Abstract
Detecting, identifying, and recognizing salient regions or feature points in images is a very important and fundamental problem in computer vision and robotics. For gray scale x-ray images, stable and repeatable salient features that are invariant to a variety of effects like rotation, scale changes, view point changes, noise, or change in illumination conditions can provide better classification results.This paper compares two different methods for scale and rotation invariant interest point/feature detector and descriptor: Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF). It also presents a way to extract distinctive invariant features from gray scale X-ray images that can be used to perform reliable
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