@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} } @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} }