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Additional info for 43.Pattern Analysis and Machine Intelligence
The reasoning is still based on the presented algorithm. Now it turns out that just one run of the algorithm sufﬁces to complete a consistent mental view of the observations (assuming that the set of observations can be matched with the feature model). It needs to be noted that the variable “BIRD” is just an ordinary input signal (perhaps from an auditory or text analysis subsystem). However, this input seems to contribute very much in θ1 , and the whole category may be labeled accordingly. What is important is that there are a plenty of other connections to other signals, so that this computational “bird concept” does not remain empty — it has meaning or default connotations in this context.
28. J. A. Ericsson andJ. ), Towards a General Theory of Expertise. New York: Cambridge University Press, 1991. 29. H. ﬁ/hyotyniemi/publications. 30. H. Helsinki University of Technology, Control Engineering Laboratory, 2006. 31. A. Jackson, Connectionism and Meaning: From Truth Conditions to Weight Representations. New Jersey: Ablex Publishing, 1996. 32. C. Jutten and J. Herault, Blind separation of sources, part 1: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24: 1–10, 1991.
However, if characters touch a line, as is often the case in tables and annotated line-drawings, the characters have to be segmented from the lines. One technique for segmenting characters from line structures is to determine the high neighborhood line density (NLD) areas in the line structures. The following is an example of the type of rules that can be used. if then if then connected component size is larger than a threshold the connected component is a figure (with likelihood Li) neighborhood line density (NLD) of a figure is high the high NLD area is a character area (with likelihood Lj) In certain document segmentation tasks it is only necessary to extract a given block of interest.