Research output: Contribution to journal › Article › peer-review
Original language | English |
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Article number | 107337 |
Journal | PATTERN RECOGNITION |
Volume | 104 |
Issue number | 107337 |
Early online date | 14 Mar 2020 |
DOIs | |
Accepted/In press | 12 Mar 2020 |
E-pub ahead of print | 14 Mar 2020 |
Published | Aug 2020 |
Additional links |
Explaining away results in_Acc12Mar2020Epub14Mar2020_GREEN AAN
dim_patchmatching.pdf, 2.29 MB, application/pdf
Uploaded date:20 Mar 2020
Version:Accepted author manuscript
Licence:CC BY-NC-ND
Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in viewpoint, partial occlusion, and non-rigid deformations. This article tests a method of template matching that is more tolerant to such changes in appearance and that can, therefore, more accurately identify image patches. In traditional template matching the comparison between a template and the image is independent of the other templates. In contrast, the method advocated here takes into account the evidence provided by the image for the template at each location and the full range of alternative explanations represented by the same template at other locations and by other templates. Specifically, the proposed method of template matching is performed using a form of probabilistic inference known as “explaining away”. The algorithm used to implement explaining away has previously been used to simulate several neurobiological mechanisms, and been applied to image contour detection and pattern recognition tasks. Here it is applied for the first time to image patch matching, and is shown to produce superior results in comparison to the current state-of-the-art methods.
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