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A New Distance Measure Based on Generalized Image Normalized Cross-Correlation for Robust Video Tracking and Image
Arie Nakhmani1, Allen Tannenbaum
1A. Nakhmani is with the Department of Electrical and Computer Engineering, Boston University, Boston, MA.
Pattern Recognition Letters
|March 19, 2013
Summary
We introduce two new normalized cross-correlation-based distance measures for image matching. These novel measures offer improved performance for specific computer vision tasks like image recognition and object tracking.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Image matching is crucial for various computer vision tasks.
- Existing distance measures have limitations depending on the application.
- Natural images exhibit high spatial correlation between pixels.
Purpose of the Study:
- To propose two novel, normalized distance measures for image matching.
- To evaluate the performance of these measures against established methods.
- To determine the suitability of the proposed measures for different computer vision applications.
Main Methods:
- Development of two novel distance measures based on normalized cross-correlation.
- Normalization of measures to a range between 0 and 1.
- Comparative analysis with Normalized Cross-Correlation (NCC) and Image Euclidean Distance (IMED).
Main Results:
- The proposed measures leverage pixel spatial correlation for enhanced accuracy.
- One measure demonstrates superior performance in tracking applications.
- The other measure is more appropriate for image recognition tasks.
Conclusions:
- The novel distance measures offer distinct advantages for specific image matching applications.
- The choice of distance measure should be tailored to the requirements of the computer vision task.
- These findings contribute to the development of more robust image matching algorithms.
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