Scale Invariant Interest Points with Shearlets

TL;DR

This paper introduces a shearlet-based method for scale-invariant blob detection, demonstrating its effectiveness in noisy and compressed images through algorithms and quantitative evaluation.

Contribution

It develops a novel shearlet-based measure for blob detection that is scale-invariant and effective in noisy conditions, with algorithms and validation on benchmark data.

Findings

Effective blob detection measure based on shearlets

Method is robust to noise and image compression

Quantitative evaluation confirms performance

Abstract

Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities such as edges and corners at multiple scales. In this work we address the problem of detecting and describing blob-like features in the shearlets framework. We derive a measure which is very effective for blob detection and closely related to the Laplacian of Gaussian. We demonstrate the measure satisfies the perfect scale invariance property in the continuous case. In the discrete setting, we derive algorithms for blob detection and keypoint description. Finally, we provide qualitative justifications of our findings as well as a quantitative evaluation on benchmark data. We also report an experimental evidence that our method is very suitable to deal with compressed and noisy images, thanks to the sparsity property of shearlets.