@article{KoeppingEbnerGrzegorzeketal.2014, author = {K{\"o}pping, Lukas and Ebner, Frank and Grzegorzek, Marcin and Deinzer, Frank}, title = {Indoor localization using step and turn detection together with floor map information}, series = {FHWS science journal}, volume = {2 (2014)}, journal = {FHWS science journal}, number = {1}, organization = {Hochschule f{\"u}r angewandte Wissenschaften W{\"u}rzburg-Schweinfurt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-976}, pages = {40 -- 49}, year = {2014}, abstract = {In this work we present a method to estimate an indoor position with the help of smartphone sensors and without any knowledge of absolute positioning systems like Wi-Fi signals. Our system uses particle filtering to solve the recursive state estimation problem of finding the position of a pedestrian. We show how to integrate the information of the previous state into the weight update step and how the observation data can help within the state transition model. High positional accuracy can be achieved by only knowing that the pedestrian makes a foot step or changes her direction together with floor map information.}, language = {en} } @article{TorresSospedraJimenezKnauthetal., author = {Torres-Sospedra, Joaqu{\´i}n and Jim{\´e}nez, Antonio R. and Knauth, Stefan and Moreira, Adriano and Beer, Yair and Fetzer, Toni and Ta, Viet-Cuong and Montoliu, Raul and Seco, Fernando and Mendoza-Silva, Germ{\´a}n M. and Belmonte, Oscar and Koukofikis, Athanasios and Nicolau, Maria Jo{\~a}o and Costa, Ant{\´o}nio and Meneses, Filipe and Ebner, Frank and Deinzer, Frank and Vaufreydaz, Dominique and Dao, Trung-Kien and Castelli, Eric}, title = {The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work}, series = {Sensors}, volume = {17}, journal = {Sensors}, number = {3}, issn = {1424-8220}, doi = {10.3390/s17030557}, language = {en} } @article{EbnerFetzerDeinzeretal., author = {Ebner, Frank and Fetzer, Toni and Deinzer, Frank and Grzegorzek, Marcin}, title = {On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization}, series = {ISPRS International Journal of Geo-Information}, volume = {6}, journal = {ISPRS International Journal of Geo-Information}, number = {8}, issn = {2220-9964}, doi = {10.3390/ijgi6080233}, language = {en} } @inproceedings{EbnerFetzerKoepingetal., author = {Ebner, Frank and Fetzer, Toni and K{\"o}ping, Lukas and Grzegorzek, Marcin and Deinzer, Frank}, title = {Multi Sensor 3D Indoor Localisation}, series = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015)}, booktitle = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015)}, pages = {1 -- 11}, language = {en} } @inproceedings{BullmannFetzerEbneretal., author = {Bullmann, Markus and Fetzer, Toni and Ebner, Frank and Deinzer, Frank and Grzegorzek, Marcin}, title = {Fast Kernel Density Estimation Using Gaussian Filter Approximation}, series = {21st International Conference on Information Fusion, FUSION 2018, Cambridge, UK, July 10-13, 2018}, booktitle = {21st International Conference on Information Fusion, FUSION 2018, Cambridge, UK, July 10-13, 2018}, doi = {10.23919/ICIF.2018.8455686}, pages = {1233 -- 1240}, language = {en} } @article{BullmannFetzerEbneretal., author = {Bullmann, Markus and Fetzer, Toni and Ebner, Frank and Ebner, Markus and Deinzer, Frank and Grzegorzek, Marcin}, title = {Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {16}, issn = {1424-8220}, doi = {10.3390/s20164515}, language = {en} } @inproceedings{EbnerDeinzerKopingetal., author = {Ebner, Frank and Deinzer, Frank and Koping, Lukas and Grzegorzek, Marcin}, title = {Robust self-localization using Wi-Fi, step/turn-detection and recursive density estimation}, series = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2014)}, booktitle = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2014)}, doi = {10.1109/IPIN.2014.7275537}, pages = {627 -- 635}, language = {en} } @article{FetzerEbnerBullmannetal., author = {Fetzer, Toni and Ebner, Frank and Bullmann, Markus and Deinzer, Frank and Grzegorzek, Marcin}, title = {Smartphone-Based Indoor Localization within a 13th Century Historic Building}, series = {Sensors}, volume = {18}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s18124095}, language = {en} } @article{FetzerEbnerDeinzeretal., author = {Fetzer, Toni and Ebner, Frank and Deinzer, Frank and Grzegorzek, Marcin}, title = {Using Barometer for Floor Assignation within Statistical Indoor Localization}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {1}, issn = {1424-8220}, doi = {10.3390/s23010080}, language = {en} } @inproceedings{DeinzerDenzlerNiemann, author = {Deinzer, Frank and Denzler, Joachim and Niemann, Heinrich}, title = {Viewpoint Selection - A Classifier Independent Learning Approach}, series = {IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, Texas, USA}, booktitle = {IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, Texas, USA}, isbn = {0-7695-0595-3}, pages = {209 -- 213}, language = {en} } @inproceedings{DeinzerDenzlerNiemann, author = {Deinzer, Frank and Denzler, Joachim and Niemann, Heinrich}, title = {Classifier Independent Viewpoint Selection for 3-D Object Recognition}, series = {Mustererkennung 2000, 22. DAGM-Symposium, Kiel}, booktitle = {Mustererkennung 2000, 22. DAGM-Symposium, Kiel}, editor = {Sommer, G. and Kr{\"u}ger, N. and Perwass, Ch.