Application of SIFT and SURF Detectors for Medical X-Ray Images

Journal Title: Journal of Medical Science And clinical Research - Year 2016, Vol 4, Issue 3

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 matching between different views of an object/scene

Authors and Affiliations

Prachi. G. Bhende

Keywords

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  • EP ID EP209433
  • DOI -
  • Views 72
  • Downloads 0

How To Cite

Prachi. G. Bhende (2016). Application of SIFT and SURF Detectors for Medical X-Ray Images. Journal of Medical Science And clinical Research, 4(3), 9641-9650. https://europub.co.uk/articles/-A-209433