KBVision Constraint System Module Page 1

KBVision Constraint Module


Overview

The Constraint System is a general purpose interactive tool that is used to create discriminative functions whose inputs are values of extracted Features, and whose outputs are levels of confidence in the existence of some object. Confidence levels can be used for:


What Is A Constraint?

A Constraint is a mathematical function that defines the mapping from one or more Token feature values (attributes) to a Constraint score. A Constraint Set is simply a set of Constraints and a Property List.

Constraint scores are generally used to numerically symbolize the presence and magnitude of characteristics of domain objects. Often, this becomes a measure of evidence or confidence that a particular object is present at the Token(s) location, and can be used to classify Tokens, or act as initial hypothesis values for later processing.

Once a Constraint has been displayed for a Tokenset, a Constraint score for every Token is temporarily included as part of the Tokenset as a Token feature. The Token feature name is identical to the Constraint name, and the Token feature value is the constraint score.

The option exists in the Constraint System to save the Tokenset with the newly-defined Constraint-based features. In addition, once the Constraint results become part of the Tokenset, they can be used as features to form other Constraints. This Constraint hierarchy is possible whether the Tokenset is saved with the Constraint scores or not.


Types of Constraints


As an aid in the construction of Constraints that directly map feature values to scores, (Primitive and Lookup), it is possible to manually create Tokensets that can be included in the histogram display. This is accomplished by explicitly selecting Tokens from a display of the outlines of all Tokens in the tokenset of the Image representation. Each of these Tokensets has an associated display color. When a histogram is displayed, bins representing Tokens from a Tokensubset will be displayed in the Tokensubset color. This display is very useful in extracting initial feature correlations for objects of interest.

It is also possible to display a combined histogram of more than one Tokenset. This is especially valuable to ensure that Constraints do not become too specialized, such that they describe only the specific appearance of an object in a single Image.


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