Image Pre-processing Algorithms Based On HVS In Color Separation System Of Ceramic Tiles

In the field of machine vision applications, intelligent color separation of ceramic tiles, a highly subjective process, focuses attention on color and texture perception. It is a typical application case that simulates human brain recognition and analysis. Image preprocessing is the necessary prerequisite for feature extraction and color separation. With regard to the above application case, the thesis proposes a series of image pre-processing algorithms based on human color perception in color separation system of ceramic tiles. The research findings are highly reflected in several national and provincial projects, such as "Ceramic Tile Sorting System by Visual Sensing" and "Highway Disaster Weather and Emergency Forewarn Decision Supplementary Synthetic System Based on Visual Sensing".Based on analysis of recent research progress at home and abroad, the meaning and role of preprocessing in vision sensing systems are described. For a variety of practical applications, difficulties and encountered problems in key modules and algorithms are analyzed.Then corresponding algorithms or optimized solutions are proposed. Study on key preprocessing algorithms includes brightness correction, color light correction based on color constancy, color correction for industrial camera and texture simulates based on HVS. These preprocessing algorithms have been experimentally verified and applied in multiple application prospects such as vision sensing based ceramic tiles sorting, express ways and abnormal event detection.In terms of brightness pre-processing, to correct the vignetting effect caused by devices or light source, the thesis proposes a correction method in brightness space based on CSLIP logarithm computing framework. The proposed method transfers calculation of image pixel values to that of transmittance, then recovers images whose brightness is uniform.In terms of image color pre-processing, to improve the output quality of color information and overcome the color cast problem caused by illumination or cameras, the thesis proposes a colored light correction method based on logarithmic color image processing framework to recover image color to the standard D65 condition. Moreover, the regression-calibration method is used to improve color accuracy of digital cameras, Combined with automatic brightness classification strategy.In terms of image texture pre-processing, the thesis simulates color assimilation and contrast phenomena of human eye, recover the processing effect of human eye on texture. The thesis puts forward a local adaptive perceptual color difference algorithm during the extracting process of image texture information and then combines the influence of local changes in luminance and spatial frequency to human visual system. In a word, the proposed method lays the foundation for advanced image feature extraction and analysis in the next stage.By experimental verification and practical application, the research results of preprocessing algorithms are credible, and have important theoretical significance and application value. The main contributions of this dissertation are summarized as follows:·A multi-parameter uneven brightness correction algorithm is improved. CSLIP is applied into brightness correction so that images with even brightness are recovered.·A colored light correction algorithm is improved. The correlations between the color channels in the diagonal model are overcome, so as to correct the image color to standard white light.·A color correction algorithm based on polynomial regression is improved. An automatic luminance gradation correction strategy is used to solve the overexposure or dark problem in color correction.·Combined with the mask effect of human eye, a calculation method of color difference threshold is improved. As a result, the influence of image noise on feature selection is effectively reduced.