What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

Matching pedestrians across disjoint camera views, known as person re-identification (re-id), is a challenging problem that is of importance to visual recognition and surveillance. Most existing methods exploit local regions within spatial manipulation to perform matching in local correspondence. However, they essentially extract \emph{fixed} representations from pre-divided regions for each image and perform matching based on the extracted representation subsequently. For models in this pipeline, local finer patterns that are crucial to distinguish positive pairs from negative ones cannot be captured, and thus making them underperformed. In this paper, we propose a novel deep multiplicative integration gating function, which answers the question of \emph{what-and-where to match} for effective person re-id. To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way. The network comprises two Convolutional Neural Networks (CNNs) to extract convolutional activations, and generates relevant descriptors for pedestrian matching. This thus, leads to flexible representations for pair-wise images. To address \emph{where} to match, we combat the spatial misalignment by performing spatially recurrent pooling via a four-directional recurrent neural network to impose spatial dependency over all positions with respect to the entire image. The proposed network is designed to be end-to-end trainable to characterize local pairwise feature interactions in a spatially aligned manner. To demonstrate the superiority of our method, extensive experiments are conducted over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie

Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio

Time resolved characterization of a free-burning argon arc after ignition by optical emission spectroscopy

Time resolvedproperties of a free-burning argon arc after ignition have been characterized using optical spectroscopic method. After ignition, when the arc current keeps constant, the plasma temperature decreases with time at any position of the arc. The decrease of the plasma temperature is associated with the increase of the arc cathode surface temperature. It is suggested that the variation of the cathode surface temperature, which changes the current density distribution over the cathode surface, leads to the decrease of the plasma temperature in the free-burning arc after ignition

Modified Fowler–Milne method for the spectroscopic determination of thermal plasma temperature without the measurement of continuum radiation

A technique based on the Fowler-Milne method for the spectroscopic determination of thermal plasma temperatures without measuring continuum radiation is presented. This technique avoids the influence of continuum radiation with the combined line and continuum emission coefficients to derive the plasma temperatures. The amount of continuum emission coefficient is estimated by using an expression related to the Biberman factors. Parameters that affect the accuracy of the proposed technique and errors in the measured plasma temperatures are analyzed. It is shown that, by using the Ar I 696.5 nm line with a bandwidth of 3.27 nm without taking into account the continuum radiation, the plasma temperature measured will be lower on the order of up to 1000-3000 K for temperatures from 20,000 to 24,000 K. The theoretically predicted temperature errors are in good agreement with the experimental results, indicating that the proposed technique is reliable for plasma temperature measurement

Nonstationary Stochastic Seismic Response Analysis for Earth and Rockfill Dams

In this article, the time domain madal analysis technique for evaluating the nonstationary responses of linear systems is combined with the equivalent linearization approach for determining the stationary responses of nonlinear soil structures. A new analysis method is formed and the nonstationary responses of the earth and rockfill dam are calculated. The results from nonstationary analysis are compared with the ones from the stationary computations. The effectiveness of nonstationarity of input and output is investigated and the relevant conclusions are given