TY - CHAP A1 - Gräßl, Christoph A1 - Deinzer, Frank A1 - Niemann, Heinrich ED - Krasnoproshin, V. ED - Ablameyko, S. ED - Soldek, J. T1 - Continuous Parametrization of Normal Distributions for Improving the Discrete Statistical Eigenspace Approach for Object Recognition T2 - Pattern Recognition and Information Processing Y1 - 2003 UR - https://d1wqtxts1xzle7.cloudfront.net/42129001/Continuous_parametrization_of_normal_dis20160205-16346-rayto3-libre.pdf?1454667530=&response-content-disposition=inline%3B+filename%3DContinuous_parametrization_of_normal_dis.pdf&Expires=1696432558&Signature=TmoSjs27NuupbCgHp6Y6aZdGGychAuKRf66hsbjelx-Zxe3FPaK25CiMC73SL~I4uz0DbjQN3ZwmFYwlgMuJRRcgO6AIaac4vNtJHfly-lOPIhXYqdsA3w3VwDeFU2SzEWZ7KEdn~rHjk4ZK8TZ6JOGzbO4CIe5SnOyhwUJL7w9o6~UwGGvtHKtNYOugvbwMdDr7iOanVhRQvMT5Fr1AEp5-mDS9VAg~7fkMD39pc-9vRBj0-4eS4tBQQFApJeYR244n7e~4wRClNqZp2lfn5kPjf-df5xj6iXBIUe4EKt53cR~ZFvE96N405yH14wGiTpGd1Vtx5VC-J0mS4o9XeQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA VL - 1 SP - 73 EP - 77 ER - TY - CHAP A1 - Gräßl, Christoph A1 - Deinzer, Frank A1 - Mattern, F. A1 - Niemann, Heinrich T1 - Improving Statistical Object Recognition Approaches by a Parameterization of Normal Distributions T2 - 6th German-Russian IAPR Workshop on Pattern Recognition and Image Understanding Y1 - 2003 SP - 38 EP - 41 ER - TY - CHAP A1 - Grzegorzek, Marcin A1 - Deinzer, Frank A1 - Reinhold, Michael A1 - Denzler, Joachim A1 - Niemann, Heinrich ED - Ertl, T. ED - Girod, B. ED - Greiner, G. ED - Niemann, Heinrich ED - Seidl, H.-P. ED - Steinbach, E. ED - Westermann, R. T1 - How Fusion of Multiple Views Can Improve Object Recognition in Real-World Environments T2 - Proceedings of the Vision, Modeling, and Visualization Conference 2003 N2 - 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. Y1 - 2003 UR - https://pub.inf-cv.uni-jena.de/pdf/Grzegorzek03:HFO.pdf SN - 3-89838-048-3 SP - 553 EP - 560 ER - TY - CHAP A1 - Deinzer, Frank A1 - Denzler, Joachim A1 - Niemann, Heinrich ED - Petkov, N. ED - Westenberg, M. T1 - Viewpoint Selection – Planning Optimal Sequences of Views for Object Recognition T2 - Computer Analysis of Images and Patterns – CAIP 2003 Y1 - 2003 UR - https://pub.inf-cv.uni-jena.de/pdf/Deinzer03:VSP.pdf SN - 3-540-40730-8 VL - 2756 SP - 65 EP - 73 ER -