A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image, in terms of a specified descriptive language, that is simplest in the sense of being shortest. We show that a descriptive language limited to a low-order polynomial description of the intensity variation within each region and a chain-code-like description of the region boundaries yields intuitively satisfying partitions for a wide class of images. The advantage of this formulation is that it can be extended to deal with subsequent steps of the image-understanding problem (or to deal with other image attributes, such as texture) in a natural way by augmenting the descriptive language. Experiments performed on a variety of both real and synthetic images demonstrate the superior performance of this approach over partitioning techniques based on clustering vectors of local image attributes and standard edge-detection techniques. 1 Introduction The partitioning proble...

Rigid motion imposes constraints on the motion of image points between the two images. The matched points must conform to one of several possible constraints, such as that given by the fundamental matrix or image-image homography, and it is essential to know which model to fit to the data before recovery of structure, matching or segmentation can be performed successfully. This paper compares several model selection methods with a particular emphasis on providing a method that will work fully automatically on real imagery. 1 Introduction Robotic vision has its basis in geometric modelling of the world, and many vision algorithms attempt to estimate these geometric models from perceived data. Usually only one model is fitted to the data. But what if the data might have arisen from one of several possible models? In this case the fitting procedure needs to fit all the potential models and select which of these fits the data best. This is the task of robust model selection which, in spi...

We describe a simple, fast and easy to implement recursive algorithm with four alternate intuitive heuristics for inferring Probabilistic Finite State Automata. The algorithm is an extension for stochastic machines of the k-tails method introduced in 1972 by Biermann and Feldman for non-stochastic machines. Experiments comparing the two are done and benchmark results are also presented. It is also shown that sk-strings performs better than k-tails at least in inferring small automata. Introduction When given a finite number of examples of the behaviour of a probabilistic state determined machine, it is possible to imagine methods by which we can infer its structure. Ideally, we would like to identify the exact automaton which generated the strings. But it is impossible to do this from the behaviour of the machine because more than one non-minimal machine may generate the same language. This paper is concerned not with identifing the generating machine, which is demonstratably impossib...

We present a new method for the detection of multiple solutions or degeneracy when estimating the Fundamental Matrix, with specific emphasis on robustness to data contamination (mismatches). The Fundamental Matrix encapsulates all the information on camera motion and internal parameters available from image feature correspondences between two views. It is often used as a first step in structure from motion algorithms. If the set of correspondences is degenerate, then this structure cannot be accurately recovered and many solutions explain the data equally well. It is essential that we are alerted to such eventualities. As current feature matchers are very prone to mismatching the degeneracy detection method must also be robust to outliers. In this paper a definition of degeneracy is given and all two view non-degenerate and degenerate cases are catalogued in a logical way by introducing the language of varieties from algebraic geometry. It is then shown how each of the cases can be ro...

This paper examines the minimumencoding approaches to inference, Minimum Message Length (MML) and Minimum Description Length (MDL). This paper was written with the objective of providing an introduction to this area for statisticians. We describe coding techniques for data, and examine how these techniques can be applied to perform inference and model selection.

Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate re ections of the original models and compare favorably with those of TETRAD II (Spirtes et al. 1994) even when it is supplied with prior temporal information and MML is not. 1

A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoauge its communications amongst the typical communications of the network. We study the task of detecting such hidden groups given only the history of the communications for the entire communication network. We develop a probabilistic approach using a Hidden Markov model of the communication network. Our approach does not require the use of any semantic information regarding the communications.

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In this paper, we propose a unified optimization framework for feature extraction that lets us simultaneously take into account image data and semantic knowledge: We model objects using a language that specifies both photometric and geometric constraints and define an information-theoretic objective function that measures the fit of the models to the data. We then treat the problem of finding objects as one of generating the optimal description of the image in terms of this language.

This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multi-scale preprocessing step, which generates junction candidates with associated regions of interest. These junction features are matched to edges based on spatial coincidence. For each matched pair, a tentative break point is introduced at the edge point closest to the junction. Finally, these feature combinations serve as input for an MDL approximation method which tests the validity of the break point hypotheses and classifies the resulting edge segments as either "straight " or "curved". Experiments on real world image data demonstrate the viability of the approach.