2,668 research outputs found
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality
reduction, with well-documented merits in various applications involving
high-dimensional data, including computer vision, preference measurement, and
bioinformatics. In this context, the fresh look advocated here permeates
benefits from variable selection and compressive sampling, to robustify PCA
against outliers. A least-trimmed squares estimator of a low-rank bilinear
factor analysis model is shown closely related to that obtained from an
-(pseudo)norm-regularized criterion encouraging sparsity in a matrix
explicitly modeling the outliers. This connection suggests robust PCA schemes
based on convex relaxation, which lead naturally to a family of robust
estimators encompassing Huber's optimal M-class as a special case. Outliers are
identified by tuning a regularization parameter, which amounts to controlling
sparsity of the outlier matrix along the whole robustification path of (group)
least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its
neat ties to robust statistics, the developed outlier-aware PCA framework is
versatile to accommodate novel and scalable algorithms to: i) track the
low-rank signal subspace robustly, as new data are acquired in real time; and
ii) determine principal components robustly in (possibly) infinite-dimensional
feature spaces. Synthetic and real data tests corroborate the effectiveness of
the proposed robust PCA schemes, when used to identify aberrant responses in
personality assessment surveys, as well as unveil communities in social
networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin
Video Registration in Egocentric Vision under Day and Night Illumination Changes
With the spread of wearable devices and head mounted cameras, a wide range of
application requiring precise user localization is now possible. In this paper
we propose to treat the problem of obtaining the user position with respect to
a known environment as a video registration problem. Video registration, i.e.
the task of aligning an input video sequence to a pre-built 3D model, relies on
a matching process of local keypoints extracted on the query sequence to a 3D
point cloud. The overall registration performance is strictly tied to the
actual quality of this 2D-3D matching, and can degrade if environmental
conditions such as steep changes in lighting like the ones between day and
night occur. To effectively register an egocentric video sequence under these
conditions, we propose to tackle the source of the problem: the matching
process. To overcome the shortcomings of standard matching techniques, we
introduce a novel embedding space that allows us to obtain robust matches by
jointly taking into account local descriptors, their spatial arrangement and
their temporal robustness. The proposal is evaluated using unconstrained
egocentric video sequences both in terms of matching quality and resulting
registration performance using different 3D models of historical landmarks. The
results show that the proposed method can outperform state of the art
registration algorithms, in particular when dealing with the challenges of
night and day sequences
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
One of the main open challenges in visual odometry (VO) is the robustness to
difficult illumination conditions or high dynamic range (HDR) environments. The
main difficulties in these situations come from both the limitations of the
sensors and the inability to perform a successful tracking of interest points
because of the bold assumptions in VO, such as brightness constancy. We address
this problem from a deep learning perspective, for which we first fine-tune a
Deep Neural Network (DNN) with the purpose of obtaining enhanced
representations of the sequences for VO. