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Extension of dynamic link matching by introducing local linear maps
1Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan.
IEEE Transactions on Neural Networks
|February 6, 2008
Summary
This study introduces local linear maps (LLMs) to enhance dynamic link matching (DLM) for pattern recognition. The new model effectively handles severely deformed patterns, improving adaptability to complex transformations.
Area of Science:
- Computer Vision
- Pattern Recognition
- Image Analysis
Background:
- Dynamic Link Matching (DLM) is a robust pattern matching technique.
- Existing DLM models struggle with severely deformed data where local features lack direct template counterparts.
Purpose of the Study:
- To extend DLM by incorporating Local Linear Maps (LLMs).
- To improve adaptability to significant deformations and nonlinear transformations in pattern matching.
Main Methods:
- Introduced LLMs associated with each lattice point in a data pattern.
- LLMs encode local neighborhood mapping information, transforming local features before matching.
- Neighboring LLMs are coupled to ensure coherent transformations.
Main Results:
- The extended DLM model demonstrates superior flexibility in handling deformations and rotations.
- Successfully matches local features even when severely distorted compared to previous models.
- Numerical demonstrations confirm enhanced performance.
Conclusions:
- The proposed DLM extension with LLMs significantly improves tolerance to data deformation.
- The framework is adaptable to various transformations and can be extended further.
- This approach offers a more robust solution for complex pattern matching tasks.
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