Description of two new species of Calodromus Guérin-Méneville, 1832 from Peninsular Malaysia (Coleoptera: Brentidae, Cyphagoginae)

Calodromus mantillerii n. sp. and Calodromus goosseni n. sp. from Perak (Malaysia) are described on two single male specimens. The new taxa are closely related to Calodromus insignis (Senna, 1895) but can be easily distinguished by the very different shape of the first tarsal article of the male hind legs. A key for the identification of the males of the three known species of the Calodromus insignis group is provided. A new locality record of Calodromus insignis from Malaysia is also given

c ○ 2003 Kluwer Academic Publishers. Manufactured in The Netherlands. Understanding the Behavior of SFM Algorithms: A Geometric Approach

Abstract. We put forth in this paper a geometrically motivated motion error analysis which is capable of supporting investigation of global effect such as inherent ambiguities. This is in contrast with the usual statistical kinds of motion error analyses which can only deal with local effect such as noise perturbations, and where much of the results regarding global ambiguities are empirical in nature. The error expression that we derive allows us to predict the exact conditions likely to cause ambiguities and how these ambiguities vary with motion types such as lateral or forward motion. Given the erroneous 3-D motion estimates caused by the inherent ambiguities, it is also important to study the behavior of the resultant distortion in depth recovered under different motion-scene configurations. Such an investigation may alert us to the occurrence of ambiguities under different conditions and be more careful in picking the solution. Our formulation, though geometrically motivated, was also put to use in modeling the effect of noise and in revealing the strong influence of feature distribution. Experiments on both synthetic and real image sequences were conducted to verify the various theoretical predictions. Keywords: structure from motion, error analysis, epipolar constraint, inherent ambiguity, depth distortio

Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees

Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group){Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such as those (approximately) lying on subspaces {We follow {liu2010robust} and use the term "subspace" to denote both linear subspaces and affine subspaces. There is a trivial conversion between linear subspaces and affine subspaces as mentioned therein.} or low-dimensional manifolds. By learning the affinity matrix in the form of sparse reconstruction, techniques proposed in this vein often considerably boost the performance in subspace settings where traditional SC can fail. Despite the success, there are fundamental problems that have been left unsolved: the spectrum property of the learned affinity matrix cannot be gauged in advance, and there is often one ugly symmetrization step that post-processes the affinity for SC input. Hence we advocate to enforce the symmetric positive semidefinite constraint explicitly during learning (Low-Rank Representation with Positive SemiDefinite constraint, or LRR-PSD), and show that factually it can be solved in an exquisite scheme efficiently instead of general-purpose SDP solvers that usually scale up poorly. We provide rigorous mathematical derivations to show that, in its canonical form, LRR-PSD is equivalent to the recently proposed Low-Rank Representation (LRR) scheme {liu2010robust}, and hence offer theoretic and practical insights to both LRR-PSD and LRR, inviting future research. As per the computational cost, our proposal is at most comparable to that of LRR, if not less. We validate our theoretic analysis and optimization scheme by experiments on both synthetic and real data sets.Comment: 10 pages, 4 figures. Accepted by ICDM Workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM), 2010. Main proof simplified and typos corrected. Experimental data slightly adde