Dynamic 3D shape measurement based on the phase-shifting moir\'e algorithm

In order to increase the efficiency of phase retrieval,Wang proposed a high-speed moire phase retrieval method.But it is used only to measure the tiny object. In view of the limitation of Wang method,we proposed a dynamic three-dimensional (3D) measurement based on the phase-shifting moire algorithm.First, four sinusoidal fringe patterns with a pi/2 phase-shift are projected on the reference plane and acquired four deformed fringe patterns of the reference plane in advance. Then only single-shot deformed fringe pattern of the tested object is captured in measurement process.Four moire fringe patterns can be obtained by numerical multiplication between the the AC component of the object pattern and the AC components of the reference patterns respectively. The four low-frequency components corresponding to the moire fringe patterns are calculated by the complex encoding FT (Fourier transform) ,spectrum filtering and inverse FT.Thus the wrapped phase of the object can be determined in the tangent form from the four phase-shifting moire fringe patterns using the four-step phase shifting algorithm.The continuous phase distribution can be obtained by the conventional unwrapping algorithm. Finally, experiments were conducted to prove the validity and feasibility of the proposed method. The results are analyzed and compared with those of Wang method, demonstrating that our method not only can expand the measurement scope, but also can improve accuracy.Comment: 14 pages,5 figures. ams.or

One shot profilometry using iterative two-step temporal phase-unwrapping

This paper reviews two techniques that have been recently published for 3D profilometry and proposes one shot profilometry using iterative two-step temporal phase-unwrapping by combining the composite fringe projection and the iterative two-step temporal phase unwrapping algorithm. In temporal phase unwrapping, many images with different frequency fringe pattern are needed to project which would take much time. In order to solve this problem, Ochoa proposed a phase unwrapping algorithm based on phase partitions using a composite fringe, which only needs projecting one composite fringe pattern with four kinds of frequency information to complete the process of 3D profilometry. However, we found that the fringe order determined through the construction of phase partitions tended to be imprecise. Recently, we proposed an iterative two-step temporal phase unwrapping algorithm, which can achieve high sensitivity and high precision shape measurement. But it needs multiple frames of fringe images which would take much time. In order to take into account both the speed and accuracy of 3D shape measurement, we get a new, and more accurate unwrapping method based on composite fringe pattern by combining these two techniques. This method not only retains the speed advantage of Ochoa's algorithm, but also greatly improves its measurement accuracy. Finally, the experimental evaluation is conducted to prove the validity of the proposed method, and the experimental results show that this method is feasible.Comment: 14 pages, 15 figure

Improved method for phase wraps reduction in profilometry

In order to completely eliminate, or greatly reduce the number of phase wraps in 2D wrapped phase map, Gdeisat et al. proposed an algorithm, which uses shifting the spectrum towards the origin. But the spectrum can be shifted only by an integer number, meaning that the phase wraps reduction is often not optimal. In addition, Gdeisat's method will take much time to make the Fourier transform, inverse Fourier transform, select and shift the spectral components. In view of the above problems, we proposed an improved method for phase wraps elimination or reduction. First, the wrapped phase map is padded with zeros, the carrier frequency of the projected fringe is determined by high resolution, which can be used as the moving distance of the spectrum. And then realize frequency shift in spatial domain. So it not only can enable the spectrum to be shifted by a rational number when the carrier frequency is not an integer number, but also reduce the execution time. Finally, the experimental results demonstrated that the proposed method is feasible.Comment: 16 pages, 15 figures, 1 table. arXiv admin note: text overlap with arXiv:1604.0723

Unsupervised CNN-Based DIC for 2D Displacement Measurement

Digital image correlation method is a non contact deformation measurement technique. Despite years of development, it is still difficult to solve the contradiction between calculation efficiency and seed point quantity.With the development of deep learning, the DIC algorithm based on deep learning provides a new solution for the problem of insufficient calculation efficiency in DIC.All supervised learning DIC methods requires a large set of high quality training set. However, obtaining such a dataset can be challenging and time consuming in generating ground truth. To fix the problem,we propose an unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image warp model is created to transform the target speckle image to the corresponding predicted reference speckle image by predicted 2D displacement map, the predicted reference speckle image is compared with the original reference speckle image to realize the unsupervised training of the CNN.The network's parameters are optimized using a composite loss function that incorporates both the Mean Squared Error and Pearson correlation coefficient.Our proposed method has a significant advantage of eliminating the need for extensive training data annotations. We conducted several experiments to demonstrate the validity and robustness of the proposed method. The experimental results demonstrate that our method can achieve can achieve accuracy comparable to previous supervised methods. The PyTorch code will be available at the following URL: https://github.com/fead1

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology