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Title: Modelling texture appearance of gonioapparent objects
Author: Kitaguchi, Saori
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2008
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Quantifying the appearance of coating products is essential in the automobile and automobile finishing industries for efficient product development and product/quality control. There is a specific need to develop techniques to measure the total appearance of metallic coatings. The present study focuses on two key attributes of visual texture: coarseness and glint. In order to develop models to capable of measuring these attributes, it was first necessary to design psychophysical experiments for assessing coarseness and glint as perceived on metallic-coating panels. The change in the appearance of the metallic coatings is known as a gonioapparent effect, and is greatly dependant on the illumination and viewing conditions. Therefore, appropriate conditions were carefully examined for the independent observation of coarseness and glint in order to discern those attributes. It was found that diffuse illumination was appropriate for viewing coarseness and directional illumination was appropriate for observing glint. Under these appropriately-controlled conditions, the perceptual coarseness and glint of sets of metallic-coating panels were assessed by human observers. A digital camera was used to capture information on the spatial detail of the metallic-coating panels. An image of each panel was captured under the same viewing conditions as used for the visual assessments. The information in a single image was sufficient to represent a metallic-coating panel under identical diffuse illumination conditions for which observers assessed coarseness. For capturing information on glint, however, a high dynamic-range (HDR) image was necessary because the dynamic range of the scene in which the glint was observed exceeded that of the camera used in this study. Two computational models were developed to predict perceptual coarseness and perceptual glint by extracting associated features from the captured images. The performance of these models was verified by comparing predictions made using them with the perceptual coarseness and glint scaled by observers. For industrial use, the visualisation of products on computer displays would give various opportunities, for example, to develop and design products or coatings and also to communicate appearance information. A digital camera and a suitable display would enable this to be achieved, but the ability to reproduce the appearance of metallic-coating products on displays in a satisfactory manner was found to have significant ~ challenges. The coarseness model developed in the present study was able to represent perceptual coarseness based on the images captured by the digital camera. However, the resolution of the images was not high enough to resolve the individual aluminium flakes contained in the coatings, which contribute to the visual texture. Therefore, verification of the images was carried out for the coarseness attribute by comparing the coarseness perceived in the images displayed on a liquid-crystal display (LCD) with the metallic-coating panels themselves. In addition to camera limitations, LCD resolution also prevented the same conditions used for physical panel assessment from being replicated. Therefore, two optimal conditions were selected and perceptual coarseness was scaled using images. Besides the difference in experimental conditions, there was also a difference in the "absolute" texture appearance between the two media because of errors in image reproduction of the images. In spite of this, the relatively-scaled perceptual coarseness for the image samples agreed well with that for the original physical samples. This implies that it is practicable to assess perceptual coarseness from an image on a display that simulates a metallic-coating panel.
Supervisor: Luo, Ronnier ; Westland, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available