21,571 research outputs found
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested
information and knowledge from remote sensing (RS) images with high efficiency
and accuracy. However, with the development of RS technologies, RS system
provide images with higher spatial and temporal resolution and more spectral
channels than before, and it is inefficient and almost impossible to manually
interpret these images. Thus, it is of great interests to explore automatic and
intelligent algorithms to quickly process such massive RS data with high
accuracy. This thesis targets to develop some efficient information extraction
algorithms for RS images, by relying on the advanced technologies in machine
learning. More precisely, we adopt the manifold learning algorithms as the
mainline and unify the regularization theory, tensor-based method, sparse
learning and transfer learning into the same framework. The main contributions
of this thesis are as follows.Comment: 7 page
Adversarial Discriminative Heterogeneous Face Recognition
The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This framework integrates cross-spectral face hallucination and
discriminative feature learning into an end-to-end adversarial network. In the
pixel space, we make use of generative adversarial networks to perform
cross-spectral face hallucination. An elaborate two-path model is introduced to
alleviate the lack of paired images, which gives consideration to both global
structures and local textures. In the feature space, an adversarial loss and a
high-order variance discrepancy loss are employed to measure the global and
local discrepancy between two heterogeneous distributions respectively. These
two losses enhance domain-invariant feature learning and modality independent
noise removing. Experimental results on three NIR-VIS databases show that our
proposed approach outperforms state-of-the-art HFR methods, without requiring
of complex network or large-scale training dataset
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie
Target-based Hyperspectral Demixing via Generalized Robust PCA
Localizing targets of interest in a given hyperspectral (HS) image has
applications ranging from remote sensing to surveillance. This task of target
detection leverages the fact that each material/object possesses its own
characteristic spectral response, depending upon its composition. As
of different materials are often correlated, matched
filtering based approaches may not be appropriate in this case. In this work,
we present a technique to localize targets of interest based on their spectral
signatures. We also present the corresponding recovery guarantees, leveraging
our recent theoretical results. To this end, we model a HS image as a
superposition of a low-rank component and a dictionary sparse component,
wherein the dictionary consists of the known characteristic
spectral responses of the target we wish to localize. Finally, we analyze the
performance of the proposed approach via experimental validation on real HS
data for a classification task, and compare it with related techniques.Comment: 5 Pages; Index Terms - Hyperspectral imaging, Robust-PCA, Dictionary
Sparse, Matrix Demixing, Target Localization, and Remote Sensing. arXiv admin
note: substantial text overlap with arXiv:1902.1023
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
It remains a huge challenge to design effective and efficient trackers under
complex scenarios, including occlusions, illumination changes and pose
variations. To cope with this problem, a promising solution is to integrate the
temporal consistency across consecutive frames and multiple feature cues in a
unified model. Motivated by this idea, we propose a novel correlation
filter-based tracker in this work, in which the temporal relatedness is
reconciled under a multi-task learning framework and the multiple feature cues
are modeled using a multi-view learning approach. We demonstrate the resulting
regression model can be efficiently learned by exploiting the structure of
blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm
is developed thereafter for efficient online tracking. Meanwhile, we
incorporate an adaptive scale estimation mechanism to strengthen the stability
of scale variation tracking. We implement our tracker using two types of
features and test it on two benchmark datasets. Experimental results
demonstrate the superiority of our proposed approach when compared with other
state-of-the-art trackers. project homepage
http://bmal.hust.edu.cn/project/KMF2JMTtracking.htmlComment: This paper has been accepted by IEEE Transactions on Circuits and
Systems for Video Technology. The MATLAB code of our method is available from
our project homepage http://bmal.hust.edu.cn/project/KMF2JMTtracking.htm
Light Ears: Information Leakage via Smart Lights
Modern Internet-enabled smart lights promise energy efficiency and many
additional capabilities over traditional lamps. However, these connected lights
create a new attack surface, which can be maliciously used to violate users'
privacy and security. In this paper, we design and evaluate novel attacks that
take advantage of light emitted by modern smart bulbs in order to infer users'
private data and preferences. The first two attacks are designed to infer
users' audio and video playback by a systematic observation and analysis of the
multimedia-visualization functionality of smart light bulbs. The third attack
utilizes the infrared capabilities of such smart light bulbs to create a
covert-channel, which can be used as a gateway to exfiltrate user's private
data out of their secured home or office network. A comprehensive evaluation of
these attacks in various real-life settings confirms their feasibility and
affirms the need for new privacy protection mechanisms
PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
Thermal infrared (TIR) pedestrian tracking is one of the important components
among numerous applications of computer vision, which has a major advantage: it
can track pedestrians in total darkness. The ability to evaluate the TIR
pedestrian tracker fairly, on a benchmark dataset, is significant for the
development of this field. However, there is not a benchmark dataset. In this
paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian
tracker evaluation. The dataset includes 60 thermal sequences with manual
annotations. Each sequence has nine attribute labels for the attribute based
evaluation. In addition to the dataset, we carry out the large-scale evaluation
experiments on our benchmark dataset using nine publicly available trackers.
