Fabio Gagliardi Cozman

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.

Depth from scattering

Light power is affected when it crosses the atmosphere; there is a simple, albeit non-linear, relationship between the radiance of an image at any given wavelength and the distance between object and viewer. This phenomenon is called atmospheric scattering and has been extensively studied by physicists and meteorologists. We present the first analysis of this phenomenon from an image understanding perspective: we investigate a group of techniques for extraction of depth cues solely from the analysis of atmospheric scattering effects in images. Depth from scattering techniques are discussed for indoor and outdoor environments, and experimental tests with real images are presented. We have found that depth cues in outdoor scenes can be recovered with surprising accuracy and can be used as an additional information source for autonomous vehicles.

Experience with rover navigation for lunar-like terrains

Reliable navigation is critical for a lunar rover, both for autonomous traverses and safeguarded remote teleoperation. This paper describes an implemented system that has autonomously driven a prototype wheeled lunar rover over a kilometer in natural, outdoor terrain. The navigation system uses stereo terrain maps to perform local obstacle avoidance, and arbitrates steering recommendations from both the user and the rover. The paper describes the system architecture, each of the major components, and the experimental results to date.

Graphical models for imprecise probabilities

This paper presents an overview of graphical models that can handle imprecision in probability values. The paper first reviews basic concepts and presents a brief historical account of the field. The main characteristics of the credal network model are then discussed, as this model has received considerable attention in the literature.

Robot localization using a computer vision sextant

This paper explores the possibility of using Sun altitude for localization of a robot in totally unknown territory. A set of Sun altitudes is obtained by processing a sequence of time-indexed images of the sky. Each altitude constrains the viewer to a circle on the surface of a celestial body, called the circle of equal altitude. A set of circles of equal altitude can be intersected to yield viewer position. We use this principle to obtain the position on Earth. Since altitude measurements are corrupted by noise, a least-square estimate is numerically calculated from the sequence of altitudes. The paper discusses the necessary theory for Sun-based localization, the technical issues of camera calibration and image processing, and presents preliminary results with real data.

Graphoid properties of epistemic irrelevance and independence

This paper investigates Walleys concepts of epistemic irrelevance and epistemic independence for imprecise probability models. We study the mathematical properties of irrelevance and independence, and their relation to the graphoid axioms. Examples are given to show that epistemic irrelevance can violate the symmetry, contraction and intersection axioms, that epistemic independence can violate contraction and intersection, and that this accords with informal notions of irrelevance and independence.

Outdoor Visual Position Estimation for Planetary Rovers

This paper describes (1) a novel, effective algorithm for outdoor visual position estimation; (2) the implementation of this algorithm in the Viper system; and (3) the extensive tests that have demonstrated the superior accuracy and speed of the algorithm. The Viper system (Visual Position Estimator for Rovers) is geared towards robotic space missions, and the central purpose of the system is to increase the situational awareness of a rover operator by presenting accurate position estimates. The system has been extensively tested with terrestrial and lunar imagery, in terrains ranging from moderate—the rounded hills of Pittsburgh and the high deserts of Chile—to rugged—the dramatic relief of the Apollo 17 landing site—to extreme—the jagged peaks of the Rockies. Results have consistently demonstrated that the visual estimation algorithm estimates position with an accuracy and reliability that greatly surpass previous work.

Editorial: Reasoning with imprecise probabilities

This special issue of the International Journal of Approximate Reasoning (IJAR) grew out of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA’05), held in Pittsburgh, USA, in July 2005 (http://www.sipta.org/isipta05). The symposium was organized by Teddy Seidenfeld, Robert Nau, and Fabio G. Cozman, and brought together researchers from various branches interested in imprecision in probabilities. Research in artificial intelligence, economics, engineering, psychology, philosophy, statistics, and other fields was presented at the meeting, in a lively atmosphere that fostered communication and debate. Invited talks by Isaac Levi and Arthur Dempster enlightened the attendants, while tutorials by Gert de Cooman, Paolo Vicig, and Kurt Weichselberger introduced basic (and advanced) concepts; finally, the symposium ended with a workshop on financial risk assessment, organized by Teddy Seidenfeld. The ISIPTA series started in 1999; the first one was held in Ghent, Belgium – followed by symposia held in Cornell, USA (in 2001), in Lugano, Switzerland (in 2003), and in Pittsburgh, USA (in 2005). The next edition of this biennial event will take place in Prague, Czech Republic, in July 2007 (http://www.sipta.org/isipta07). Selected papers from the first three symposia appeared in special issues of IJAR in 2000 and 2005, in a special issue of Risk, Decision and Policy in 2000, and in a special issue of Annals of Mathematics and Artificial Intelligence in 2005. This special issue of IJAR contains ten articles; the first eight of them are revised versions of selected papers from ISIPTA’05. The first four papers deal with independence and graphical models; they are followed by two papers on probabilistic logic, and by two papers on decision–theoretic and combinatorial results. We close this special issue with a very special treat – the publication of Peter Williams’ essay Notes on Coherent Previsions, a fundamental paper that appeared in 1975 as a technical report, and that has widely circulated since then. ISIPTA’05 marked the 30th anniversary of this paper, and we were fortunate to obtain a revised version from its author for this special issue. Williams’ essay deals with foundations of probability, and addresses many profound questions that are basic to reasoning under uncertainty. The paper requires substantial background; for this reason, it is preceded by a short paper by Vicig, Zaffalon, and Cozman. This short paper offers commentary and guidance on Williams’ influential work.

Position estimation from outdoor visual landmarks for teleoperation of lunar rovers

The paper presents a new application of computer vision to space robotics: a teleoperation interface which analyzes images sent by a mobile robot in space missions and produces position estimates based an the images. The estimates are displayed to the robot operator as additional information to prevent loss of orientation. The current version of the interface detects mountain formations in images and automatically searches for mountain peaks in a given topographic map. A new algorithm for position estimation uses a statistical description of the various disturbances and signals in the measurement process to produce estimates. The authors have tested the system with real images obtained in the Pittsburgh East and Dromedary Peak USGS quadrangles; they report significant improvements in speed and accuracy compared to previous systems.

Computing lower and upper expectations under epistemic independence

This paper investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated.