32,238 research outputs found
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
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