A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Shape Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The analysis of morphological filters with multiple structuring elements
Computer Vision, Graphics, and Image Processing
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Signal representation using fuzzy morphology
ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Journal of Visual Communication and Image Representation
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
A morphological gradient approach to color edge detection
IEEE Transactions on Image Processing
The Pairing of a Wavelet Basis With a Mildly Redundant Analysis via Subband Regression
IEEE Transactions on Image Processing
Subband DCT: definition, analysis, and applications
IEEE Transactions on Circuits and Systems for Video Technology
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A novel signal representation using fuzzy mathematical morphology is developed. We take advantage of the optimum fuzzy fitting and the efficient implementation of morphological operators to extract geometric information from signals. The new representation provides results analogous to those given by the polynomial transform. Geometrical decomposition of a signal is achieved by windowing and applying sequentially fuzzy morphological opening with structuring functions. The resulting representation is made to resemble an orthogonal expansion by constraining the results of opening to equate adapted structuring functions. Properties of the geometric decomposition are considered and used to calculate the adaptation parameters. Our procedure provides an efficient and flexible representation which can be efficiently implemented in parallel. The application of the representation is illustrated in data compression and fractal dimension estimation temporal signals and images.