The basic limitations of the standard appearance-based matching methods using eigenimages are nonrobust estimation of coefficients and inability to cope with problems related to outliers, occlusions, and varying background. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way the coefficients of the eigenimages are d...

We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships between scene parts. Our approach does not rely on any strong assumptions about the object or the scene to aid in this segmentation into layers. In general, the problem of buildin...

An instance crucial to most problems in signal processing is the selection of the order of a presupposed model. Examples are the determination of the putative number of signals present in white Gaussian noise or the number of noise-contaminated sources impinging on a passive sensor array. It is shown that Maximum a Posteriori Bayesian arguments, coupled with Maximum Entropy considerations, offe...

In many situations there exists an abundance of positive examples, but only a handful of negatives. In this paper we show how in binary or transaction data such rare cases can be identified and characterised. Our approach uses the Minimum Description Length principle to decide whether an instance is drawn from the training distribution or not. By using frequent itemsets to construct this compre...

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This tutorial provides an overview of and introduction to Rissanen’s Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the first chapter are made mathematically precise. This tutorial will appear as the ...

We introduce the minimum description length (MDL) principle, a general principle for inductive inference based on the idea that regularities (laws) underlying data can always be used to compress data. We introduce the fundamental concept of MDL, called the stochastic complexity, and we show how it can be used for model selection. We briefly compare MDL-based model selection to other approaches ...

Common approximations for the minimum description length (MDL) criterion imply that the cost of adding a parameter to a model fit to n observations is about (1/2) log n bits. While effective for parameters which are large on a standardized scale, this approximation overstates the parameter cost near zero. A uniform approximation and local asymptotic argument show that the addition of a small pa...

The Minimum Description Length (MDL) approach to shape modelling is reviewed. It solves the point correspondence problem of selecting points on shapes defined as curves so that the points correspond across a data set. An efficient numerical implementation is presented and made available as open source Matlab code. The problems with the early MDL approaches are discussed. Finally the MDL approac...

This paper presents a new information theoretic framework for aligning sequences in bioinformatics. A transmitter compresses a set of sequences by constructing a regular expression that describes the regions of similarity in the sequences. To retrieve the original set of sequences, a receiver generates all strings that match the expression. An alignment algorithm uses minimum description length...