Micro Fourier Transform Profilometry (μ\muFTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $\mu$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.Comment: This manuscript was originally submitted on 30th January 1

Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even in the presence of different types of error sources, e.g., intensity noise, low fringe modulation, and projector nonlinearity. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU

Genetic linkage maps of Pinus koraiensis Sieb. et Zucc. based on AFLP markers

Genetic linkage maps provide essential information for molecular breeding. In this paper, the genetic linkage map of Pinus koraiensis was constructed using an F1 progeny of 88 individuals. One hundred and thirty (130) of molecular markers were mapped onto 6 linkage groups, 4 triples and 15 pairs at the linkage criteria LOD 4.0. Nine primer combinations were applied to map construction. The consensus map gained covers 620.909 cM, with an average marker spacing of 4.776 cM. The presented map provides crucial information for future genomic studies of P. koraiensis, in particular for QTL (quantitative trait loci) mapping of economically important breeding target traits.Keywords: Genetic mapping, Korean pine, linkage map, marker-aided selectionAfrican Journal of Biotechnology Vol. 9(35), pp. 5659-5664, 30 August, 201

Edge Detection of Concrete Mesostructure Based on DIS Operator

Aggregate edge detection is the basis of creating concrete mesoscale model, which is applied to analyze concrete mesoscale characteristics. A concrete digital image edge detection method using DIS operator is presented in this paper. Mean filter, multi-scale filter, and Gaussian filter are compared on the effect of concrete image noise reduction. Based on the result, Gaussian filter is the most optimum method to reduce image noise and remain aggregate edge distinct. Sobel operator, Laplacian operator, and DIS operator are applied respectively to detect the aggregate edge on Gaussian filter processed images. Based on the experiment, DIS operator outperforms other two operators in the veracity and integrity of edge detection. It is concluded that using Gaussian filter and DIS operator for edge segmentation can provide geometrical models for FEM analysis

Revealing the cosmic web dependent halo bias

Halo bias is the one of the key ingredients of the halo models. It was shown at a given redshift to be only dependent, to the first order, on the halo mass. In this study, four types of cosmic web environments: clusters, filaments, sheets and voids are defined within a state of the art high resolution $N$-body simulation. Within those environments, we use both halo-dark matter cross-correlation and halo-halo auto correlation functions to probe the clustering properties of halos. The nature of the halo bias differs strongly among the four different cosmic web environments we describe. With respect to the overall population, halos in clusters have significantly lower biases in the {$10^{11.0}\sim 10^{13.5}h^{-1}\rm M_\odot$} mass range. In other environments however, halos show extremely enhanced biases up to a factor 10 in voids for halos of mass {$\sim 10^{12.0}h^{-1}\rm M_\odot$}. Such a strong cosmic web environment dependence in the halo bias may play an important role in future cosmological and galaxy formation studies. Within this cosmic web framework, the age dependency of halo bias is found to be only significant in clusters and filaments for relatively small halos \la 10^{12.5}\msunh.Comment: 14 pages, 14 figures, ApJ accepte

Mapping the real space distributions of galaxies in SDSS DR7: I. Two Point Correlation Functions

Using a method to correct redshift space distortion (RSD) for individual galaxies, we mapped the real space distributions of galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7). We use an ensemble of mock catalogs to demonstrate the reliability of our method. Here as the first paper in a series, we mainly focus on the two point correlation function (2PCF) of galaxies. Overall the 2PCF measured in the reconstructed real space for galaxies brighter than $^{0.1}{\rm M}_r-5\log h=-19.0$ agrees with the direct measurement to an accuracy better than the measurement error due to cosmic variance, if the reconstruction uses the correct cosmology. Applying the method to the SDSS DR7, we construct a real space version of the main galaxy catalog, which contains 396,068 galaxies in the North Galactic Cap with redshifts in the range $0.01 \leq z \leq 0.12$. The Sloan Great Wall, the largest known structure in the nearby Universe, is not as dominant an over-dense structure as appears to be in redshift space. We measure the 2PCFs in reconstructed real space for galaxies of different luminosities and colors. All of them show clear deviations from single power-law forms, and reveal clear transitions from 1-halo to 2-halo terms. A comparison with the corresponding 2PCFs in redshift space nicely demonstrates how RSDs boost the clustering power on large scales (by about $40-50\%$ at scales $\sim 10 h^{-1}{\rm {Mpc}}$) and suppress it on small scales (by about $70-80\%$ at a scale of $0.3 h^{-1}{\rm {Mpc}}$).Comment: 19 pages, 13 figure