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2005
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Incorporating Background Invariance into Feature-Based Object Recognition

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Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformations, such as rotation, scaling, or affine warping. When patches overlap object boundaries, however, errors in both detection and matching will almost certainly occur due to inclusion of unwanted background pixels. This is common in real images, which often contain significant background clutter, objects which are not heavily textured, or objects which occupy a relatively small portion of the image. We suggest improvements to the popular Scale Invariant Feature Transform (SIFT) which incorporate local object boundary information. The resulting feature detection and descriptor creation processes are invariant to changes in background. We call this method the Background and Scale Invariant Feature Transform (BSIFT). We demonstrate BSIFT’s superior performance in feature detection and matching on synthetic and ...
Andrew N. Stein, Martial Hebert
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where WACV
Authors Andrew N. Stein, Martial Hebert
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