By Isabelle Bloch
The world of knowledge fusion has grown significantly over the last few years, resulting in a quick and bold evolution. In such fast-moving occasions, you will need to take inventory of the adjustments that experience happened. As such, this books deals an outline of the final rules and specificities of data fusion in sign and snapshot processing, in addition to protecting the most numerical tools (probabilistic ways, fuzzy units and hazard thought and trust functions).
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Additional info for Information Fusion in Signal and Image Processing (Digital Signal and Image Processing)
These two elements of information are provided for a set of attributes that can be seen as different explanations of the real situation, and which have to be exploited together. The information characterizing the classes will be referred to as a priori information, since it speciﬁes what we can expect for the values of the attributes, conditionally to each hypothesis, before obtaining an observation. As for the observations (perceptive information), they are measurements of these attributes. This approach is maintained at every information level.
If we compare the problem of image fusion with that of data fusion based on aggregation and multi-criteria optimization, we notice that one of the main differences lies in the fact that for the latter, the goal is to ﬁnd a solution that best satisﬁes a set of generally stringent constraints, whereas in image processing, each source provides (fairly explicitly) a level of satisfaction (for belonging to a category, for example, which can then be considered a criterion) and the decision rather consists of choosing the best one (the best category, for example).
The exclusive use of dead reckoning works through the integration of data using a dynamic model and cannot prevent the estimated trajectory from straying from the actual trajectory. It is therefore necessary to observe the real world at regular intervals, using sensors such as cameras, distance measurements, acoustic or optical barriers, GPS (Global Positioning System) in order to register the estimated trajectory with the real world. 2). The use of an adequate Kalman ﬁlter [CHU 91] provides an optimal estimate of the internal state involving the moving object’s navigation, in the context of stochastic dynamic systems theory [GEL 84, GOP 93].