1,879 research outputs found
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Although it has been widely discussed in video surveillance, background
subtraction is still an open problem in the context of complex scenarios, e.g.,
dynamic backgrounds, illumination variations, and indistinct foreground
objects. To address these challenges, we propose an effective background
subtraction method by learning and maintaining an array of dynamic texture
models within the spatio-temporal representations. At any location of the
scene, we extract a sequence of regular video bricks, i.e. video volumes
spanning over both spatial and temporal domain. The background modeling is thus
posed as pursuing subspaces within the video bricks while adapting the scene
variations. For each sequence of video bricks, we pursue the subspace by
employing the ARMA (Auto Regressive Moving Average) Model that jointly
characterizes the appearance consistency and temporal coherence of the
observations. During online processing, we incrementally update the subspaces
to cope with disturbances from foreground objects and scene changes. In the
experiments, we validate the proposed method in several complex scenarios, and
show superior performances over other state-of-the-art approaches of background
subtraction. The empirical studies of parameter setting and component analysis
are presented as well.Comment: 12 pages, 7 figure
Background Subtraction via Fast Robust Matrix Completion
Background subtraction is the primary task of the majority of video
inspection systems. The most important part of the background subtraction which
is common among different algorithms is background modeling. In this regard,
our paper addresses the problem of background modeling in a computationally
efficient way, which is important for current eruption of "big data" processing
coming from high resolution multi-channel videos. Our model is based on the
assumption that background in natural images lies on a low-dimensional
subspace. We formulated and solved this problem in a low-rank matrix completion
framework. In modeling the background, we benefited from the in-face extended
Frank-Wolfe algorithm for solving a defined convex optimization problem. We
evaluated our fast robust matrix completion (fRMC) method on both background
models challenge (BMC) and Stuttgart artificial background subtraction (SABS)
datasets. The results were compared with the robust principle component
analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both
solved by inexact augmented Lagrangian multiplier (IALM). The results showed
faster computation, at least twice as when IALM solver is used, while having a
comparable accuracy even better in some challenges, in subtracting the
backgrounds in order to detect moving objects in the scene
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
Recent research on problem formulations based on decomposition into low-rank
plus sparse matrices shows a suitable framework to separate moving objects from
the background. The most representative problem formulation is the Robust
Principal Component Analysis (RPCA) solved via Principal Component Pursuit
(PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix.
However, similar robust implicit or explicit decompositions can be made in the
following problem formulations: Robust Non-negative Matrix Factorization
(RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust
Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal
of these similar problem formulations is to obtain explicitly or implicitly a
decomposition into low-rank matrix plus additive matrices. In this context,
this work aims to initiate a rigorous and comprehensive review of the similar
problem formulations in robust subspace learning and tracking based on
decomposition into low-rank plus additive matrices for testing and ranking
existing algorithms for background/foreground separation. For this, we first
provide a preliminary review of the recent developments in the different
problem formulations which allows us to define a unified view that we called
Decomposition into Low-rank plus Additive Matrices (DLAM). Then, we examine
carefully each method in each robust subspace learning/tracking frameworks with
their decomposition, their loss functions, their optimization problem and their
solvers. Furthermore, we investigate if incremental algorithms and real-time
implementations can be achieved for background/foreground separation. Finally,
experimental results on a large-scale dataset called Background Models
Challenge (BMC 2012) show the comparative performance of 32 different robust
subspace learning/tracking methods.Comment: 121 pages, 5 figures, submitted to Computer Science Review. arXiv
admin note: text overlap with arXiv:1312.7167, arXiv:1109.6297,
arXiv:1207.3438, arXiv:1105.2126, arXiv:1404.7592, arXiv:1210.0805,
arXiv:1403.8067 by other authors, Computer Science Review, November 201
Quantification of Morphological Features in Non-Contrast Ultrasound Microvasculature Imaging
