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A concise synthesis of both C5′ diastereomers of 5′-(hydroxymethyl)-6,5′-cyclo-2′,5′-dideoxyuridine is presented. Individual crystal structures of the fully deprotected S- and R-stereoisomers indicate that the addition of a methylene group between the C5′ and O5′ positions of 6,5′-cyclo-2′-deoxyuridine increases the distance between the nucleobase and backbone. The resulting nucleosides adopt a conformation that more closely mimics A-form rather than B-form nucleotides. Nucleotides of 5′(R)-(hydroxymethyl)-6,5′-cyclo-2′,5′-dideoxyuridine were successfully incorporated into DNA oligonucleotides during automated synthesis, but full-length oligonucleotides were not obtained after post-synthetic cleavage from the solid support due to a base-catalyzed strand cleavage at the site of insertion.

In this paper, we apply the geometric curve evolution approach to 3D reconstruction for a number of images in the stereo vision. The curve evolution approach is based on the Euclidean curve shortening evolution theory, and the level set method is introduced into it for numerical computation. The Euler-Lagrange equations that are deduced from the variational principle provide a set of curve evolving equations. These PDE's describe the process of the geometric curve evolution on the relevant epipolar plane. In 3D space, the each epipolar plane is monogamously projected as a unique pair of the intra-scanlines on stereo pairs. These PDE's are used to deform an initial set of curves that then move towards the object outlines to be detected. The level set implementation of these PDE's provides an efficient and robust computational way for the kind of the geometric-driven evolving equations. The velocity term in the level set equations can be obtained from the above geometric curve evolving equations. It is intrinsic and only depends upon the stereo problem. We present the close form of the velocity. Finally, the results of implementation of our theory are presented on synthetic images.

Image morphing method, which is based on the pixels positions and color interpolation between two images, hasbeen widely used in image interpolation. Existing morphing methods can not keep 3D shape, even though 2D shape.In this paper, we put forward a new morphing method. The interpolation image gained by our method is correspondentto certain viewpoint. The method need not predetermine the camera matrix. And it is of simple calculation and easy realization. Our method is practical by the experiments: The interpolation images gained by our method exactly represent the transformation of the view point from the resource image to the destination image. 1 IMAGE MORPHING The processing to gain the intermediate image(15) from two source images(I,I) is called Image Mophing. Ij andIi are images of the different viewpoints, I is the image of the intermediate viewpoint. Image morphing include thefollowing steps:(1)Establish corresponding relationship Co :I C1 :I —I where p0 E 1 C0(p0) E I (

The weighted nuclear norm minimization and truncated nuclear norm minimization are two well-known low-rank constraint for visual applications. In this paper, by integrating their advantages into a unified formulation, we find a better weighting strategy, namely truncated reweighting norm minimization (TRNM), which provides better approximation to the target rank for some specific task. Albeit nonconvex and truncated, we prove that TRNM is equivalent to certain weighted quadratic programming problems, whose global optimum can be accessed by the newly presented reweighting singular value thresholding operator. More importantly, we design a computationally efficient optimization algorithm, namely momentum update and rank propagation (MURP), for the general TRNM regularized problems. The individual advantages of MURP include, first, reducing iterations through nonmonotonic search, and second, mitigating computational cost by reducing the size of target matrix. Furthermore, the descent property and convergence of MURP are proven. Finally, two practical models, i.e., Matrix Completion Problem via TRNM (MCTRNM) and Space Clustering Model via TRNM (SCTRNM), are presented for visual applications. Extensive experimental results show that our methods achieve better performance, both qualitatively and quantitatively, compared with several state-of-the-art algorithms.

In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2-order tensors (2D matrices). In order to avoid the difficulties faced by tensors computations (such as the multiplication, general transpose and Hermitian symmetry of tensors), our proposed nD-PCA algorithm has to exploit a newly proposed higher-order singular value decomposition (HO-SVD). To evaluate the validity and performance of nD-PCA, a series of experiments are performed on the FRGC 3D scan facial database

In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the generalized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.