| Image skeletonizing and vectorization of the resulting skeleton are widely used techniques in object classification and shape detection. The skeletonizing process consists of several stages, e.g. dichotomization, boundary pruning, propagation and further detailed reduction. To incorporate uncertainties from these stages, we propose a hidden GMRF model which is applied to the original pixel intensity and outputs the vector formed skeleton in one step.; Currently, there is no widely agreed upon skeletonizing algorithm that can provide a definite single pixel skeleton result as needed in classification and other comparisons. The situation is similar to the vectorization process where important parameters are selected subjectively. These low level tasks are sequentially rigid and non-reversible, i.e. mistakes made in earlier processing steps will not have a chance to be corrected. Our approach can be applied to the results from any skeletonizing algorithm as initial values and generates posterior samples which can be used to compute posterior mean and confidence intervals.; To analyze a circular shape subset of interest from the estimated skeletons, we present two multivariate flexible models that compare image structures and shapes which vary. For each object, the locations and properties of points, which are sampled along the skeleton, are aligned automatically by the given shape information and set as responses. A multivariate statistic is computed to compare the groups of objects change over time. Within a single object, the covariance matrix of sampled points is assumed circulant. The second method assumed a deformed ellipse model for each object skeleton with measurement error i.i.d. distributed. The posterior mean shapes are used as responses for comparison at different time points. |
