Novel registration algorithm for 3-D images captured from multiple views of object surface

A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.

A Two-Stage Algorithm of High Resolution Image Alignment for Mobile Applications

Global motion estimation (GME) algorithms are widely applied to computer vision and video processing. In the previous works, the image resolutions are usually low for the real-time requirement (e.g. video stabilization). However, in some mobile devices applications (e.g. image sequence panoramic stitching), the high resolution is necessary to obtain satisfactory quality of panoramic image. However, the computational cost will become too expensive to be suitable for the low power consumption requirement of mobile device. The full search algorithm can obtain the global minimum with extremely computational cost, while the typical fast algorithms may suffer from the local minimum problem. This paper proposed a fast algorithm to deal with 2560 × 1920 high-resolution (HR) image sequences. The proposed method estimates the motion vector by a two-level coarse-to-fine scheme which only exploits sparse reference blocks (25 blocks in this paper) in each level to determine the global motion vector, thus the computational costs are significantly decreased. In order to increase the effective search range and robustness, the predictive motion vector (PMV) technique is used in this work. By the comparisons of computational complexity, the proposed algorithm costs less addition operations than the typical Three-Step Search algorithm (TSS) for estimating the global motion of the HR images without the local minimum problem. The quantitative evaluations show that our method is comparable to the full search algorithm (FSA) which is considered to be the golden baseline.

Fast and Robust Stereo Vision Algorithm for Obstacle Detection

Binocular computer vision is based on bionics, after the calibration through the camera head by double-exposure image synchronization, access to the calculation of two-dimensional image pixels of the three-dimensional depth information. In this paper, a fast and robust stereo vision algorithm is described to perform in-vehicle obstacles detection and characterization. The stereo algorithm which provides a suitable representation of the geometric content of the road scene is described, and an in-vehicle embedded system is presented. We present the way in which the algorithm is used, and then report experiments on real situations which show that our solution is accurate, reliable and efficient. In particular, both processes are fast, generic, robust to noise and bad conditions, and work even with partial occlusion.

Facial model fitting algorithm based on active appearance model

Active appearance model（AAM） is an efficient useful for the subsequent work such as face detection and method for the localization of facial feature points, which is also facial expression recognition. In this paper, we mainly discuss the AAMs based on principal component analysis （PCA）. We also propose an efficient facial fitting algorithm, which is named inverse compositional image alignment （ICIA）, to eliminate a considerable amount of computation resulting from traditional gradient descent fitting algorithm. Finally, 3D facial curvature is used to initialize the location of facial feature, which helps select the parameters of initial state for the improved AAM.

A user-friendly nano-CT image alignment and 3D reconstruction platform based on Lab VIEW

X-ray computed tomography at the nanometer scale （nano-CT） offers a wide range of applications in scientific and industrial areas. Here we describe a reliable, user-friendly, and fast software package based on LabVIEW that may allow us to perform all procedures after the acquisition of raw projection images in order to obtain the inner structure of the investigated sample. A suitable image alignment process to address misalignment problems among image series due to mechanical manufacturing errors, thermal expansion, and other external factors has been considered, together with a novel fast parallel beam 3D reconstruction procedure that was developed ad hoc to perform the tomographic reconstruction. We have obtained remarkably improved reconstruction results at the Beijing Synchrotron Radiation Facility after the image calibration, the fundamental role of this image alignment procedure was confirmed, which minimizes the unwanted blurs and additional streaking artifacts that are always present in reconstructed slices. Moreover, this nano-CT image alignment and its associated 3D reconstruction procedure are fully based on LabVIEW routines, significantly reducing the data post-processing cycle, thus making the activity of the users faster and easier during experimental runs.

Batch image alignment via subspace recovery based on alternative sparsity pursuit

The problem of robust alignment of batches of images can be formulated as a low-rank matrix optimization problem, relying on the similarity of well-aligned images. Going further, observing that the images to be aligned are sampled from a union of low-rank subspaces, we propose a new method based on subspace recovery techniques to provide more robust and accurate alignment. The proposed method seeks a set of domain transformations which are applied to the unaligned images so that the resulting images are made as similar as possible. The resulting optimization problem can be linearized as a series of convex optimization problems which can be solved by alternative sparsity pursuit techniques. Compared to existing methods like robust alignment by sparse and low-rank models, the proposed method can more effectively solve the batch image alignment problem,and extract more similar structures from the misaligned images.

一种用于位移场分析的应变模式频域数字图像相关方法

本文提出了一种在频域中考虑应变对位移影响的频域数字图像相关方法.研究并建立了一个在频域中评价参考图像子区与目标图像子区之间匹配的评价标准,并在此基础之上,提出了可用于位移和应变计算的频域迭代算法,其中,Hessian矩阵在迭代过程中保持不变.在Hessian矩阵的形成过程中,参考图像子区中灰度的梯度可以通过快速傅里叶变换进行计算,具有很高的计算效率和精度.同时,一种离散傅里叶变换重采样技术被用于代替传统的插值方法对变形图像子区中亚像素位置的灰度进行重建和更新.为了验证所提方法的有效性和准确性,使用了国际DIC挑战委员会(IDCC)提供的系列图像(2D-Challenge 1.0,样本6)进行位移分析.分析的误差结果表明,在子区大小为41×41像素的条件下,对变形为50%的模拟实验进行了计算,位移分析结果的精度约为0.0032像素(最大绝对值误差),在精度上优于已发表的文献.最后,对硅橡胶试件进行了14.85%的单轴拉伸试验,通过相关系数分布检验了本文所提方法在实际应用中的有效性,并给出了分析的位移和应变分布.