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Dual rotating-compensator Mueller matrix polarimetry (DRC-MMP) has achieved wide spread applications in material characterization, nano-scale measurement, and biomedical diagnostics. However, the traditional calibration method for DRC-MMP relies on establishing an accurate system model, making its implementation cumbersome, especially in the presence of polarizing components that are to complex to be modeled. We propose a novel, to the best of our knowledge, eigenvalue calibration method for DRC-MMP without system modeling. Two specific basis vectors are introduced in order to transform the continuously modulated light intensity in DRC-MMP into a 5×5 projection matrix. Eigenvalue analysis is then performed based on the light intensity projection matrix to obtain the modulation matrix and the analysis matrix associated with the polarization state generator and the polarization state analyzer, respectively. The method is applied for DRC-MMP in both single-pass and double-pass setups. The experimental results have verified the proposed calibration method.

The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank truematrix and a sparse errormatrix. We then formulate an optimization problem by regularizing the truematrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the truecommunity indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.