Morphological features of small vessels provide invaluable information
regarding underlying tissue, especially in cancerous tumors. This paper
introduces methods for obtaining quantitative morphological features from
microvasculature images obtained by non-contrast ultrasound imaging. Those
images suffer from the artifact that limit quantitative analysis of the vessel
morphological features. In this paper we introduce processing steps to increase
accuracy of the morphological assessment for quantitative vessel analysis in
presence of these artifact. Specifically, artificats are reduced by additional
filtering and vessel segments obtained by skeletonization of the regularized
microvasculature images are further analyzed to satisfy additional constraints,
such as diameter, and length of the vessel segments. Measurement of some
morphological metrics, such as tortuosity, depends on preserving large vessel
trunks that may be broken down into multiple branches. We propose two methods
to address this problem. In the first method, small vessel segments are
suppressed in the vessel filtering process via adjusting the size scale of the
regularization. Hence, tortuosity of the large trunks can be more accurately
estimated by preserving longer vessel segments. In the second approach, small
connected vessel segments are removed by a combination of morphological erosion
and dilation operations on the segmented vasculature images. These methods are
tested on representative in vivo images of breast lesion microvasculature, and
the outcomes are discussed. This paper provides a tool for quantification of
microvasculature image from non-contrast ultrasound imaging may result in
potential biomarkers for diagnosis of some diseases.Comment: 32 pages, 18 figures, 2 table
COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
Extracting moving objects from a video sequence and estimating the background
of each individual image are fundamental issues in many practical applications
such as visual surveillance, intelligent vehicle navigation, and traffic
monitoring. Recently, some methods have been proposed to detect moving objects
in a video via low-rank approximation and sparse outliers where the background
is modeled with the computed low-rank component of the video and the foreground
objects are detected as the sparse outliers in the low-rank approximation. All
of these existing methods work in a batch manner, preventing them from being
applied in real time and long duration tasks. In this paper, we present an
online sequential framework, namely contiguous outliers representation via
online low-rank approximation (COROLA), to detect moving objects and learn the
background model at the same time. We also show that our model can detect
moving objects with a moving camera. Our experimental evaluation uses simulated
data and real public datasets and demonstrates the superior performance of
COROLA in terms of both accuracy and execution time.Comment: 37 pages, 10 figure
Low-Rank Modeling and Its Applications in Image Analysis
Low-rank modeling generally refers to a class of methods that solve problems
by representing variables of interest as low-rank matrices. It has achieved
great success in various fields including computer vision, data mining, signal
processing and bioinformatics. Recently, much progress has been made in
theories, algorithms and applications of low-rank modeling, such as exact
low-rank matrix recovery via convex programming and matrix completion applied
to collaborative filtering. These advances have brought more and more
attentions to this topic. In this paper, we review the recent advance of
low-rank modeling, the state-of-the-art algorithms, and related applications in
image analysis. We first give an overview to the concept of low-rank modeling
and challenging problems in this area. Then, we summarize the models and
algorithms for low-rank matrix recovery and illustrate their advantages and
limitations with numerical experiments. Next, we introduce a few applications
of low-rank modeling in the context of image analysis. Finally, we conclude
this paper with some discussions.Comment: To appear in ACM Computing Survey
Wavelet subspace decomposition of thermal infrared images for defect detection in artworks
Monitoring the health of ancient artworks requires adequate prudence because
of the sensitive nature of these materials. Classical techniques for
identifying the development of faults rely on acoustic testing. These
techniques, being invasive, may result in causing permanent damage to the
material, especially if the material is inspected periodically. Non destructive
testing has been carried out for different materials since long. In this
regard, non-invasive systems were developed based on infrared thermometry
principle to identify the faults in artworks. The test artwork is heated and
the thermal response of the different layers is captured with the help of a
thermal infrared camera. However, prolonged heating risks overheating and thus
causing damage to artworks and an alternate approach is to use pseudo-random
