method as compared with other morphological approaches.