Abstract
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.
Original language | American English |
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Pages (from-to) | 111-125 |
Number of pages | 15 |
Journal | International Journal of Computer Vision |
Volume | 121 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2017 |
Keywords
- Image matching
- Pattern matching
- Sublinear algorithms
- Template matching
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence