Multi-class Model Fitting by Energy Minimization and Mode-Seeking

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used alpha-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting

Robust Motion Segmentation from Pairwise Matches

In this paper we address a classification problem that has not been considered before, namely motion segmentation given pairwise matches only. Our contribution to this unexplored task is a novel formulation of motion segmentation as a two-step process. First, motion segmentation is performed on image pairs independently. Secondly, we combine independent pairwise segmentation results in a robust way into the final globally consistent segmentation. Our approach is inspired by the success of averaging methods. We demonstrate in simulated as well as in real experiments that our method is very effective in reducing the errors in the pairwise motion segmentation and can cope with large number of mismatches

Panchromatic spectral energy distributions of Herschel sources

(abridged) Far-infrared Herschel photometry from the PEP and HerMES programs is combined with ancillary datasets in the GOODS-N, GOODS-S, and COSMOS fields. Based on this rich dataset, we reproduce the restframe UV to FIR ten-colors distribution of galaxies using a superposition of multi-variate Gaussian modes. The median SED of each mode is then fitted with a modified version of the MAGPHYS code that combines stellar light, emission from dust heated by stars and a possible warm dust contribution heated by an AGN. The defined Gaussian grouping is also used to identify rare sources. The zoology of outliers includes Herschel-detected ellipticals, very blue z~1 Ly-break galaxies, quiescent spirals, and torus-dominated AGN with star formation. Out of these groups and outliers, a new template library is assembled, consisting of 32 SEDs describing the intrinsic scatter in the restframe UV-to-submm colors of infrared galaxies. This library is tested against L(IR) estimates with and without Herschel data included, and compared to eight other popular methods often adopted in the literature. When implementing Herschel photometry, these approaches produce L(IR) values consistent with each other within a median absolute deviation of 10-20%, the scatter being dominated more by fine tuning of the codes, rather than by the choice of SED templates. Finally, the library is used to classify 24 micron detected sources in PEP GOODS fields. AGN appear to be distributed in the stellar mass (M*) vs. star formation rate (SFR) space along with all other galaxies, regardless of the amount of infrared luminosity they are powering, with the tendency to lie on the high SFR side of the "main sequence". The incidence of warmer star-forming sources grows for objects with higher specific star formation rates (sSFR), and they tend to populate the "off-sequence" region of the M*-SFR-z space.Comment: Accepted for publication in A&A. Some figures are presented in low resolution. The new galaxy templates are available for download at the address http://www.mpe.mpg.de/ir/Research/PEP/uvfir_temp