Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

A broadband leptonic model for gamma-ray emitting microquasars

Observational and theoretical studies point to microquasars (MQs) as possible counterparts of a significant fraction of the unidentified gamma-ray sources detected so far. At present, a proper scenario to explain the emission beyond soft X-rays from these objects is not known, nor what the precise connection is between the radio and the high-energy radiation. We develop a new model where the MQ jet is dynamically dominated by cold protons and radiatively dominated by relativistic leptons. The matter content and power of the jet are both related with the accretion process. The magnetic field is assumed to be close to equipartition, although it is attached to and dominated by the jet matter. For the relativistic particles in the jet, their maximum energy depends on both the acceleration efficiency and the energy losses. The model takes into account the interaction of the relativistic jet particles with the magnetic field and all the photon and matter fields. Such interaction produces significant amounts of radiation from radio to very high energies through synchrotron, relativistic Bremsstrahlung, and inverse Compton (IC) processes. Variability of the emission produced by changes in the accretion process (e.g. via orbital eccentricity) is also expected. The effects of the gamma-ray absorption by the external photon fields on the gamma-ray spectrum have been taken into account, revealing clear spectral features that might be observed. This model is consistent to the accretion scenario, energy conservation laws, and current observational knowledge, and can provide deeper physical information of the source when tested against multiwavelength data.