Modelling the total appearance of gonio-apparent surfaces using stereo vision

Over recent decades, the textured coating provided by metallic surfaces has been an important factor in attracting customers of the automobile industry. This has meant that quantifying the appearance of coating products is essential for product development and quality control. The appearance of these coated products strongly depends on the viewing geometry, giving rise to a variety of properties of perceptual attributes such as texture, colour and gloss. Due to the visually-complex nature of such coatings, there remains an unsatisfied demand to develop techniques to measure the total appearance of metallic coatings. This study describes which aims to define the total appearance of metallic coatings and then objectively characterise it. Total appearance here refers to the combination of three properties of perceptual attributes of the surface: glint, coarseness and brightness. A number of metallic panels were visually scaled and a computational model capable for predicting three perceptual attributes was developed. A computational model was developed to relate the results from this psychophysical experiment to data obtained from a stereo image capture system. This is a new alternative technique aimed at solving one of the most challenging problems in computer vision: stereo matching. In the system, two images are captured by a same camera under two different lighting conditions to mimic stereoscopic vision. This not only addresses the problem of stereo matching (i.e. to find the corresponding pixels between two images) but also enhances the effect of perceptual attributes. After linearisation of camera response, spatial uniformity correction was performed to minimise the effect of uneven illumination. A characterisation method was then used to transfer the RGB to device-independent values. Two images captured under different lighting conditions were merged to obtain stereo data. In glint feature extraction, the pixels in the final image were segmented into two regions: bright spots and dark background. Next, statistical analyses were applied to extract features. Finally a model was created to predict the glint attribute of the metallic coating panels based on an image captured by the stereo capture system. In coarseness feature extraction, the merged image transformed to frequency domain using a discrete Fourier Transform. An octave bandpass filter was then applied to the Fourier Spectra image and data analysis was carried out to achieve the “image variance value” for each band. In similar to final step of glint, a model was created to predict the coarseness attribute