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

Metacognitive presence in asynchronous online learning environments : self, mutual and hetero-regulation

Peer-reviewed

Removal of chromium (vi) in aqueous environments using cork and heat-treated cork samples from quercus cerris and quercus suber

Chromium (VI) removal and its reduction to chromium (III) from aqueous solution by untreated and heat-treated Quercus cerris and heat-treated Quercus suber black agglomerate cork granules was investigated. Initial screening studies revealed that among the sorbents tested, untreated Q. cerris and Q. suber black agglomerate are the most efficient in the removal of Cr(VI) ions and were selected for adsorption essays. Heat treatment adversely affected chromium adsorption and chromium (VI) reduction in Q. cerris cork. The highest metal uptake was found at pH 3.0 for Q. cerris and pH 2.0 for black agglomerate. The experimental data fitted the Langmuir model and the calculated qmax was 22.98 mg/g in black agglomerate and 21.69 mg/g in untreated Q. cerris cork. The FTIR results indicated that while in black agglomerate, lignin is the sole component responsible for Cr(VI) sorption, and in untreated Q. cerris cork, suberin and polysaccharides also play a significant role on the sorption. The SEM-EDX results imply that chromium has a homogenous distribution within both cork granules. Also, phloemic residues in Q. cerris granules showed higher chromium concentration. The results obtained in this study show that untreated Q. cerris and black agglomerate cork granules can be an effective and economical alternative to more costly materials for the treatment of liquid wastes containing chromium

Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments

Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping