Scale Invariant Interest Points with Shearlets
This work addresses feature detection for image analysis, but it is incremental as it adapts shearlets to a known problem.
The paper tackled blob detection in images by deriving a scale-invariant measure within the shearlets framework, demonstrating effectiveness on benchmark data and robustness in compressed and noisy conditions.
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.