This paper analyzes the theoretical under­ pinnings of recent proposals for computing Dempster-Shafer Belief functions from ATMS labels. Such proposals are intended to be a means of integrating symbolic and numeric rep­ resentation methods and of focusing search in the ATMS. This synthesis is formalized us­ ing graph theory, thus showing the relation­ ship between graph theory, the logic-theore...

In this work, we present a computational method for solving second kindnonlinear Fredholm Volterra integral equations which is based on the use ofHaar wavelets. These functions together with the collocation method are thenutilized to reduce the Fredholm Volterra integral equations to the solution ofalgebraic equations. Finally, we also give some numerical examples that showsvalidity and applica...

here a posteriori error estimate for the numerical solution of nonlinear voltena- hammerstein equations is given. we present an error upper bound for nonlinear voltena-hammastein integral equations, in which the form of nonlinearity is algebraic and develop a posteriori error estimate for the recently proposed method of brunner for these problems (the implicitly linear collocation method).we al...

in this paper, a method for finding an approximate solution of a class of two-dimensional nonlinear volterra integral equations of the first-kind is proposed. this problem is transformedto a nonlinear two-dimensional volterra integral equation of the second-kind. the properties ofthe bivariate shifted legendre functions are presented. the operational matrices of integrationtogether with the produ...

SUMMARY In this paper, we deal with the perceptual organization which is a crucial problem in computer vision. Perceptual organization consists in structuring the image into perceptually significant groups. These structures will be used in higher level processes such as image understanding or object recognition. The grouping is based on application of the Gestaltic perceptual phenomena. Origina...

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptivenetwork-based fuzzy inference system, where a back propagation learning rule is employed to tune the evidence assignment functions using training samples. The...

Dempster-Shafer evidence theory is an efficient method to process uncertain, incomplete and vague information in data fusion. The combination of conflicting bodies of evidence has been one of the most important issues in Dempster-Shafer evidence theory since the example of counter-intuitive results produced by Dempster’s rule was found. Based on the framework of the Dempster-Shafer evidence the...