Search

In this paper we present a novel pipeline for large-scale SfM. We first organise the images into a hierarchical tree built using agglomerative clustering. The SfM problem is then solved by reconstructing smaller image sets and merging them into a common frame of reference as we move up the tree in a bottom-up fashion. Such an approach drastically reduces the computational load for matching image pairs without sacrificing accuracy. It also makes the resulting sequence of bundle adjustment problems well-conditioned at all stages of reconstruction. We use motion averaging followed by global bundle adjustment for reconstruction of each individual cluster. Our 3D registration or alignment of partial reconstructions based on epipolar relationships is both robust and reliable and works well even when the available camera-point relationships are poorly conditioned. The overall result is a robust, accurate and efficient pipeline for large-scale SfM. We present extensive results that demonstrate these attributes of our pipeline on a number of large-scale, real-world datasets and compare with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we report the development of a new parallel solver using the Correction Procedure via Reconstruction (CPR) for viscous flows on moving and deforming grids. By employing an accurate treatment of flux derivatives for moving and deforming unstructured grids consisting of all quadrilateral cells, it is found that the Geometric Conservation Law is not explicitly required, the free-stream preservation is automatically satisfied. The CPR code is verified using a benchmark case for a moving inviscid vortex on moving and deforming grids. The optimal orders of accuracy are obtained. It is subsequently employed to study viscous flows on moving and deforming grids. The CPR method is faster than and nearly as accurate as the SD method for solving viscous flow problems with moving boundaries. [Copyright &y& Elsevier]

Abstract: This paper aims to mathematically advance the field of quantitative thermo-acoustic imaging. Given several electromagnetic data sets, we establish for the first time an analytical formula for reconstructing the absorption coefficient from thermal energy measurements. Since the formula involves derivatives of the given data up to the third order, it is unstable in the sense that small measurement noises may cause large errors. However, in the presence of measurement noise, the obtained formula, together with a noise regularization technique, provides a good initial guess for the true absorption coefficient. We finally correct the errors by deriving a reconstruction formula based on the least square solution of an optimal control problem and prove that this optimization step reduces the errors occurring and enhances the resolution. [Copyright &y& Elsevier]

Abstract: Yang and Ciou recently proposed a two-in-one image secret sharing scheme (TiOISSS), which can easily preview a vague image by human eyes, but also provide a perfect reconstruction of the original image by computation. However, their scheme cannot recover the lossless image by computation as they claimed. In this paper, we resolve the problem of lossless reconstruction. In addition, we improve the visual quality of the previewed image. Also, we introduce a new definition of contrast to evaluate the visual quality of the previewed image. Compared with Yang and Ciou’s TiOISSS, our scheme can gain the lossless secret image and meantime enhance the contrast of previewed image. [Copyright &y& Elsevier]

Abstract: The total variation based regularization method has been proven to be quite efficient for image restoration. However, the noise in the image is assumed to be Gaussian in the overwhelming majority of researches. In this paper, an extended ROF model is presented to restore image with non-Gaussian noise, in which the locations of the blurred pixels with high level noise are detected by a function and two estimated parameters of noise, while the fidelity and smoothness terms can be adaptively adjusted by updating these parameters. In contrast to the previous method, our model can give a much better restoration in some particular cases, such as the blurred image corrupted by impulsive noise and mixed noise. Moreover, the proposed minimization problem is solved by the split Bregman iteration, which makes our algorithm very fast. We provide some experiments and comparisons with other methods to illustrate the high efficiency of our method. [Copyright &y& Elsevier]