}, isbn = {3-540-67886-7}, pages = {237 -- 244}, language = {en} } @inproceedings{GraesslDeinzerNiemann, author = {Gr{\"a}ßl, Christoph and Deinzer, Frank and Niemann, Heinrich}, title = {Continuous Parametrization of Normal Distributions for Improving the Discrete Statistical Eigenspace Approach for Object Recognition}, series = {Pattern Recognition and Information Processing}, volume = {1}, booktitle = {Pattern Recognition and Information Processing}, editor = {Krasnoproshin, V. and Ablameyko, S. and Soldek, J.}, pages = {73 -- 77}, language = {en} } @inproceedings{DeinzerDenzlerNiemann, author = {Deinzer, Frank and Denzler, Joachim and Niemann, Heinrich}, title = {On Fusion of Multiple Views for Active Object Recognition}, series = {Pattern Recognition - Proceedings of the 23rd DAGM Symposium}, volume = {2191}, booktitle = {Pattern Recognition - Proceedings of the 23rd DAGM Symposium}, editor = {Radig, B.}, pages = {239 -- 245}, language = {en} } @inproceedings{GraesslDeinzerMatternetal., author = {Gr{\"a}ßl, Christoph and Deinzer, Frank and Mattern, F. and Niemann, Heinrich}, title = {Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions}, series = {6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding}, booktitle = {6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding}, pages = {38 -- 41}, language = {en} } @inproceedings{GraesslDeinzerMatternetal., author = {Gr{\"a}ßl, Christoph and Deinzer, Frank and Mattern, F. and Niemann, Heinrich}, title = {Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions}, series = {6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding}, volume = {14}, booktitle = {6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding}, number = {2}, issn = {1054-6618}, pages = {222 -- 230}, abstract = {As statistical approaches play an important role in object recognition, we present a novel approach which is based on object mod- els consisting of normal distributions for each training image. We show how to parameterize the mean vector and covariance matrix independently from the interpolation technique and formulate the classification and localization as a continuous optimization problem. This enables the computation of object poses which have never been seen during training. For interpolation, we present four different techniques which are compared in an experiment with real images. The results show the benefits of our method both in classification rate and pose estimation accuracy.}, language = {en} } @inproceedings{GrzegorzekDeinzerReinholdetal., author = {Grzegorzek, Marcin and Deinzer, Frank and Reinhold, Michael and Denzler, Joachim and Niemann, Heinrich}, title = {How Fusion of Multiple Views Can Improve Object Recognition in Real-World Environments}, series = {Proceedings of the Vision, Modeling, and Visualization Conference 2003}, booktitle = {Proceedings of the Vision, Modeling, and Visualization Conference 2003}, editor = {Ertl, T. and Girod, B. and Greiner, G. and Niemann, Heinrich and Seidl, H.-P. and Steinbach, E. and Westermann, R.}, isbn = {3-89838-048-3}, pages = {553 -- 560}, abstract = {In the past decades most object recognition systems were based on passive approaches. But in the last few years a lot of research was done in the field of active object recognition. In this context there are several unique problems to be solved. One of them is how to fuse a series of images that might differ in their viewpoints. In this paper we present a well-founded approach for the fusion of multiple views based on a recursive density propagation method. It uses particle filters for solving the fusion in a continuous pose space. Furthermore we will show by means of a statistical object recognition system how to integrate such systems into our fusion approach. The experimental result will show, how the fusion can improve classification rates substantial, es-pecially for difficult conditions like heterogeneous background within real world environments.