Fast Homotopy-Preserving Skeletons Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Improvement on Joint Time-Frequency Representation with Application of Image Processing Technique
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A new technique to reduce cross terms in the Wigner distribution
Digital Signal Processing
An adaptive optimal-kernel time-frequency representation
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
Time-frequency representations have been of great interest in the analysis and classification of non-stationary signals. The use of highly selective transformation techniques is a valuable tool for obtaining accurate information for studies of this type. The Wigner-Ville distribution has high time and frequency selectivity in addition to meeting some interesting mathematical properties. However, due to the bi-linearity of the transform, interference terms emerge when the transform is applied over multi-component signals. In this paper, we propose a technique to remove cross-components from the Wigner-Ville transform using image processing algorithms. The proposed method exploits the advantages of non-linear morphological filters, using a spectrogram to obtain an adequate marker for the morphological processing of the Wigner-Ville transform. Unlike traditional smoothing techniques, this algorithm provides cross-term attenuations while preserving time-frequency resolutions. Moreover, it could also be applied to distributions with different interference geometries. The method has been applied to a set of different time-frequency transforms, with promising results.