An Online Adaptive Model for Location Prediction

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications

Fast Fréchet queries

Inspired by video analysis of team sports, we study the following problem. Let P be a polygonal path in the plane with n vertices. We want to preprocess P into a data structure that can quickly count the number of inclusion-minimal subpaths of P whose Fréchet Distance to a given query segment Q is at most some threshold value e. We present a data structure that solves an approximate version of this problem: it counts all subpaths whose Fréchet Distance is at most e, but this count may also include subpaths whose Fréchet Distance is up to (2+3 \sqrt 2) . For any parameter n¿=¿s¿=¿n 2, our data structure can be tuned such that it uses O(s polylog n) storage and has O((n/\sqrt) polylog n) query time. For the special case where we wish to count all subpaths whose Fréchet Distance to Q is at most e·length(Q), we present a structure with O(n polylog n) storage and O(polylog n) query time

Dilation Matrices for Nonseparable Bidimensional Wavelets

For nonseparable bidimensional wavelet transforms, the choice of the dilation matrix is all–important, since it governs the downsampling and upsampling steps, determines the cosets that give the positions of the filters, and defines the elementary set that gives a tesselation of the plane. We introduce nonseparable bidimensional wavelets, and give formulae for the analysis and synthesis of images. We analyze several dilation matrices, and show how the wavelet transform operates visually. We also show some distorsions produced by some of these matrices. We show that the requirement of their eigenvalues being greater than 1 in absolute value is not enough to guarantee their suitability for image processing applications, and discuss other conditions

Automatic video logo detection and removal

10.1007/s00530-005-0167-6Multimedia Systems105379-391MUSY