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It is shown that frequency sensitive competitive learning (FSCL), one version of the recently improved competitive learning (CL) algorithms, significantly deteriorates in performance when the number of units is inappropriately selected. An algorithm called rival penalized competitive learning (RPCL) is proposed. In this algorithm, not only is the winner(More)
—We consider the extension of the Whittaker–Shannon (WS) reconstruction formula to the case of signals sampled in the presence of noise and which are not necessarily band limited. Observing that in this situation the classical sampling expansion yields inconsistent reconstruction, we introduce a class of signal recovery methods with a smooth correction of(More)
The denoising of a natural image corrupted by Gaussian noise is a classical problem in signal or image processing. Donoho and his coworkers at Stanford pioneered a wavelet denoising scheme by thresholding the wavelet coefficients arising from the standard discrete wavelet transform. This work has been widely used in science and engineering applications.(More)
In this paper, we present a novel descriptor for feature extraction by using a combination of Ridgelets and Fourier transform. We have successfully implemented ridgelets on the circular disk containing the pattern and applied Fourier transform on the resulting ridgelet coefficients to extract rotation-invariant features for pattern recognition. The(More)
We propose an algorithm to find piecewise linear skeletons of handwritten characters by using principal curves. The development of the method was inspired by the apparent similarity between the definition of principal curves (smooth curves which pass through the " middle " of a cloud of points) and the medial axis (smooth curves that go equidistantly from(More)