Francisco J. Cortijo

Image restoration in astronomy: a Bayesian perspective

When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise models and on the algorithms to find the restoration. Topics like parameter estimation and stopping rules are also commented on. We start by describing the Bayesian paradigm and then proceed to study the noise and blur models used by the astronomical community. Then the prior models used to restore astronomical images are examined. We describe the algorithms used to find the restoration for the most common combinations of degradation and image models. Then we comment on important issues such as acceleration of algorithms, stopping rules, and parameter estimation. We also comment on the huge amount of information available to, and made available by, the astronomical community.

A dynamic approach for clustering data

Abstract This paper introduces a new method for clustering data using a dynamic scheme. An appropriate partitioning is obtained based on both a dissimilarity measure between pairs of entities as well as a dynamic procedure of splitting. A dissimilarity function is defined by using the cost of the optimum path from a datum to each entity on a graph, with the cost of a path being defined as the greatest distance between two successive vertices on the path. The procedure of clustering is dynamic in the sense that the initial problem of determining a partition into an unknown number of natural groupings has been reduced to a sequence of only two class splitting stages. Having arisen from any particular application, the proposed approach could be effective for many domains, and it is especially successful to identify clusters if there is lack of prior knowledge about the data set. The usefulness of the dynamic algorithm to deal with elongated or non-piecewise linear separable clusters as well as sparse and dense groupings is demonstrated with several data sets.

Bayesian super-resolution of text image sequences from low resolution observations

This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of under-sampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.

On the Bayesian deconvolution of planets

Considers Bayesian methods and spatial stochastic processes applied to the deconvolution of images of planets. Under simple but realistic prior assumptions about the true underlying image of a planet the Bayesian framework is put to work. The method has been tested on CCD images of Jupiter.<<ETX>>

TMiner aspects: Crosscutting concerns in the TMiner component-based data mining framework

TMiner (Berzal, Cubero, & Jimenez, 2009) is a component-based data mining framework that has been designed to support the whole KDD process and facilitate the implementation of complex data mining scenarios. This paper shows how aspect-oriented programming techniques support some tasks whose implementation using conventional object-oriented programming would be extremely time-consuming and error-prone. In particular, we have successfully employed aspects in TMiner to evaluate and monitor the I/O performance of alternative data mining techniques. Without having to modify the source code of the system under analysis, aspects provide an unintrusive mechanism to perform this kind of performance analysis. In fact, aspects let us probe a system implementation so that we can identify potential bottlenecks, detect redundant computations, and characterize system behavior+lessons learned during the development of TMiner.

The ModelCC Model-Driven Parser Generator.

Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.

Fencing the Lamb: A Chart Parser for ModelCC

Traditional grammar-driven language specification techniques constrain language designers to specific kinds of grammars. In contrast, model-based language specification techniques decouple language design from language processing. They allow the occurrence of ambiguities and the declarative specification of constraints for solving them. As a result, these techniques require general parser generators, which should be able to parse context-free grammars, handle ambiguities, and enforce constraints to disambiguate them as desired by the language designer. In this paper, we describe Fence, a bottom-up chart parser with lexical and syntactic ambiguity support. Fence accepts lexical analysis graphs outputted by the Lamb (Lexical AMBiguity) lexer and efficiently resolves ambiguities by means of the declarative specification of constraints. Both Lamb and Fence are part of the ModelCC model-based parser generator.