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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall

Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall 2004 Project Kerry Widder. Problem Statement. What: Automatically locate a license plate in the image of a vehicle Who: Law enforcement, parking structures

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Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall

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  1. Automatic License Plate Location Using Template Matching University of Wisconsin - Madison ECE 533 Image Processing Fall 2004 Project Kerry Widder

  2. Problem Statement What: Automatically locate a license plate in the image of a vehicle Who: Law enforcement, parking structures Why: Quicker, cheaper Difficulties: variations – lighting, angle, size, location, distance, color, vehicle features, contrast, holders

  3. Approaches Distance sets (spatial arrangement of features) Morphological operations Contrast levels Contrast transitions – row, column Genetic algorithms Pattern matching

  4. Approach Approach used in this project – template matching Two methods used to measure a template match: 1. Correlation The greater the similarity between the template and the image in a particular location, the greater the value resulting from the correlation. 2. Moment Invariants A set of regional statistical descriptors – invariant to translation, rotation and scale changes.

  5. Implementation Algorithm Filter image (gaussian, then Sobel to find edges) Calculate correlation and correlation on correlation Threshold sobel image to convert to binary Perform Morphological opening to reduce protrusions Find boundaries in binary image Calculate moment invariants of each object and compare to template to find best match

  6. Implementation Data Obtained ten images of vehicles Conditions were controlled: sunny, same distance All images were processed through the algorithm implemented in MATLAB

  7. ImplementationData – sample of processed data Input image Edge image Correlation result Boundaries

  8. ImplementationData – Templates License plate template Correlation template

  9. ResultsCorrelation - success Sobel image Best matches marked Correlation result Correlation on correlation

  10. ResultsCorrelation - failure Correlation image Best matches marked Correlation image Best matches marked

  11. ResultsMoment Invariants - success Input image Boundaries Selected match

  12. ResultsMoment Invariants - failure Input image Boundaries - insufficient Input image Boundaries - protrusions

  13. ResultsSummary Correlation 60% success rate Correlation on correlation 20% success rate Moment Invariants 20% success rate

  14. Discussion Correlation: moderate success, correlation on correlation not better Further work – refine template? (will be difficult to do for all cases) Moment Invariants: not successful Further work – refine boundary/object identification (difficult due to wide variations in vehicle features, lighting, etc.) Limitations: sample size small, images controlled

  15. Conclusion Simple template matching, using correlation or moment invariants, does not appear to be a good candidate for automatic license plate location.

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