Name
Abstract | ||
|---|---|---|
This paper presents a method that reduces the computational cost of template matching based on the Zero-mean Normal- ized Cross-Correlation (ZNCC) without compromising the accuracy of the results. A very effective condition is deter- mined at a small and� xed cost that allow to rapidly detect a large number of mismatching candidates with no need to compute the ZNCC score. Then, thanks to the use of an additional set of conditions, the computation of the whole ZNCC function is typically required only for a very small number of candidates. Experimental results demonstrate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICIP.2008.4711888 | San Diego, CA |
Keywords | Field | DocType |
correlation methods,image matching,object detection,ZNCC template matching,computational cost,image detection,mismatching candidate rejection,zero-mean normalized cross-correlation,Template matching,ZNCC,cross correlation,exhaustive,fast | Cross-correlation,Template matching,Computer vision,Object detection,Normalization (statistics),Pattern recognition,Computer science,Image processing,Fast Fourier transform,Pixel,Artificial intelligence,Computation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-1764-3 | 978-1-4244-1764-3 | 7 |
PageRank | References | Authors |
0.57 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Mattoccia | 1 | 727 | 52.65 |
| Federico Tombari | 2 | 39 | 1.78 |
| Luigi Di Stefano | 3 | 197 | 11.89 |