Fast-Match: Fast Affine Template Matching

Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothn...

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Veröffentlicht in:International journal of computer vision Jg. 121; H. 1; S. 111 - 125
Hauptverfasser: Korman, Simon, Reichman, Daniel, Tsur, Gilad, Avidan, Shai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2017
Springer
Springer Nature B.V
Schlagworte:
Affine transformations
Algorithms
Analysis
Approximation
Artificial Intelligence
Computer Imaging
Computer Science
Computer vision
Errors
Experiments
Image Processing and Computer Vision
Image processing systems
Mathematical analysis
Pattern Recognition
Pattern Recognition and Graphics
Studies
Template matching
Transformations
Vision
Image matching
Template matching
Sublinear algorithms
Pattern matching
ISSN:0920-5691, 1573-1405
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Zusammenfassung:Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound-like scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-016-0926-1