18 research outputs found

    Reconstructing teeth with bite information

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    Fast Realization of Digital Elevation Model

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    International audienceWe propose an optimization approach to speed up the point matching process underlying the 3D reconstruction of complex urban scenes. We consider the Optical Flow technique for point matching and propose to introduce MMX and SSE2 instructions to accelerate significantly the matching process. Fast point matching allows using sub-pixel image resolution, which provides a more accurate estimation of the Optical Flow by exploiting wider correlation windows, and therefore improves the final quality of urban scenes 3D reconstructions

    Concept-based video search with the PicSOM multimedia retrieval system

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    Non-negative bases in spectral image archiving

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    Automatic Plant Annotation Using 3D Computer Vision

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    Discriminative learning with application to interactive facial image retrieval

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    The amount of digital images is growing drastically and advanced tools for searching in large image collections are therefore becoming urgently needed. Content-based image retrieval is advantageous for such a task in terms of automatic feature extraction and indexing without human labor and subjectivity in image annotations. The semantic gap between high-level semantics and low-level visual features can be reduced by the relevance feedback technique. However, most existing interactive content-based image retrieval (ICBIR) systems require a substantial amount of human evaluation labor, which leads to the evaluation fatigue problem that heavily restricts the application of ICBIR. In this thesis a solution based on discriminative learning is presented. It extends an existing ICBIR system, PicSOM, towards practical applications. The enhanced ICBIR system allows users to input partial relevance which includes not only relevance extent but also relevance reason. A multi-phase retrieval with partial relevance can adapt to the user's searching intention in a from-coarse-to-fine manner. The retrieval performance can be improved by employing supervised learning as a preprocessing step before unsupervised content-based indexing. In this work, Parzen Discriminant Analysis (PDA) is proposed to extract discriminative components from images. PDA regularizes the Informative Discriminant Analysis (IDA) objective with a greatly accelerated optimization algorithm. Moreover, discriminative Self-Organizing Maps trained with resulting features can easily handle fuzzy categorizations. The proposed techniques have been applied to interactive facial image retrieval. Both a query example and a benchmark simulation study are presented, which indicate that the first image depicting the target subject can be retrieved in a small number of rounds

    Visual analysis of faces with application in biometrics, forensics and health informatics

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    Turku Centre for Computer Science – Annual Report 2013

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    Due to a major reform of organization and responsibilities of TUCS, its role, activities, and even structures have been under reconsideration in 2013. The traditional pillar of collaboration at TUCS, doctoral training, was reorganized due to changes at both universities according to the renewed national system for doctoral education. Computer Science and Engineering and Information Systems Science are now accompanied by Mathematics and Statistics in newly established doctoral programs at both University of Turku and &Aring;bo Akademi University. Moreover, both universities granted sufficient resources to their respective programmes for doctoral training in these fields, so that joint activities at TUCS can continue. The outcome of this reorganization has the potential of proving out to be a success in terms of scientific profile as well as the quality and quantity of scientific and educational results.&nbsp; International activities that have been characteristic to TUCS since its inception continue strong. TUCS&rsquo; participation in European collaboration through EIT ICT Labs Master&rsquo;s and Doctoral School is now more active than ever. The new double degree programs at MSc and PhD level between University of Turku and Fudan University in Shaghai, P.R.China were succesfully set up and are&nbsp; now running for their first year. The joint students will add to the already international athmosphere of the ICT House.&nbsp; The four new thematic reseach programmes set up acccording to the decision by the TUCS Board have now established themselves, and a number of events and other activities saw the light in 2013. The TUCS Distinguished Lecture Series managed to gather a large audience with its several prominent speakers. The development of these and other research centre activities continue, and&nbsp; new practices and structures will be initiated to support the tradition of close academic collaboration.&nbsp; The TUCS&rsquo; slogan Where Academic Tradition Meets the Exciting Future has proven true throughout these changes. Despite of the dark clouds on the national and European economic sky, science and higher education in the field have managed to retain all the key ingredients for success. Indeed, the future of ICT and Mathematics in Turku seems exciting.</p

    Building Energy Model Generation Using a Digital Photogrammetry-Based 3D Model

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    Buildings consume a large amount of energy and environmental resources. At the same time, current practices for whole-building energy simulation are costly and require skilled labor. As Building Energy Modeling (BEM) and simulations are becoming increasingly important, there is a growing need to make environmental assessments of buildings more efficient and accessible. A building energy model is based on collecting input data from the real, physical world and representing them as a digital energy model. Real-world data is also collected in the field of 3D reconstruction and image analysis, where major developments have been happening in recent years. Current digital photogrammetry software can automatically match photographs taken with a simple smartphone camera and generate a 3D model. This thesis presents methods and techniques that can be used to generate a building energy model from a digital photogrammetry-based 3D model. To accomplish this, a prototype program was developed that uses 3D reconstructed data as geometric modeling inputs for BEM. To validate the prototype, an experiment was conducted where a case-study building was selected. Photographs of the building were taken using a small remotelycontrolled Unmanned Aerial Vehicle (UAV) drone. Then, using photogrammetry software, the photographs were used to automatically generate a textured 3D model. The texture map, which is an image that represents the color information in the 3D model, was semantically annotated to extract building elements. The window annotations were iii used as inputs for the BEM process. In addition, a number of algorithms were applied to automatically convert both the 3D model and the annotated texture map into geometry that is compatible for a building energy model. Through the prototype, pre-defined templates were used with the geometric inputs to generate an EnergyPlus model (as an example building energy model). The feasibility of this experiment was verified by running a successful energy simulation. The results of this thesis contribute towards creating an automated and user-friendly photo-to-BEM method
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