METHOD FOR PARTITIONING A PATTERN INTO OPTIMIZED SUB-PATTERNS

• US 20120155768A1
• Filed: 12/19/2011
• Published: 06/21/2012
• Est. Priority Date: 07/22/2003
• Status: Active Grant

• Current Assignee: Cognex Technology & Investment Corporation (Cognex Corporation)
• Original Assignee: Cognex Technology & Investment Corporation (Cognex Corporation)
• Inventors: Davis, Jason, Koljonen, Juha
• Granted Patent: US 9,147,252 B2
• Time in Patent Office: Days
• US Class Current: 382/173
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/162 involving graph-based methods
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/426 Graphical representations

Abstract

A method is provided for dividing a pattern into a plurality of sub-patterns, each sub-pattern being adapted for use with an image search method that can provide a plurality of sub-pattern search results. The method represents the pattern as a plurality of feature points, generates candidate partitions of the plurality of feature points, and then scores the candidate partitions by examining characteristics of each potential sub-pattern of each candidate partition. The highest-scoring partition is selected, and then it is applied to the plurality of feature points, creating one or more sub-pluralities of features. The invention advantageously provides a plurality of sub-patterns where each sub-pattern contains enough information to be located with a feature-based search method, where that information has been pre-evaluated as being useful and particularly adapted for running feature-based searches.

Claims

1-48. -48. (canceled)

49. A method for partitioning a pattern into optimized sub-patterns, the method comprising:

providing a list of features of the pattern;

generating a set of candidate partitions using the list of features of the pattern'"'"'

scoring each candidate partition of the set of candidate partitions by building sub-patterns using the set of candidate partitions, wherein the scoring includes analyzing degeneracy;

determining a best-scoring partition among the set of candidate partitions;

applying the best-scoring partition to the list of features so as to provide a plurality of sub-lists of features respectively representing a plurality of optimized sub-patterns.