Feature extraction processing time in human visual system

Feature Extraction from Visual Data Feature Extraction from Visual Data

Real-time Hypothesis Driven Feature Extraction on Parallel Processing Architectures

Feature extraction in content-based indexing of media streams is often computational intensive. Typically, a parallel processing architecture is necessary for real-time performance when extracting features brute force. On the other hand, Bayesian network based systems for hypothesis driven feature extraction, which selectively extract relevant features one-by-one, have in some cases achieved re...

Feature Extraction and Image Processing

A Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

Feature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition

Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...