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This dissertation investigates wavelets as a multiscale tool on non-Euclidean manifolds. The growing importance of using non-Euclidean manifolds as a geometric model for data comes from the diversity of the data collected. In this work we mostly deal with ...
This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invari ...
This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assumi ...
This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invari ...
In this thesis we deal with three different but connected questions. Firstly (cf. Chapter 2) we make a systematic study of the generalized notions of convexity for sets. We study the notions of polyconvex, quasiconvex and rank one convex set. We remark tha ...
We present a method for improving robustness in feature-based tracking of human motion. Motion flows of features estimated by a standard tracker are modified to be coherent with neighboring ones. This coherence constraint is computed based on a smooth appr ...
