arXiv subjects

Di Huang

Publications and source records attributed to Di Huang.

258 records · Page 15Linked to original sources

arXiv2017

Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images

In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner. Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.

cs.CV↗

arXiv2017

Gate tunable third-order nonlinear optical response of massless Dirac fermions in graphene

Materials with massless Dirac fermions can possess exceptionally strong and widely tunable optical nonlinearities. Experiments on graphene monolayer have indeed found very large third-order nonlinear responses, but the reported variation of the nonlinear optical coefficient by orders of magnitude is not yet understood. A large part of the difficulty is the lack of information on how doping or chemical potential affects the different nonlinear optical processes. Here we report the first experimental study, in corroboration with theory, on third harmonic generation (THG) and four-wave mixing (FWM) in graphene that has its chemical potential tuned by ion-gel gating. THG was seen to have enhanced by ~30 times when pristine graphene was heavily doped, while difference-frequency FWM appeared just the opposite. The latter was found to have a strong divergence toward degenerate FWM in undoped graphene, leading to a giant third-order nonlinearity. These truly amazing characteristics of graphene come from the possibility to gate-control the chemical potential, which selectively switches on and off one- and multi-photon resonant transitions that coherently contribute to the optical nonlinearity, and therefore can be utilized to develop graphene-based nonlinear optoelectronic devices.

cond-mat.mes-hall↗

arXiv2017

Dark Matter Results From 54-Ton-Day Exposure of PandaX-II Experiment

We report a new search of weakly interacting massive particles (WIMPs) using the combined low background data sets in 2016 and 2017 from the PandaX-II experiment in China. The latest data set contains a new exposure of 77.1 live day, with the background reduced to a level of 0.8$\times10^{-3}$ evt/kg/day, improved by a factor of 2.5 in comparison to the previous run in 2016. No excess events were found above the expected background. With a total exposure of 5.4$\times10^4$ kg day, the most stringent upper limit on spin-independent WIMP-nucleon cross section was set for a WIMP with mass larger than 100 GeV/c$^2$, with the lowest exclusion at 8.6$\times10^{-47}$ cm$^2$ at 40 GeV/c$^2$.

astro-ph.CO↗

arXiv2017

Defect detection for patterned fabric images based on GHOG and low-rank decomposition

In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper. Defect-free pattern fabric images have the specified direction, while defects damage their regularity of direction. Therefore, a direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor and HOG, which is extremely valuable for localizing the defect region. Upon devising a powerful directional descriptor, an efficient low-rank decomposition model is constructed to divide the matrix generated by the directional feature extracted from image blocks into a low-rank matrix (background information) and a sparse matrix (defect information). A nonconvex log det(.) as a smooth surrogate function for the rank instead of the nuclear norm is also exploited to improve the efficiency of the low-rank model. Moreover, the computational efficiency is further improved by utilizing the alternative direction method of multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix is segmented via the optimal threshold algorithm to locate the defect regions. Experimental results show that the proposed method can effectively detect patterned fabric defects and outperform the state-of-the-art methods.

cs.CV↗

arXiv2015

Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation

Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue by using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that handle the facial texture integrally, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that change gradually over time. It then merely transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. Additionally, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.

cs.CV↗

arXiv2014

The variability of crater identification among expert and community crater analysts

The identification of impact craters on planetary surfaces provides important information about their geological history. Most studies have relied on individual analysts who map and identify craters and interpret crater statistics. However, little work has been done to determine how the counts vary as a function of technique, terrain, or between researchers. Furthermore, several novel internet-based projects ask volunteers with little to no training to identify craters, and it was unclear how their results compare against the typical professional researcher. To better understand the variation among experts and to compare with volunteers, eight professional researchers have identified impact features in two separate regions of the moon. Small craters (diameters ranging from 10 m to 500 m) were measured on a lunar mare region and larger craters (100s m to a few km in diameter) were measured on both lunar highlands and maria. Volunteer data were collected for the small craters on the mare. Our comparison shows that the level of agreement among experts depends on crater diameter, number of craters per diameter bin, and terrain type, with differences of up to $\sim\pm45%$. We also found artifacts near the minimum crater diameter that was studied. These results indicate that caution must be used in most cases when interpreting small variations in crater size-frequency distributions and for craters $\le10$ pixels across. Because of the natural variability found, projects that emphasize many people identifying craters on the same area and using a consensus result are likely to yield the most consistent and robust information.

astro-ph.EP↗