Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

An Efficient QBIR System Using Adaptive Segmentation and Multiple Features

AbstractQuery by Image Content Retrieval abbreviated as QBIR, has become new thirst now a days. By using this systems, user can retrieve the similar images of an already existed image (or) a rough sketch (or) a symbolic representation. To make more efficient and user friendly QBIR multiple features areemployed. This paper proposes a novel approach for image retrieval using adaptive k-means clustering and shape, texture features. The experimental results portraystheperformance of the proposed retrieval system in terms of better precision. To evaluate the proposed method COIL and MPEG-7 shape 1 datasets are used