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Ward","Hans Gellersen","Gerhard Tröster" ], "authors": [ {"first" : "Andreas", "last" : "Bulling"}, {"first" : "Jamie A.", "last" : "Ward"}, {"first" : "Hans", "last" : "Gellersen"}, {"first" : "Gerhard", "last" : "Tröster"} ], "volume": "33","number": "4","pages": "741-753","note": "spotlight","abstract": "In this work we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data was recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals - saccades, fixations, and blinks - and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance feature selection (mRMR). We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video, and browsing the web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1% and recall of 70.5% over all classes and participants. 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By its very nature, a height field cannot store terrain structures with multiple vertical layers such as overhanging cliffs, caves, or arches. This restriction does not apply to volumetric data structures. However, the workflow of manual modelling and editing of volumetric terrain usually is tedious and very time-consuming. Therefore, we propose to use three-dimensional curve-based primitives to efficiently model prominent, large-scale terrain features. We present a technique for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while retaining the full artistic freedom of volumetric terrains. Feature Curves also contain material information that can be complemented with local details by using per-face texture mapping. All stages of our method are GPU-accelerated using compute shaders to ensure interactive editing of terrain. 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Friedrich" ], "authors": [ {"first" : "Roman", "last" : "Klinger"}, {"first" : "Christoph M.", "last" : "Friedrich"} ], "editor": [ "Galia Angelova","Kalina Bontcheva","Ruslan Mitkov","Nicolas Nicolov","Nicolai Nikolov" ], "editors": [ {"first" : "Galia", "last" : "Angelova"}, {"first" : "Kalina", "last" : "Bontcheva"}, {"first" : "Ruslan", "last" : "Mitkov"}, {"first" : "Nicolas", "last" : "Nicolov"}, {"first" : "Nicolai", "last" : "Nikolov"} ], "pages": "185-191", "bibtexKey": "Klinger2009a" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20b3c9b4fb2a1f5d4a2122b0dbdbcc9bc/isw-bibliothek", "tags" : [ "Feature","ISW","data","feature-based","interactions,","model" ], "intraHash" : "0b3c9b4fb2a1f5d4a2122b0dbdbcc9bc", "interHash" : "7bfb36e6551f51bc4e6d542eab82b2f3", "label" : "Defining, recognizing and representing feature interactions in a feature-baseddata model: Defining, recognizing and representing feature interactions in a feature-baseddata model", "user" : "isw-bibliothek", "description" : "", "date" : "2016-03-03 09:53:12", "changeDate" : "2016-03-03 09:18:35", "count" : 1, "pub-type": "misc", "year": "2011", "url": "", "author": [ "Tobias Dipper","Xun Xu","Peter Klemm" ], "authors": [ {"first" : "Tobias", "last" : "Dipper"}, {"first" : "Xun", "last" : "Xu"}, {"first" : "Peter", "last" : "Klemm"} ], "pages": "--","abstract": "This research takes a feature data model, i.e. STEP-NC as input and determinesinteractions among the features in the model. The feature interaction data arethen appended to the original data model for subsequent uses such as processplanning. The method is generic by defining and utilising a set of interactionentities such as removal volumes, common volumes, connection face and bridgefaces. This feature interaction data model is developed using the EXPRESSlanguage, making it compatible with the STEP-NC data model. The interactiondata are presented in both 3D and feature interaction graph (FIG) format in adeveloped software system assisted by the Open CASCADE solid modelling kernel.", "__markedentry" : "[xtl:6]", "bibtexKey": "DipperXuKlemm2011" } ] }