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We present a novel classification method for recognizing traffic sign symbols undergoing image degradations. In order to cope with the degradations, it is desirable to use combined blur-affine invariants (CBAIs) of traffic sign symbols as the feature vectors. Combined invariants allow to recognize objects in the degraded scene without any restoration. In this research, multi-class support vector machines...
This paper investigates the connection between modified fuzzy basis function (MFBF)-based classifiers and support vector classifiers, establishes a link between fuzzy rules and kernels, and proposes a new approach to build MFBF-based classifiers. Under some minor constrains, the equivalence of the two seemingly quite distinct classifiers is proved. Moreover, the kernel method has the inherent advantage...
Recently, support vector machine (SVM) has become a popular tool in pattern recognition. In developing a successful SVM classifier, the first step is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining...
This paper exhibits the connection between fuzzy inference system and kernel machines, and proposes a support vector learning approach to construct fuzzy inference system so that it can have good generalization ability in a high dimensional feature space. It is showed that the two seemingly unrelated research areas, fuzzy inference systems and kernel machines, are closely related. Under some minor...
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