<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on measured data.</p> <p>Artificial neural networks, or neural networks are famous for their ability to deal with complex data and to approximate arbitrary functions. They have been adopted by many data-driven approaches for computer graphics and proven to be effective. Furthermore, neural networks have also been used by the artists for creative works and proven to have the ability of supporting creation of visual effects, animation and computational arts. Therefore, it is reasonable to consider artificial neural networks as potential tools for representing lighting models. Since shaders are used for general-purpose computing, neural networks can be further combined with modern graphics pipeline using shader implementation. </p> <p>In this research, the possibilities of shader-based neural networks to be used as an alternative to traditional lighting models are explored. Fully connected neural networks are implemented in fragment shader to reproduce lighting results in the graphics pipeline, and trained in compute shaders. Implemented networks are proved to be able to approximate mathematical lighting models. In this thesis, experiments are described to prove the ability of shader-based neural networks, to explore the proper network architecture and settings for different lighting models. Further explorations of possibilities of manually editing parameters are also described. Mean-square errors and runtime are taken as measurements of success to evaluate the experiments. Rendered images are also reported for visual comparison and evaluation.</p>

The thesis deals with incorporating the shape-from-shading technique into the multi-view stereo (MVS) reconstruction framework using the Oren-Nayar reflectance model for rough natural materials. Two methods for enhancing the MVS algorithm with new photo-consistency measure are proposed. Experiments with the laboratory images as well as with images of Mars's surface were conducted, proving that the proposed plane-sweeping method using shading information suitable for combining with MVS can nd the correct position of surface in 3D scene. The experiments also showed, that the Oren-Nayar reflectance model is very accurate for some real-world materials and it can be succesfuly used in the plane-sweeping method to accomplish better results than the Lambert's reflectance model. With precisely estimated material parameters and the light source and camera directions, it is possible to achieve the accuracy of few centimeters in estimating the position of real surface in the scene. / <p>Validerat; 20110825 (anonymous)</p>

This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.

This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.

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This thesis deals with the development of a new methodology for automatic generation of scanning positions based on a computer model of the part for digitization of sheet metal parts. Manufacture and related inspection of sheet metal parts are closely connected to automotive industry. Based on increasing general requirements on accuracy, there is also a requirement for accurate inspection of manufactured parts in serial-line production. Optical 3D scanners and industrial robots are used more often for that purpose. Measuring positions for accurate and fast digitization of a part need to be prepared as the manufacturing of the new part begins. Planning of such positions is done manually by positioning of the industrial robot and saving the positions. The planning of positions proposed by this methodology is done automatically. A methodology of positions planning, their simulation for true visibility of the part elements using reflectance model and a simulation of the positions for robot reachability is presented in this thesis. The entire methodology is implemented as a plug-in for the Rhinoceros software. High reduction of time in positions planning compared to the manual approach was observed in the performed experiments.