Method for detection optimization in image-based decision systems
OwnerShih-Jong J. Lee, Chi-Chou Huang +1inventors
InventorsShih-Jong J. Lee, Chi-Chou Huang, Seho Oh
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Abstract
A systematic way of linking structure constraints of components of a common object and checking and resolving their inconsistency is used to improve detection results in image-based decision systems. A multilevel Chain-And-Tree (CAT) model is used to direct processing using both forward and backward scans through the related components. Since components occur as parts of an object, the context (relational structure) in which the component appears can be used to reduce noise and variation affects. In the method, object knowledge is translated into constraints between components. The constraints are used to enhance feature detection, defect detection, and measurement accuracy and consistency.
Claims9
A detection optimization method using a multilevel Chain-And-Tree model comprises the following steps:
- (a) Performing a forward optimization scan initialization using the root node;
- (b) Performing a forward optimization scan for each node using the detections and relations from its preceding nodes having at least one forward optimized result output;
- (c) Performing a backward optimization scan initialization using the end node;
- (d) Performing a backward optimization scan for each node using the detections and relations from its descending nodes having at Icast one backward optimized result output.
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