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.

Integrated color defect detection method for polysilicon wafers using machine vision

For the typical color detects of polysilicon wafers, i.e., edge discoloration, color inaccuracy and color non-uniformity, a new integrated machine vision detection method is proposed based on an HSV color model. By transforming RGB image into three-channel HSV images, the HSV model can efficiently reduce the disturbances of complex wafer textures. A fuzzy color clustering method is used to detect edge discoloration by defining membership function for each channel image. The mean-value classi- fying method and region growing method are used to identify the other two defects, respectively. A vision detection system is developed and applied in the produc- tion of polysilicon wafers.

On-Line Defect Detection of Aluminum Coating Using Fiber Optic Sensor

Aluminum metallization using the sprayed coating for exhaust mild steel （MS） pipes of tractors is a standard practice for avoiding rusting. Patches of thin metal coats are prone to rusting and are thus considered as defects in the surface coating. This paper reports a novel configuration of the fiber optic sensor for on-line checking the aluminum metaUization uniformity and hence for defect detection. An optimally chosen high bright 440 nm BLUE LED （light-emitting diode） launches light into a transmitting fiber inclined at the angle of 60° to the surface under inspection placed adequately. The reflected light is transported by a receiving fiber to a blue enhanced photo detector. The metallization thickness on the coated surface results in visually observable variation in the gray shades. The coated pipe is spirally inspected by a combination of linear and rotary motions. The sensor output is the signal conditioned and monitored with RISHUBH DAS. Experimental results show the good repeatability in the defect detection and coating non-uniformity measurement.

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.