These microsatellite loci will provide a useful tool for further investigating genetic variation in natural populations of C. oliveri, which will inform future conservation and management strategies. Additionally, cross-amplification in C. fortunei suggested the potential utility of these loci in this and other congeneric species.

Shape alignment or estimation under occlusion is one of the most challenging tasks in computer vision field. Most previous works treat occlusion as noises or part models, which usually lead to low accuracy or inefficiencies. This paper proposes an efficient and accurate regression-based algorithm for face alignment. In this framework, local and global regressions are iteratively used to train a series of random forests in a cascaded manner. In training and testing process, each step consists of two layers. In the first layer, a set of highly discriminative local features are extracted from local regions according to locality principle. The regression forests are trained for each facial landmark independently using those local features. Then the leaf node of the regression tree is encoded by histogram statistic method and the final shape is estimated by a linear regression matrix. In the second layer, our proposed global features are generated. Then we use those features to train a random fern to keep the global shape constraints. Experiments show that our method has a high speed, but same or slightly lower accuracy than state of the art methods under occlusion condition. In order to gain a higher accuracy we use multi-random shape for initialization, which may slightly reduce the calculation efficiency as a trade-off.

Human action recognition is very important and significant research work in numerous fields of science, for example, human–computer interaction, computer vision and crime analysis. In recent years, relative geometry features have been widely applied to the description of relative relation of body motion. It brings many benefits to action recognition such as clear description, abundant features etc. But the obvious disadvantage is that the extracted features severely rely on the local coordinate system. It is difficult to find a bijection between relative geometry and skeleton motion. To overcome this problem, many previous methods use relative rotation and translation between all skeleton pairs to increase robustness. In this paper we present a new motion representation method. It establishes a motion model based on the relative geometry with the aid of special orthogonal group SO(3). At the same time, we proved that this motion representation method can establish a bijection between relative geometry and motion of skeleton pairs. After the motion representation method in this paper is used, the computation cost of action recognition reduces from the two-way relative motion (motion from A to B and B to A) to one-way relative motion (motion from A to B or B to A) between any skeleton pair, namely, permutation problem [Formula: see text] is simplified into combinatorics problem [Formula: see text]. Finally, the experimental results of the three motion datasets are all superior to present skeleton-based action recognition methods.

Abstract. The matrix-form LSQR method is presented in this paper for solving the least squares problem of the matrix equation AXB = C with tridiagonal matrix constraint. Based on a matrix-form bidiagonalization procedure, the least squares problem associated with the tridiagonal constrained matrix equation AXB = C reduces to a unconstrained least squares problem of linear system, which can be solved by using the classical LSQR algorithm. Furthermore, the preconditioned matrix-form LSQR method is adopted for solving the corresponding least squares problem.

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