The experimental results help us understand the strengths and weaknesses of
these trackers.In addition, in order to gain more insight into the TIR
pedestrian tracker, we divide its functions into three components: feature
extractor, motion model, and observation model. Then, we conduct three
comparison experiments on our benchmark dataset to validate how each component
affects the tracker's performance. The findings of these experiments provide
some guidelines for future research. The dataset and evaluation toolkit can be
downloaded at {https://github.com/QiaoLiuHit/PTB-TIR_Evaluation_toolkit}.Comment: 10 pages,IEEE Transactions on Multimedia (2019
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
Infrared small target detection is an important fundamental task in the
infrared system. Therefore, many infrared small target detection methods have
been proposed, in which the low-rank model has been used as a powerful tool.
However, most low-rank-based methods assign the same weights for different
singular values, which will lead to inaccurate background estimation.
Considering that different singular values have different importance and should
be treated discriminatively, in this paper, we propose a non-convex tensor
low-rank approximation (NTLA) method for infrared small target detection. In
our method, NTLA regularization adaptively assigns different weights to
different singular values for accurate background estimation. Based on the
proposed NTLA, we propose asymmetric spatial-temporal total variation (ASTTV)
regularization to achieve more accurate background estimation in complex
scenes. Compared with the traditional total variation approach, ASTTV exploits
different smoothness intensities for spatial and temporal regularization. We
design an efficient algorithm to find the optimal solution of our method.
Compared with some state-of-the-art methods, the proposed method achieves an
improvement in terms of various evaluation metrics. Extensive experimental
results in various complex scenes demonstrate that our method has strong
robustness and low false-alarm rate. Code is available at
https://github.com/LiuTing20a/ASTTV-NTLA.Comment: This paper is accepted by IEEE Transactions on Geoscience and Remote
Sensin
Recommended from our members
Attention during natural vision warps semantic representation across the human brain.
Little is known about how attention changes the cortical representation of sensory information in humans. On the basis of neurophysiological evidence, we hypothesized that attention causes tuning changes to expand the representation of attended stimuli at the cost of unattended stimuli. To investigate this issue, we used functional magnetic resonance imaging to measure how semantic representation changed during visual search for different object categories in natural movies. We found that many voxels across occipito-temporal and fronto-parietal cortex shifted their tuning toward the attended category. These tuning shifts expanded the representation of the attended category and of semantically related, but unattended, categories, and compressed the representation of categories that were semantically dissimilar to the target. Attentional warping of semantic representation occurred even when the attended category was not present in the movie; thus, the effect was not a target-detection artifact. These results suggest that attention dynamically alters visual representation to optimize processing of behaviorally relevant objects during natural vision
A Survey on Periocular Biometrics Research
Periocular refers to the facial region in the vicinity of the eye, including
eyelids, lashes and eyebrows. While face and irises have been extensively
studied, the periocular region has emerged as a promising trait for
unconstrained biometrics, following demands for increased robustness of face or
iris systems. With a surprisingly high discrimination ability, this region can
be easily obtained with existing setups for face and iris, and the requirement
of user cooperation can be relaxed, thus facilitating the interaction with
biometric systems. It is also available over a wide range of distances even
when the iris texture cannot be reliably obtained (low resolution) or under
partial face occlusion (close distances). Here, we review the state of the art
in periocular biometrics research. A number of aspects are described,
including: i) existing databases, ii) algorithms for periocular detection
and/or segmentation, iii) features employed for recognition, iv) identification
of the most discriminative regions of the periocular area, v) comparison with
iris and face modalities, vi) soft-biometrics (gender/ethnicity
classification), and vii) impact of gender transformation and plastic surgery
on the recognition accuracy. This work is expected to provide an insight of the
most relevant issues in periocular biometrics, giving a comprehensive coverage
of the existing literature and current state of the art.Comment: Published in Pattern Recognition Letter
- …