REGION-BASED FUZZY CLUSTERING IMAGE SEGMENTATION ALGORITHM WITH KULLBACK-LEIBLER DISTANCE

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Multiresolution Image Registration Based on Kullback-Leibler Distance

This paper extends our prior work on multi-modal image registration based on the a priori knowledge of the joint intensity distribution that we expect to obtain, and Kullback-Leibler distance. This expected joint distribution can be estimated from pre-aligned training images. Experimental results show that, as compared with the Mutual Information and Approximate Maximum Likelihood based registr...

Model Confidence Set Based on Kullback-Leibler Divergence Distance

Consider the problem of estimating true density, h(.) based upon a random sample X1,…, Xn. In general, h(.)is approximated using an appropriate in some sense, see below) model fƟ(x). This article using Vuong's (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(.).Application of such confide...

Image Segmentation Based on Fuzzy Clustering Algorithm

Image segmentation plays an important role for machine vision applications. In this paper, we present a new segmentation strategy based on fuzzy clustering algorithm. The new algorithm includes the spatial interactions by assuming that the statistical model of segmented image regions is Gibbs Random Field ( GRF ). We specitjl the neighborhood system, the associated cliques. and the potentials o...

Model Averaging Based on Kullback-leibler Distance.

This paper proposes a model averaging method based on Kullback-Leibler distance under a homoscedastic normal error term. The resulting model average estimator is proved to be asymptotically optimal. When combining least squares estimators, the model average estimator is shown to have the same large sample properties as the Mallows model average (MMA) estimator developed by Hansen (2007). We sho...