We have developed a generic ontology of objects, and a knowledge base of everyday physical objects. Objects are represented as assemblies of functional features and their spatial relations. Generic shape information of objects and features is stored using a partial boundary representation. Formfunction reasoning is applied to deduce geometric shape elements from a feature’s functions. We have a...

We present an ontology of objects, functions, and generic shape representation that supports form-function reasoning. By reasoning from the mechanical and other functions of objects to their geometric shape requirements, we deduce the generic shape representation of objects, which we represent as a partial boundary representation composed of primitive geometric shape elements and their spatial ...

We propose two differential geometric representations of planar shapes using: (i) direction functions and (ii) curvature functions, of their boundaries. Under either representation, planar shapes are treated as elements of infinite-dimensional shape spaces. Pairwise differences between the shapes are quantified using the lengths of geodesics connecting them on the shape spaces. We specify the g...

In this paper, we propose a pair of shape features for shape-similarity search of 3D polygonal-mesh models. The shape features are extension of the D2 shape functions proposed by Osada et al. [Osada01, Osada02]. Our proposed shape features are tolerant of topological variations and geometrical degeneracies. Our shape feature is also invariant to similarity transformation. Experiments showed tha...

In this paper we propose a strategy to shape adaptive radial basis functions through potential functions. DYPOF (DYnamic POtential Functions) neural network (NN) is designed based on radial basis functions (RBF) NN with a two-stage training procedure. Static (fixed number of RBF) and dynamic (ability to add or delete one or more RBF) versions of our learning algorithm are introduced. We investi...

Proteins are the main working molecules of a cell. Typical protein molecules contain thousands or more atoms and have complex shapes. Understanding how atoms in proteins pack and how they form intricate shape in three dimensional space is an important task, as it helps us to gain insight on how proteins fold and how they carry out biological functions. There exists a large body of work using ge...

The Meshless Local Petrov–Galerkin method (MLPG) is one of the popular meshless methods that has been used very successfully to solve several types of boundary value problems since the late nineties. In this paper, using a generalized moving least squares (GMLS) approximation, a new direct MLPG technique, called DMLPG, is presented. Following the principle of meshless methods to express everyth...

Email has become an integral part of everyday life. Without a second thought we receive bills, bank statements, and sales promotions all to our inbox. Each email has hidden features that can be extracted. In this paper, we present a new mechanism to characterize an email without using content or context called Email Shape Analysis. We explore the applications of the email shape by carrying out ...