Motion estimation with object based regularisation

A dynamic programming based matching method for motion estimation, that optimises a Bayesian maximum likelihood function in a 3-D optimisation space, is presented. The Bayesian function consists of a matching cost and an object based 2-D regularisation cost. The method gives results more accurate than block-based matching since the motion boundaries are close to the actual object boundaries

Few-to-few Cross-domain Object Matching

Cross-domain object matching refers to the task of inferring unknown alignment between objects in two data collections that do not have a shared data representation. In recent years several methods have been proposed for solving the special case that assumes each object is to be paired with exactly one object, resulting in a constrained optimization problem over permutations. A related problem formulation of cluster matching seeks to match a cluster of objects in one data set to a cluster of objects in the other set, which can be considered as many-to-many extension of cross-domain object matching and can be solved without explicit constraints. In this work we study the intermediate region between these two special cases, presenting a range of Bayesian inference algorithms that work alsofor few-to-few cross-domain object matching problems where constrained optimization is necessary but the optimization domain is broader than just permutations.Peer reviewe

Models and methods for Bayesian object matching

This thesis is concerned with a central aspect of computer vision, the object matching problem. In object matching the aim is to detect and precisely localize instances of a known object class in a novel image. Factors complicating the problem include the internal variability of object classes and external factors such as rotation, occlusion, and scale changes. In this thesis, the problem is approached from the feature-based point of view, in which objects are considered to consist of certain pertinent features, which are then located in the perceived image. The methodological framework applied in this thesis is probabilistic Bayesian inference. Bayesian inference is a branch of statistics which assigns a great role to the mathematical modeling of uncertainty. After describing the basics of Bayesian statistics the object matching problem problem is formulated as a Bayesian probability model and it is shown how certain necessary sampling algorithms can be applied to analyze the resulting probability distributions. The Bayesian approach to the problem partitions it naturally into two submodels; a feature appearance model and an object shape model. In this thesis, feature appearance is modeled statistically via a type of bandpass filters known as Gabor filters, whereas two different shape models are presented: a simpler hierarchical model with uncorrelated feature location variations, and a full covariance model containing the interdependeces of the features. Furthermore, a novel model for the dynamics of object shape changes is introduced. The most important contributions of this thesis are the proposed extensions to the basic matching model. It is demonstrated how it is very straightforward to adjust the Bayesian probability model when difficulties such as scale changes, occlusions and multiple object instances arise. The changes required to the sampling algorithms and their applicability to the changed conditions are also discussed. The matching performance of the proposed system is tested with different datasets, and capabilities of the extended model in adverse conditions are demonstrated. The results indicate that the proposed model is a viable alternative to object matching, with performance equal or superior to existing approaches.reviewe

Probabilistic Cross-Identification of Astronomical Sources

We present a general probabilistic formalism for cross-identifying astronomical point sources in multiple observations. Our Bayesian approach, symmetric in all observations, is the foundation of a unified framework for object matching, where not only spatial information, but physical properties, such as colors, redshift and luminosity, can also be considered in a natural way. We provide a practical recipe to implement an efficient recursive algorithm to evaluate the Bayes factor over a set of catalogs with known circular errors in positions. This new methodology is crucial for studies leveraging the synergy of today's multi-wavelength observations and to enter the time-domain science of the upcoming survey telescopes.Comment: Accepted for publication in the Astrophysical Journal, 8 pages, 1 figure, emulateapj w/ apjfont

A hierarchical Bayesian approach to record linkage and population size problems

We propose and illustrate a hierarchical Bayesian approach for matching statistical records observed on different occasions. We show how this model can be profitably adopted both in record linkage problems and in capture--recapture setups, where the size of a finite population is the real object of interest. There are at least two important differences between the proposed model-based approach and the current practice in record linkage. First, the statistical model is built up on the actually observed categorical variables and no reduction (to 0--1 comparisons) of the available information takes place. Second, the hierarchical structure of the model allows a two-way propagation of the uncertainty between the parameter estimation step and the matching procedure so that no plug-in estimates are used and the correct uncertainty is accounted for both in estimating the population size and in performing the record linkage. We illustrate and motivate our proposal through a real data example and simulations.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS447 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Dissociating the effect of disruptive colouration on localisation and identification of camouflaged targets

Disruptive camouflage features contrasting areas of pigmentation across the animals’ surface that form false edges which disguise the shape of the body and impede detection. In many taxa these false edges feature local contrast enhancement or edge enhancement, light areas have lighter edges and dark areas have darker edges. This additional quality is often overlooked in existing research. Here we ask whether disruptive camouflage can have benefits above and beyond concealing location. Using a novel paradigm, we dissociate the time courses of localisation and identification of a target in a single experiment. We measured the display times required for a stimulus to be located or identified (the critical duration). Targets featured either uniform, disruptive or edge enhanced disruptive colouration. Critical durations were longer for identifying targets with edge enhanced disruptive colouration camouflage even when presented against a contrasting background, such that all target types were located equally quickly. For the first time, we establish empirically that disruptive camouflage not only conceals location, but also disguises identity. This shows that this form of camouflage can be useful even when animals are not hidden. Our findings offer insights into how edge enhanced disruptive colouration undermines visual perception by disrupting object recognition