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This transformation can be modeled as a chirplet transform implemented via a self-organizing neural network.
We use the wavelet transform implemented in a conventional dynamic spectrogram as the starting point.
This paper describes a watershed transform implemented on an FPGA for image segmentation.
The use of the short-time Fourier transform and the wavelet transform implemented using the new harmonic wavelets for analyzing the time variation of the spectral contents of exponentially time-decaying signals is studied in the present investigation.
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Finally, the discrete cosine transform was implemented.
We used the Hilbert transform as implemented by MATLAB.
Additionally, the transform is implemented using the lifting scheme [17] to reduce the complexity and maintain invertibility.
Note that due to occlusions and the way in which the inter-view transform is implemented, two or more layers may overlap in each subband.
The marker-controlled watershed transform is implemented with the markers and the edge strength image to segment buildings from the background.
Fast 8 × 8 forward and inverse transform is implemented using matrix decomposition and matrix operation such as Kronecker product and direct sum.
Morphological operations and Hough transform are implemented to locate the optic disc, while the fovea is located by using the spatial relationship with the optic disc and from the spatial distribution of the macula.
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