Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

Data-Driven Time-Frequency Analysis

In this paper, we introduce a new adaptive data analysis method to study trend and instantaneous frequency of nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently developed compressed (compressive) sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form $\{a(t) \cos(\theta(t))\}$, where $a \in V(\theta)$, $V(\theta)$ consists of the functions smoother than $\cos(\theta(t))$ and $\theta'\ge 0$. This problem can be formulated as a nonlinear $L^0$ optimization problem. In order to solve this optimization problem, we propose a nonlinear matching pursuit method by generalizing the classical matching pursuit for the $L^0$ optimization problem. One important advantage of this nonlinear matching pursuit method is it can be implemented very efficiently and is very stable to noise. Further, we provide a convergence analysis of our nonlinear matching pursuit method under certain scale separation assumptions. Extensive numerical examples will be given to demonstrate the robustness of our method and comparison will be made with the EMD/EEMD method. We also apply our method to study data without scale separation, data with intra-wave frequency modulation, and data with incomplete or under-sampled data

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

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

Linearizing nonlinear optics

In the framework of linear optics, light fields do not interact with each other in a medium. Yet, when their field amplitude becomes comparable to the electron binding energies of matter, the nonlinear motion of these electrons emits new dipole radiation whose amplitude, frequency and phase differ from the incoming fields. Such high fields are typically achieved with ultra-short, femtosecond (1fs = 10-15 sec.) laser pulses containing very broad frequency spectra. Here, the matter not only couples incoming and outgoing fields but also causes different spectral components to interact and mix through a convolution process. In this contribution, we describe how frequency domain nonlinear optics overcomes the shortcomings arising from this convolution in conventional time domain nonlinear optics1. We generate light fields with previously inaccessible properties because the uncontrolled coupling of amplitudes and phases is turned off. For example, arbitrary phase functions are transferred linearly to the second harmonic frequency while maintaining the exact shape of the input power spectrum squared. This nonlinear control over output amplitudes and phases opens up new avenues for applications based on manipulation of coherent light fields. One could investigate c.f. the effect of tailored nonlinear perturbations on the evolution of discrete eigenmodes in Anderson localization2. Our approach might also open a new chapter for controlling electronic and vibrational couplings in 2D-spectroscopy3 by the geometrical optical arrangement

Simulated LSST Survey of RR Lyrae Stars throughout the Local Group

We report on a study to determine the efficiency of the Large Synoptic Survey Telescope (LSST) to recover the periods, brightnesses, and shapes of RR Lyrae stars' light curves in the volume extending to heliocentric distances of 1.5 Mpc. We place the smoothed light curves of 30 type ab and 10 type c RR Lyrae stars in 1007 fields across the sky, each of which represents a different realization of the LSST sampling cadences, and that sample five particular observing modes. A light curve simulation tool was used to sample the idealized RR Lyrae stars' light curves, returning each as it would have been observed by LSST, including realistic photometric scatter, limiting magnitudes, and telescope downtime. We report here the period, brightness, and light curve shape recovery as a function of apparent magnitude and for survey lengths varying from 1 to 10 years. We find that 10 years of LSST data are sufficient to recover the pulsation periods with a fractional precision of ~10^(–5) for ≥90% of ab stars within ≈360 kpc of the Sun in Universal Cadence fields and out to ≈760 kpc for Deep Drilling fields. The 50% completeness level extends to ≈600 kpc and ≈1.0 Mpc for the same fields, respectively. For virtually all stars that had their periods recovered, their light curve shape parameter φ_31 was recovered with sufficient precision to also recover photometric metallicities to within 0.14 dex (the systematic error in the photometric relations). With RR Lyrae stars' periods and metallicities well measured to these distances, LSST will be able to search for halo streams and dwarf satellite galaxies over half of the Local Group, informing galaxy formation models and providing essential data for mapping the Galactic potential. This study also informs the LSST science operations plan for optimizing observing strategies to achieve particular science goals. We additionally present a new [Fe/H]-φ_31 photometric relation in the r band and a new and generally useful metric for defining period recovery for time domain surveys