期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 26, 期 6, 页码 2853-2867出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2017.2687122
关键词
Shearlets; multi-scale image analysis; scale selection; image features; blob detection; keypoint description
Shearlets are a relatively new directional multiscale framework for signal analysis, which have been shown effective to enhance signal discontinuities, such as edges and corners at multiple scales even in the presence of a large quantity of noise. In this paper, we consider blob-like features in the shearlets framework. We derive a measure, which is very effective for blob detection, and, based on this measure, we propose a blob detector and a keypoint description, whose combination outperforms the state-of-the-art algorithms with noisy and compressed images. We also demonstrate that the measure satisfies the perfect scale invariance property in the continuous case. We evaluate the robustness of our algorithm to different types of noise, including blur, compression artifacts, and Gaussian noise. Furthermore, we carry on a comparative analysis on benchmark data, referring, in particular, to tolerance to noise and image compression.
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