Enhanced Scalar-Invariant Feature Transformation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)

Abstract

The proposed work enhances the feature point detection in the scalar-invariant feature transformation (SIFT). The sequence of steps in the SIFT algorithm drops most of the feature points in the low-contrast regions of the image. This paper provides a solution to this problem by adding the output of Sobel filtered image on the input image iteratively until the entropy of the input image is increased to a saturation level. The SIFT descriptors generated for this enhanced image tend to describe the redundant features in the image. To overcome this problem, affinity propagation clustering is done.

Keywords

Scalar-invariant feature transformation (SIFT) Low contrast Entropy Sobel Affinity propagation clustering 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computing Sciences and EngineeringVIT UniversityChennaiIndia

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