The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and t...
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ISBN:
(纸本)0819440809
The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and the system performance. Multi-agent architecture can be designed in order to respect the first two efficiency constraints. As for third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first The need for such system and its parameters is explained. A brief review of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. After a brief introduction to genetic algorithms, these are adapted to the particularity of this study. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.
Omnidirectional visual sensors have been successfully introduced recently to robot navigation, providing improved localization performances and a more stable path following behavior. As a consequence of the sensor cha...
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ISBN:
(纸本)0819440809
Omnidirectional visual sensors have been successfully introduced recently to robot navigation, providing improved localization performances and a more stable path following behavior. As a consequence of the sensor characteristics, occlusion of the entire panoramic visual field becomes very unlikely. The presented work exploits these characteristics providing a Bayesian framework to gain even partial evidence about a current location by applying decision fusion on the multi-directional visual context. The panoramic image is first partitioned into a fixed number of overlapping unidirectional camera views, i,e., appearance sectors. For each sector image one learns then a posterior distribution over potential locations within a predefined environment. The ambiguity in a local sector interpretation is then resolved by Bayesian reasoning over the spatial context of the current position, discriminating occlusions which do not fit to the appearance model of subsequent sector views. The results from navigation experiments in an office using a robot equipped with an omnidirectional camera demonstrate that the Bayesian reasoning allows highly occlusion tolerant localization to enable visual navigation of autonomous robots even at crowded places such as offices, factories, and urban environments.
This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different select...
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ISBN:
(纸本)0819440809
This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different selectivity, the outputs of which are fused together with an artificial neural network in order to recognize various human-determined odors. The basic idea is to ground human-provided linguistic descriptions of these odors in the actual sensory perceptions of the nose through a process of supervised learning. Analogous to the human nose, the paper explains a method by which an electronic nose can be used for substance identification. First, the receptors of the nose are exposed to a substance by means of inhalation with an electric pump. Then a chemical reaction takes place in the gas sensors over a period of time and an artificial neural network processes the resulting sensor patterns. This network was trained to recognize a basic set of "pure" substances such as "vanilla", "lavender" and "yogurt" under controlled laboratory conditions. The complete system was then validated through a series of experiments on various combinations of the basic substances. First, we showed that the nose was able to consistently recognize unseen samples of the same substances on which it had been trained. In addition, we present some first results where the nose was tested on novel combinations of substances on which it had not been trained by combining the learned descriptions - for example, it could distinguish "lavender yogurt" as a combination of "lavender" and "yogurt".
In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time-consuming. The challenge then has been to develop a general and close to rea...
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ISBN:
(纸本)0819440809
In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time-consuming. The challenge then has been to develop a general and close to real-time road extraction method. This study falls in this perspective and aims at developing a close to real-time semi-automatic system able to extract linear planimetric features (including roads). The major concern of the study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi-resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A* algorithm First, a cubic natural splines interpolation of the operator points is used to parameterize the A* algorithm cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for the selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A* algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images).
The collection and management of vast quantities of meteorological data, including satellite-based as well as ground-based measurements, is presenting great challenges in the optimal usage of this information. To addr...
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The collection and management of vast quantities of meteorological data, including satellite-based as well as ground-based measurements, is presenting great challenges in the optimal usage of this information. To address these issues, the Army Research Laboratory has developed neural networks for combining multisensor meteorological data for Army battlefield weather forecasting models. As a demonstration of this data fusion methodology, multi-sensor data was taken from the Meteorological Measurement Set Profiler (MMSP-POC) system and from satellites with orbits coinciding with the geographical locations of interest. The MMS Profiler-POC comprises a suite of remote sensing instrumentation and surface measuring devices. Neural network techniques were used to retrieve temperature and wind information from a combination of polar orbiter and/or geostationary satellite observations and ground-based measurements. Back-propagation neural networks were constructed which used satellite radiances, simulated microwave radiometer measurements, and other ground-based measurements as inputs and produced temperature and wind profiles as outputs. The network was trained with rawinsonde measurements used as truth-values. The final outcome will be an integrated, merged temperature/wind profile from the surface up to the upper troposphere.
This Volume 3376 of the conference proceedings contains 22 papers. Topics discussed include sensorfusion, sensorfusionarchitectures, feature and decision level fusion, data and image level fusion and sensorfusion ...
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This Volume 3376 of the conference proceedings contains 22 papers. Topics discussed include sensorfusion, sensorfusionarchitectures, feature and decision level fusion, data and image level fusion and sensorfusionalgorithms.
The report will highlight the final results of an Advanced Technology Demonstration effort for an enhanced all source fusion (EASF) system recently developed at the fusion Technology Branch, Air Force Research Laborat...
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ISBN:
(纸本)0819436771
The report will highlight the final results of an Advanced Technology Demonstration effort for an enhanced all source fusion (EASF) system recently developed at the fusion Technology Branch, Air Force Research Laboratory/IFEA. It will describe an innovative approach of traditional fusionalgorithms and heuristic reasoning techniques to significantly improve the detection, identification, location and tracking of mobile red, blue and gray components of the electronic environment.
Information fusion includes the integration of feature data, expert knowledge, and algorithms. For example, in automatic target recognition (ATR) features of size, color, and motion can be fused to assess the combinat...
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ISBN:
(纸本)0819436771
Information fusion includes the integration of feature data, expert knowledge, and algorithms. For example, in automatic target recognition (ATR) features of size, color, and motion can be fused to assess the combination of multi-modal information. A neurofuzzy fusion of features captures the multilevel language content of sensory information by fusing neural network data analysis with rule-based decision making. Additionally, the neurofuzzy architecture can effectively fuse coarse and fine abstracted feature data at the content level for decision making. In this paper, we investigate a multilevel neuro-fuzzy feature-based architecture for synthetic aperture radar (SAR) target recognition.
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