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.
|Number of pages||15|
|Journal||International Journal of Computer Vision|
|State||Published - 1 Jan 2017|
Bibliographical notePublisher Copyright:
© 2016, Springer Science+Business Media New York.
- Image matching
- Pattern matching
- Sublinear algorithms
- Template matching
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence