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EN
The use of virtual reality (VR) has been exponentially increasing and due to that many researchers have started to work on developing new VR based social media. For this purpose it is important to have an avatar of the user which look like them to be easily generated by the devices which are accessible, such as mobile phones. In this paper, we propose a novel method of recreating a 3D human face model captured with a phone camera image or video data. The method focuses more on model shape than texture in order to make the face recognizable. We detect 68 facial feature points and use them to separate a face into four regions. For each area the best fitting models are found and are further morphed combined to find the best fitting models for each area. These are then combined and further morphed in order to restore the original facial proportions. We also present a method of texturing the resulting model, where the aforementioned feature points are used to generate a texture for the resulting model.
EN
In this paper we present MESA: a platform for design and evaluation of medical image segmentation methods. The platform offers a complete approach for the method creation and validation using simulated and real tomographic images. The system consists of several modules that provide a comprehensive workflow for generation of test data, segmentation method development as well as experiment planning and execution. The test data can be created as a virtual scene that provides an ideal reference segmentation and is also used to simulate the input images by a virtual magnetic resonance imaging (MRI) scanner. Both ideal reference segmentation and simulated images could be utilized during the evaluation of the segmentation methods. The platform offers various experimental capabilities to measure and compare the performance of the methods on various data sets, parameters and initializations. The segmentation framework, currently based on deformable models, uses a template solution for dynamical composition and creation of two- and three-dimensional methods. The platform is based on a client–server architecture, with computational and data storage modules deployed on the server and with browser-based client applications. We demonstrate the platform capabilities during the design of segmentation methods with the use of simulated and actual tomographic images.
3
Content available remote Multiresolution representation techniques of 3D objects from range data
EN
The advances realised during the last years in 3D object digitization technology have resulted in a considerable increase in the number and importance of applications that handle 3D spatial information, and in the amount of interest shown by the scientific community to the different problems involved in 3D data processing. This paper presents a comparison between two techniques for computing multiresolution shape models of 3D objects acquired as clouds of 3D points. Both of these techniques deform a mesh template over the input data, constructing the multiresolution representations using wavelet transforms. The first technique is based on a facet-based approach, while the second one uses a vertex-based approximation. The procedure is fully automated and can process data from any object with a genus equivalent to that of a sphere. An important feature of these methods is that they do not impose any restrictions on the input data. which can be provided by any type of 3D sensor. Advantages and disadvantages of both approaches are also analysed, and some experimental results are shown.
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