Patent
Image recognition method using two-dimensional stochastic grammars
TLDR
In this article, a method of automatically identifying bitmapped image objects is presented, where each of a set of templates in an object template library is compared with all areas of like size of a bit mapped image and the set of signals generated for each such comparison that satisfies a defined matching criteria between the template and the image area being compared.Abstract:
A method of automatically identifying bitmapped image objects. Each of a set of templates in an object template library is compared with all areas of like size of a bitmapped image. A set of signals is generated for each such comparison that satisfies a defined matching criteria between the template and the image area being compared. The set of signals identifies the object based on the matching template, the location of the object in the image and an indication of the goodness of the match between the object and the template. A series of possible parse trees are formed that describe the image with a probability of occurrence for each tree. Each parent node and its child nodes of each parse tree satisfies a grammatical production rule in which some of the production rules define spatial relationships between objects in the image. The one of the possible parse trees which has the largest probability of occurence is selected for further utilization.read more
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References
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Patent
Means for resolving ambiguities in text based upon character context
TL;DR: A method of identifying an object within a set of object candidates includes the steps of: calculating the probability of occurrence of each member of each candidate, wherein each candidate contains one member of the set of candidate candidates, and employing formulae using a method of groups and projections; and identifying one of the objects based on the calculated probability as discussed by the authors.
Patent
Multiple-parts-of-speech disambiguating method and apparatus for machine translation system
Eri Okamoto,Atsushi Okajima +1 more
TL;DR: In this paper, the parts of speech of words capable of functioning as multiple part of speech should be in the inputted sentence are determined in consideration of an array of the parts by applying the multiple-parts-of-speech disambiguating rules.
Stochastic modeling as a means of automatic speech recognition.
TL;DR: A simplified implementation of the DRAGON speech recognition system has been developed using knowledge A and L, and some of the knowledge from S, where A,L,P,S represent the acoustic-phonetic, lexical, phonological, and syntactic-semantic knowledge.