期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 11, 页码 3768-3780出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2451175
关键词
Shearlets; multi-scale image analysis; image features; edge detection; corner detection
资金
- Progetto PRIN Varieta Reali e Complesse Geometria, Topologia e Analisi Armonica
- Gruppo Nazionale per l'Analisi Matematica, la Probabilita e le loro Applicazioni through the Istituto Nazionale di Alta Matematica
Shearlets are a relatively new and very effective multi-scale framework for signal analysis. Contrary to the traditional wavelets, shearlets are capable to efficiently capture the anisotropic information in multivariate problem classes. Therefore, shearlets can be seen as the valid choice for multi-scale analysis and detection of directional sensitive visual features like edges and corners. In this paper, we start by reviewing the main properties of shearlets that are important for edge and corner detection. Then, we study algorithms for multi-scale edge and corner detection based on the shearlet representation. We provide an extensive experimental assessment on benchmark data sets which empirically confirms the potential of shearlets feature detection.
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