Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification

Wavelet Based Feature Extraction of Electromyogram Signal for Denoising

Electrical signals recorded from muscles require processing before its use, so the modeling of these bioelectric signals is necessary. Wavelets are used for the processing of signals that are non-stationary and time varying. The surface Electromyogram signals were estimated with following steps, first, the obtained signal was decomposed using wavelet transform; then, decomposed coefficients wer...

Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

Eigenvector-Based Feature Extraction for Classification

This paper shows the importance of the use of class information in feature extraction for classification and inappropriateness of conventional PCA to feature extraction for classification. We consider two eigenvector-based approaches that take into account the class information. The first approach is parametric and optimizes the ratio of between-class variance to within-class variance of the tr...

Feature Extraction for Surface Classification – An Approach with Wavelets

Surface metrology with image processing is a challenging task having wide applications in industry. Surface roughness can be evaluated using texture classification approach. Important aspect here is appropriate selection of features that characterize the surface. We propose an effective combination of features for multi-scale and multi-directional analysis of engineering surfaces. The features ...

Empirical Modelling and Classification of Surface Electromyogram