Selecting a Confusion Threshold and an Acceptance Threshold
CNLSearch uses the confusion threshold and acceptance threshold that you supply to ensure that the correct instance of the model within the search image is located as quickly as possible. Of the two thresholds, the confusion threshold is the more important to obtaining good results from CNLSearch.
CNLSearch uses both the acceptance threshold and the confusion threshold when considering whether or not a match represents a valid instance of the model. The confusion threshold is the score above which any match is guaranteed to be an instance of the model; all matches with scores greater than or equal to the confusion threshold are considered to be valid. The acceptance threshold is the score at or above which the scores of all valid matches will lie. But other matches, which might not be actual instances of the model, can receive scores above the acceptance threshold.
CNLSearch uses the confusion threshold to speed the search process. If you are searching for a single instance of the model in an image, as soon as CNLSearch finds an instance with a score above the confusion threshold, it stops searching and returns the location of the match. If CNLSearch does not find a match with a score above the confusion threshold, it locates all the matches with scores above the acceptance threshold and returns the location of the match with the highest score.
CNLSearch uses the confusion threshold to determine how to go about discriminating among potential instances of the model within the search image. CNLSearch takes the acceptance threshold as an indication of the degree of image degradation it may expect to encounter.
You should set the confusion threshold high enough to ensure that confusing features in a search image do not receive scores above the confusion threshold. Search images with a high degree of confusion contain features that receive high scores even though they are not valid instances of the model. Search images where the only features that receive high scores are valid instances of the model have a low degree of confusion. The figure below shows examples of scenes with low and high degrees of confusion.
Images with low and high degrees of confusion