By James J. Clark, Alan L. Yuille
The technological know-how linked to the advance of man-made sen sory platforms is occupied essentially with choosing how information regarding the realm could be extracted from sensory info. for instance, computational imaginative and prescient is, for the main half, involved in the de velopment of algorithms for distilling information regarding the area and popularity of varied gadgets within the environ (e. g. localization ment) from visible photographs (e. g. photos or video frames). There are frequently a mess of the way within which a selected piece of informa tion in regards to the global could be acquired from sensory info. A subarea of study into sensory structures has arisen that is serious about tools for combining those quite a few details resources. This box is named info fusion, or sensor fusion. The literature on information fusion is large, indicating the serious curiosity during this subject, yet is kind of chaotic. There are not any authorised techniques, retailer for a number of precise instances, and plenty of of the simplest equipment are advert hoc. This booklet represents our try at delivering a mathematical beginning upon which information fusion algorithms could be built and analyzed. The technique that we found in this article is mo tivated via a powerful trust within the significance of constraints in sensory details processing structures. In our view, info fusion is better un derstood because the embedding of a number of constraints at the strategy to a sensory info processing challenge into the answer seasoned cess.
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Extra resources for Data Fusion for Sensory Information Processing Systems
For example, for an assembly robot looking for a screwdriver in a field of flowers, it is safe to misinterpret the flowers as engine gaskets, as neither interpretation is of importance to the robot's activity. This can lead to trouble, of course, if the the robot misinterprets irrelevant objects as objects of importance to its activities. For example, some birds have been known to treat strange objects, such as bottles and rocks, that have been put into their nests by mischievous scientist, as their own eggs.
32 DATA FUSION Bayes theorem is most useful in the framework of subjective probabilities, and it is for this reason that many statisticians object to the use of Bayesian methods. It is because of the difficulty in specifying the a priori constraints and even the image formation model that some statisticians consider Bayesian methods to be useless. What we have been calling the image formation model is generally assumed to be objectively determined, but in practice is usually based on subjective opinion, although these opinions may be generally regarded as valid.
For example, consider the space F to be composed of apples, oranges and bananas. We can construct a conditional density which expresses the probability that we are sensing the various elements of F given our sensory data, but it makes no sense to talk about the mean or variance of this conditional density. In the situations where the variance of the a priori distribution does not exist, we must use estimators other than the conditional mean or minimum variance estimator. The MAP estimate will always be defined, but other estimators based on "pseudo-variance" measures may be used.
Data Fusion for Sensory Information Processing Systems by James J. Clark, Alan L. Yuille