By James J. Clark, Alan L. Yuille
The technology linked to the improvement of synthetic sen sory structures is occupied basically with settling on how information regarding the area might be extracted from sensory info. for instance, computational imaginative and prescient is, for the main half, interested by the de velopment of algorithms for distilling information regarding the realm and popularity of varied items within the environ (e. g. localization ment) from visible pictures (e. g. photos or video frames). There are frequently a mess of the way during which a particular piece of informa tion in regards to the global might be got from sensory info. A subarea of analysis into sensory platforms has arisen that is occupied with equipment for combining those numerous info resources. This box is named facts fusion, or sensor fusion. The literature on info fusion is huge, indicating the serious curiosity during this subject, yet is kind of chaotic. There aren't any authorized methods, shop for a couple of specific circumstances, and plenty of of the simplest equipment are advert hoc. This publication represents our try at supplying a mathematical beginning upon which information fusion algorithms may be built and analyzed. The method that we found in this article is mo tivated through a powerful trust within the value of constraints in sensory info processing structures. In our view, information 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 info for Data Fusion for Sensory Information Processing Systems
P(Y) is the a priori probability of the particular image. It is. a normalization factor which can be determined from Prob(YIX) and P(X). In this framework the visual system needs to make assumptions to specify the a priori probability (P(X» and the model of reflectance (P(XIY». Once these probabilities are determined the system can 19 BAYESIAN INFORMATION PROCESSING use Bayes rule to determine the probability of there being an apple given the image. In the general sensory information processing case we will have a set of measurements, or data, from our sensing elements.
In these cases one would like to reduce the dependence of a sensory information processing algorithm on these constraints if possible. One way of doing this is to use information from independent sensory information processing modules as constraints on a given module. This data fusion, then, is a means for reducing dependence of possibly invalid a priori constraints. Chapter 2 Bayesian Sensory Inforlllation Processing The introductory chapter addressed the need for the application of constraints, both natural and artificial, to aid in the performance of sensory information processing tasks.
Hence, if we model the uncertainties in both the reflectance model and in the sensor noise model with a Gaussian distribution (probably not a very good model, but it will be difficult to come up with anything else that is really useful) then the resulting image formation conditional distribution will be Gaussian as well. dl To finish our example, let us assume a Lambertian reflectance law, a single light source at infinity of unit intensity with direction s, point sensors (in space and time), and assume that the same coordinate system is used for both the object and the sensor.