}, language = {en} } @inproceedings{DeinzerDenzlerNiemann, author = {Deinzer, Frank and Denzler, Joachim and Niemann, Heinrich}, title = {Viewpoint Selection - Planning Optimal Sequences of Views for Object Recognition}, series = {Computer Analysis of Images and Patterns - CAIP 2003}, volume = {2756}, booktitle = {Computer Analysis of Images and Patterns - CAIP 2003}, editor = {Petkov, N. and Westenberg, M.}, isbn = {3-540-40730-8}, pages = {65 -- 73}, language = {en} } @inproceedings{DeinzerDenzlerNiemann, author = {Deinzer, Frank and Denzler, Joachim and Niemann, Heinrich}, title = {Improving Object Recognition By Fusion Of Multiple Views}, series = {Proceedings of the Third Indian Conference on Computer Vision Graphics and Image Processing}, booktitle = {Proceedings of the Third Indian Conference on Computer Vision Graphics and Image Processing}, pages = {161 -- 166}, language = {en} } @inproceedings{ReinholdDeinzerDenzleretal., author = {Reinhold, M. and Deinzer, Frank and Denzler, Joachim and Paulus, D. and P{\"o}sl, Joseph}, title = {Active Appearance-Based Object Recognition Using Viewpoint Selection}, series = {Proceedings of the 2000 Conference on Vision Modeling and Visualization}, booktitle = {Proceedings of the 2000 Conference on Vision Modeling and Visualization}, editor = {Girod, B. and Niemann, Heinrich and Seidel, H.-P.}, pages = {105 -- 112}, abstract = {In this paper we address the classification of 3-D objects that look similar from sev-eral sights and can only be distinguished from some certain viewpoints. For this purpose we combine a statistical appearance-based object recognition approach with an active viewpoint selection mechanism. For appearance-based object recognition lo-cal features are derived from wavelet multires-olution analysis. The recognition process is performed hierarchically in a statistical frame-work by a maximum likelihood estimation. Based on this result the active viewpoint se-lection mechanism chooses one further view that allows a reliable classification. Hereby the viewpoint selection mechanism can be trained unsupervised and represents the space of possible viewpoints continuously. Experimental results show that our approach is well suited for a reliable classifica-tion of similar looking objects only by one fur-ther view.}, language = {en} } @inproceedings{DeutschDeinzerZobeletal., author = {Deutsch, Benjamin and Deinzer, Frank and Zobel, Matthias and Denzler, Joachim}, title = {Active Sensing Strategies for Robotic Platforms, with an Application in Vision-Based Gripping}, series = {Proceedings of the 1st International Conference on Informatics in Control, Automation and Robotics}, volume = {2}, booktitle = {Proceedings of the 1st International Conference on Informatics in Control, Automation and Robotics}, isbn = {972-8865-12-0}, pages = {169 -- 176}, language = {en} } @inproceedings{FischerHaasNoethetal., author = {Fischer, Julia and Haas, J{\"u}rgen and N{\"o}th, Elmar and Niemann, Heinrich and Deinzer, Frank}, title = {Empowering Knowledge Based Speech Understanding through Statistics}, series = {5th Interational Conference on Spoken Language Processing}, volume = {5}, booktitle = {5th Interational Conference on Spoken Language Processing}, pages = {2231 -- 2235}, language = {en} } @article{DeinzerFischerAhlrichsetal., author = {Deinzer, Frank and Fischer, Julia and Ahlrichs, Ulrike and N{\"o}th, Elmar}, title = {Learning of Domain Dependent Knowledge in Semantic Networks}, series = {Eurospeech}, volume = {5}, journal = {Eurospeech}, pages = {1987 -- 1990}, abstract = {For an effcient linguistic analysis of spoken queries a lot of domain specific knowledge is needed and usually has to be entered manually into the knowledge base of each domain. This makes the adaption of dialogue systems which base on explicit knowledge representation to new domains a very costly pro cedure. We use a frequency based statistical method combined with general hidden markov models in order to learn domain specific knowledge within a semantic network formalism. As a framework we use a dialogue system for German train timetable information. By means of experiments we show that our statistical approach is not only able to reach, but even outperforms previous results with manually entered restrictions.}, language = {en} } @inproceedings{DerichsDeinzerNiemann, author = {Derichs, Christian and Deinzer, Frank and Niemann, Heinrich}, title = {Cost Integration in Multi-Step Viewpoint Selection for Object Recognition}, series = {Proceedings of the International Conference on Machine Learning and Data Mining MLDM 2005}, booktitle = {Proceedings of the International Conference on Machine Learning and Data Mining MLDM 2005}, pages = {415 -- 425}, language = {en} }