Template matching is a fundamental problem in computer vision, with many applications. Existing methods use sliding window computation for choosing an image-window that best matches the template. For classic algorithms based on SSD, SAD and normalized cross-correlation, efficient algorithms have been developed allowing them to run in real-time. Current state of the art algorithms are based on nearest neighbor (NN) matching of small patches within the template to patches in the image. These algorithms yield state-of-the-art results since they can deal better with changes in appearance, viewpoint, illumination, non-rigid transformations, and occlusion. However, NN-based algorithms are relatively slow not only due to NN computation for each image patch, but also since their sliding window computation is inefficient. We therefore propose in this paper an efficient NN-based algorithm. Its accuracy is similar (in some cases slightly better) than the existing algorithms and its running time is 43–200 times faster depending on the sizes of the images and templates used. The main contribution of our method is an algorithm for incrementally computing the score of each image window based on the score computed for the previous window. This is in contrast to computing the score for each image window independently, as in previous NN-based methods. The complexity of our method is therefore O(|I|) instead of O(|I||T|), where I and T are the image and the template respectively.
|שפה מקורית||אנגלית אמריקאית|
|כותר פרסום המארח||Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings|
|עורכים||Martial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss|
|מוציא לאור||Springer Verlag|
|מזהי עצם דיגיטלי (DOIs)|
|סטטוס פרסום||פורסם - 2018|
|אירוע||15th European Conference on Computer Vision, ECCV 2018 - Munich, גרמניה|
משך הזמן: 8 ספט׳ 2018 → 14 ספט׳ 2018
|שם||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|כנס||15th European Conference on Computer Vision, ECCV 2018|
|תקופה||8/09/18 → 14/09/18|
הערה ביבליוגרפיתPublisher Copyright:
© Springer Nature Switzerland AG 2018.
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