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< < | The process involves designing colour maps (or transfer functions) which relate the original image values (for instance density for CT data) to colour, opacity and potentially other material parameters such as roughness. Many different sorts of transfer functions have been proposed, which also make use of data gradients or curvatures as well as data values. However, the limit to all of these is often the complexity of defining the actual colour mapping. Standard transfer functions are usually provided which 'work' for certain scenarios. Editing these would nearly always produce a better result, even if the scenario fits, but this is oftern a very counter-intuitive task. | |||||||
> > | The process involves designing colour maps (or transfer functions) which relate the original image values (for instance density for CT data) to colour, opacity and potentially other material parameters such as roughness. Many different sorts of transfer functions have been proposed, which also make use of data gradients or curvatures as well as data values. However, the limit to all of these is often the complexity of defining the actual colour mapping. Standard transfer functions are usually provided which 'work' for certain scenarios. Editing these would nearly always produce a better result, even if the scenario fits, but this is often a very counter-intuitive task. | |||||||
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< < | So to what extent can these mappings be automatically designed for each specific data set? There are several interesting avenues to pursue here. For instance, there is a lot of work on colour spaces and how we perceive colour: what are the best combinations of colours, and how shouls they be combined, to achieve the best possible contrast in a volume rendering, and hence allow you to see as many individual features as possible? Or alternatively, if it is desired to match a volume rendering to a real object, can the set of colours and material parameters be automatically extracted from, say, a photo, and applied to the data so that the rendering closely matches reality? | |||||||
> > | So to what extent can these mappings be automatically designed for each specific data set? There are several interesting avenues to pursue here. For instance, there is a lot of work on colour spaces and how we perceive colour: what are the best combinations of colours, and how should they be combined, to achieve the best possible contrast in a volume rendering, and hence allow you to see as many individual features as possible? Or alternatively, if it is desired to match a volume rendering to a real object, can the set of colours and material parameters be automatically extracted from, say, a photo, and applied to the data so that the rendering closely matches reality? | |||||||
This is an algorithmic development / software project, so experience of writing software is essential. C++ and GLSL would be helpful, though the development could be done using another programming environment, and the project could be used as a way to learn a shading language such as GLSL. |