According to the defects of KNN(K-Nearest Neighbor) algorithm and SVM(Support Vector Machine) algorithm in tracking a moving target such the large consumption and the low accuracy of target tracking error, a tracking model of moving target is proposed based on the combination of KNN algorithm and SVM algorithm with minimum distance optimization. First categories divided according to the princip...

We address the problem of autonomously classifying objects from the sounds they make when struck, and present results from different attempts to classify various items. Previous work has shown that object classification is possible based on features derived from the frequency content of signals. We develop a moving-maximum algorithm to extract the two most significant spikes in the FFT of the s...

The classical Bayes classifier plays an important role in the field of pattern recognition. Usually, it is not easy to use a Bayes classifier for pattern recognition problems in high dimensional spaces. This paper proposes a novel PCA-based Bayes classifier for pattern recognition problems in high dimensional spaces. Experiments for face analysis have been performed on CMU facial expression ima...

We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier. Keywords—Text-independent speaker recognition, mel cepstral analysis, speech ...

The recognition of handwritten digit recognition has been a challenging problem among the researchers for few decades. This paper proposes a relative density feature extraction algorithm for recognizing unconstrained single connected handwritten numerals independent of the languages. The proposed method consists of four phases, namely, image enhancement (dilation), representation (zone based), ...

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distingui...

Image fusion is well used for medical brain image classification. Wavelet transform is the most commonly used image fusion method, which fuses the source images' information in wavelet domain according to some fusion rules. But because of the uncertainties of the source images' contributions to the fused image, how to design a good fusion rule to integrate as much information as possible into t...

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using...

A new automated MYC IHC classifier based on bivariate logistic regression is presented. The predictor relies on image analysis developed with the open-source ImageJ platform. From a histologic section immunostained for MYC protein, 2 dimensionless quantitative variables are extracted: (a) relative distance between nuclei positive for MYC IHC based on euclidean minimum spanning tree graph and (b...