Detection of primary RGB colors projected on a screen using fNIRS

In this study,functional near-infrared spectroscopy(fNIRS)is utilized to measure the hemodynamic responses(HRs)in the visual cortex of 14 subjects(aged 22–34 years)viewing the primary red,green,and blue(RGB)colors displayed on a white screen by a beam projector.The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins(HbO and HbR)in the visual cortex are measured using a 15-source and 15-detector optode con¯guration.To see whether the activation maps upon RGB-color stimuli can be distinguished or not,the t-values of individual channels are averaged over 14 subjects.To¯nd the best combination of two features for classi¯cation,the HRs of activated channels are averaged over nine trials.The HbO mean,peak,slope,skewness and kurtosis values during 2–7 s window for a given 10 s stimulation period are analyzed.Finally,the linear discriminant analysis(LDA)for classifying three classes is applied.Individually,the best classi¯cation accuracy obtained with slope-skewness features was 74.07%(Subject 1),whereas the best overall over 14 subjects was 55.29%with peak-skewness combination.Noting that the chance level of 3-class classi¯cation is 33.33%,it can be said that RGB colors can be distinguished.The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

A ROBUST COLOR EDGE DETECTION ALGORITHM BASED ON THE QUATERNION HARDY FILTER

This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.