binary sequence excitations. The faults in the artwork, though, cannot be
detected on the captured images, especially if their strength is weak. The
weaker faults are either masked by the stronger ones, by the pictorial layer of
the artwork or by the non-uniform heating. This work addresses the detection
and localization of the faults through a wavelet based subspace decomposition
scheme. The proposed scheme, on one hand, allows to remove the background
while, on the other hand, removes the undesired high frequency noise. It is
shown that the detection parameter is proportional to the diameter and the
depth of the fault. A criterion is proposed to select the optimal wavelet basis
along with suitable level selection for wavelet decomposition and
reconstruction. The proposed approach is tested on a laboratory developed test
sample with known fault locations and dimensions as well as real artworks. A
comparison with a previously reported method demonstrates the efficacy of the
proposed approach for fault detection in artworks
Phase-Optimized K-SVD for Signal Extraction from Underdetermined Multichannel Sparse Mixtures
We propose a novel sparse representation for heavily underdetermined
multichannel sound mixtures, i.e., with much more sources than microphones. The
proposed approach operates in the complex Fourier domain, thus preserving
spatial characteristics carried by phase differences. We derive a
generalization of K-SVD which jointly estimates a dictionary capturing both
spectral and spatial features, a sparse activation matrix, and all
instantaneous source phases from a set of signal examples. The dictionary can
then be used to extract the learned signal from a new input mixture. The method
is applied to the challenging problem of ego-noise reduction for robot
audition. We demonstrate its superiority relative to conventional
dictionary-based techniques using recordings made in a real room
Implementation of face recognition in a mobile robot with RGBD images
The objective of this work is to contribute to developing a system for person identification through the analysis of an RGBD image of the face of the person in front of an Assistant Personal Robot (APR).
To accomplish the purpose, the state-of-the-art works related to this subject will be studied, focusing on subject recognition but also on gender and age identification. Those works will be evaluated in advantages and disadvantages and finally an approach will be chosen to be implemented in this work.
The camera used in this work is the Creative Senz3D Camera which provides RGB, infrared and depth simultaneous information; the RGB image without background is also processed and obtained, furthermore the camera provides the world 3D points. In the final planned application, the APR will capture face images trough the 3D camera and they will be used to identify the person in front of the robot
Intelligent Measurement Analysis on Single Cell Raman Images for the Diagnosis of Follicular Thyroid Carcinoma
Inter-observer variability and cancer over-diagnosis are emerging clinical
problems, and there is a strong necessity to support the standards histological
and cytological evaluations by biochemical composition information. Over the
past decades, there has been a very active research in the development of Raman
spectroscopy techniques for oncological applications and large scale clinical
diagnosis. A major issue that has received a lot of attention in the Raman
literature is the fact that variations in instrumental responses and intrinsic
spectral backgrounds over different days of measurements or devices creates
strong inconsistency of Raman intensity spectra over the various experimental
condition, thus making the use of Raman spectroscopy on a large scale and
reproductive basis difficult. We explore different methods to tackle this
inconsistency and show that regular preprocessing methods such as baseline
correction, normalization or wavelet transformation are inefficient on our
datasets. We find that subtracting the mean background spectrum estimated by
identifying non-cell regions in Raman images makes the data more consistent. As
a proof of concept, we employ our single-cell Raman Imaging protocol to
diagnosis challenging follicular lesions, that is known to be particularly
difficult due to the lack of obvious morphological and cytological criteria for
malignancy. We explore dimensionality reduction with both PCA and feature
selection methods, and classification is then performed at the single cell
level with standard classifiers such as k Nearest Neighbors or Random Forest.
We investigate Raman hyperspectral images from FTC133, RO82W-1 and NthyOri 3-1
cell lines and show that the chemical information for the diagnosis is mostly
contained in the cytoplasm. We also reveal some important wavenumber for
malignancy, that can be associated mainly to lipids, cytochrome and
phenylalanine.Comment: The manuscript's content is currently being discussed with the
co-authors before submissio
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