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13 Mar 2018: This initialization is subsequently used as starting point for nonlinearleast-squares optimization (termed bundle adjustment) over all unknowns (see [24] fora review). ... In: Advances in Neural Information Processing Systems 24 (NIPS 2011), pp.406–414

13 Mar 2018: 2 Related WorkSemantic pixel labelling was initially approached with TextonBoost [24], TextonForest [23]and Random Forest Based Classifiers [25]. ... CVPR 2008. IEEE Conference on, pages 1–8. IEEE, 2008. [24] Jamie Shotton, John Winn, Carsten Rother,

13 Mar 2018: IEEE Trans. Pattern Analysis and Machine Intell., 24(4):509–522,April 2002. 5. M. ... Journal of Computer Vision, 48(1):9–19, June 2002. 24. J. Vermaak, A.

13 Mar 2018: 13], [17], [24], [25], [28] that built on our formulation and attempted to address some of its. ... shortcomings. In Furukawaet al. [9] and Sinhaet al. [24] two different ways were proposed for in-.

13 Mar 2018: for each run the videos. of 24 subjects are exploited for training and the videos of.

20 Feb 2018: Corpus Mean (SD) Grades Correlationn Human Auto R p. L 21 24.2 (3.1) 17.1 (1.9). ... 69C 50 24.0 (3.0) 15.6 (3.3). 59. 01. Table 2: Mean (standard deviation) of human andautomated grades, along with Pearson’s correla-tions between the human and

13 Mar 2018: 6] M. A. Fischler and R. C. Bolles. Random sample consen-sus: A paradigm for model fitting with applications to imageanalysis and automated cartography.IEEE Transactions onComputers, 24(6):381–395,

13 Mar 2018: 24]. Taking the latter approach, wematch manifolds using canonical correlations. Canonical Correlation Analysis (CCA)(also called canonical or principal angles) [24] compares manifolds by measuring the an-gles between them (see ... InProc. ACCV, pages

13 Mar 2018: 24] for video classifi-cation. Recently, it was utilized in [11] for 3D shape objectrecognition tasks. ... Found. Trends. Comput. Graph. Vis.,pages 177–280, 2008. 2. [24] G. Willems, T.

13 Mar 2018: 1.22%. 1.52%. 1.99%. 0.05%. 1.38%. 60.43%. 69.01%. 60.09%. 89.85%. 75.19%. 87.24%. ... The overall score for TextonBoost in training and testing with daytime subsets of our database is 75.02%, and for dusk it was72.24%.