LIC color texture enhancement algorithm for ocean vector field data based on HSV color mapping and cumulative distribution function

Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image,a new color texture enhancement algorithm based on the Line Integral Convolution(LIC)for the vector field data is proposed,which combines the HSV color mapping and cumulative distribution function calculation of vector field data.This algorithm can be summarized as follows:firstly,the vector field data is convoluted twice by line integration to get the gray texture image.Secondly,the method of mapping vector data to each component of the HSV color space is established.And then,the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image.Thirdly,the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values.Finally,both the gray texture image and the color image are fused to get the color texture.The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data.Furthermore,the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.

Brain-computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition

Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies.Then,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials.Afterward,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate.Finally,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.