Intelligent Data Analysis for Real-Life Applications: ...
https://books.google.com/books?isbn=1466618078
Magdalena-Benedito, Rafael - 2012 - Computers
It was assumed in our early work (Zhang & Fan, 2010) that the gait manifold has a closed-loop 1D structure while the pose manifold is characterized by a circle.
https://books.google.com/books?isbn=1466618078
Magdalena-Benedito, Rafael - 2012 - Computers
It was assumed in our early work (Zhang & Fan, 2010) that the gait manifold has a closed-loop 1D structure while the pose manifold is characterized by a circle.Continuous Multiclass Labeling Approaches and Algorithms
arxiv.org/pdf/1102.5448
arXiv
by J Lellmann - 2011 - Cited by 61 - Related articles
Mar 2, 2011 - mented in C++. We used Matlab's built-in FFTW ...... J. Lellmann, F. Becker, and C[PDF]Dissertation_Keuper.pdf - Computer Vision Group, Freiburg
lmb.informatik.uni-freiburg.de/.../...
5We used the C++ implementation of the algorithm from the ArrayToolbox library ...... J. Lellmann, F. Becker, and C. Schnörr. Convex optimization for multi-class ...
Albert Ludwigs University of Freiburg
[PDF]Convex Variational Methods for Semantic Image Analysis
mediatum.ub.tum.de/doc/.../1273106.pdf
Matlab or in C++ with parallelization on GPU and compare well to state-of-the- ...... [88]J. Lellmann, F. Becker, and C. Schnörr. Convex optimization for.
Technical University of Munich
[PDF]thesis-b-shafei.pdf (10167 KB) - KLUEDO - Technische ...
https://kluedo.ub.uni-kl.de/files/3656/thesis-b-shafei.pdf
by B Shafei - Related articles
Nov 11, 2013 - We executed our C++ implementation of TVcM (Algorithm 8) with 4 ...... [97] J. Lellmann, F. Becker, and C. Schnörr. Convex optimization for ...[PDF]Globally optimal image segmentation incorporating region ...
ir.uiowa.edu/cgi/viewcontent.cgi?article=3136&context=etd
by Q Song - 2012 - Related articles
4.6.3.3 Execution time. Our algorithm was implemented in C++ on a Linux workstation (3GHz, 32GB ...... [31] J.Lellmann, F.Becker, and C.Schnorr. Convex ...This thesis is devoted to the automatic segmentation of small biological specimen from microscopic recordings. The specimen we are dealing with are cells, cell nuclei, and other cell organelles, i.e. we are working at the limit of the optical resolution. However, the exact knowledge of the individual cell or cell nucleus anatomy is crucial for the analysis of cellular and sub-cellular mechanisms. Depending on the concrete application, biologists for example want to measure the variation of size and shape of cells in a population, are interested in protein colocalizations, i.e. the co-occurrence of two or more proteins, or want to investigate the distribution of a certain protein in relation to the cell nucleus, nucleolus, mitochondria, or other structures. For these questions, it is usually necessary to record different data channels and relate them to one another. An example is given in figure 1.1. In this exemplary experimental setting, the cell nucleus is recorded in channel 0, the nucleolus in channel 1, and the protein under investigation in channel 2. From the segmentations of the cell nucleus and nucleolus and the detected protein clusters, a cell nucleus model can be built, that allows, for example, to measure the distances from the protein clusters to the nuclear membrane
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