We present a novel approach for representing shape knowledge in terms of example views of 3D objects. Typically, such data sets exhibit a highly nonlinear structure with distinct clusters in the shape vector space, preventing the usual encoding by linear principal component analysis (PCA). For this reason, we propose a nonlinear Mercerkernel PCA scheme which takes into account both the projecti...

Many of our everyday jobs we imagine robots accomplishing are defined via a variety of task-specific constraints. In order for robots to perform these tasks, the robot’s motion planners must respect these constraints. While a robotic manipulator moves and plans in its joint or configuration space, many constraints are naturally defined in task space. We focus on the specific constraint asking t...

Project selection is considered as an important problem in project management. It is multi-criteria in nature and is based on various quantitative and qualitative factors. The main purpose of this paper is to present a new rank-based method for project selection in outranking relation. According to this approach, decision alternatives were clustered in the concordance matrix and the discordance...

Edit distance is not the only approach how distance between two character sequences can be calculated. Strings can be also compared in somewhat subtler geometric ways. A procedure inspired by Random Indexing can attribute an D-dimensional geometric coordinate to any character N-gram present in the corpus and can subsequently represent the word as a sum of N-gram fragments which the string conta...

Proximity is a fundamental concept in any comprehensive ontology of space (Worboys 2001). The provision of a context-contingent translation mechanism between linguistic proximity measures (e.g. “near”, “far”) and metric distance measures is an important topic in current GIS research. After a discussion of context factors that mediate the relationship between linguistic and metric distance measu...

For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity or dissimilarity measure used. Instead of manually choosing a distance function in advance, a more promising approach is to learn a good distance function from data automatically. In this paper, we propose a kernel approach to impro...

In this paper, we investigate the use of a distance between Gaussian mixture models for speaker detection. The proposed distance is derived from the KL divergence and is defined as a Euclidean distance in a particular model space. This distance is simply computable directly from the model parameters thus leading to a very efficient scoring process. This new framework for scoring is compared to ...

We define a geodesic distance associated with the polarization space of non-singular coherency matrices. Its introduction on HPD(2) (the manifold of Hermitian positive definite matrices of dimension 2) can be directly related to the Jones calculus. The expression of distance and related notion of mean value in this particular metric space are also presented. We investigate the properties of thi...

Feature extraction has been recognized as a useful technique for pattern recognition. Feature extraction is accomplished by constructing a mapping from the measurement space to a feature space. Often, the mapping is chosen from an arbitrarily specified parametric family by optimizing the parameters with respect to a separability criterion. In the approach described here, a scalar distance funct...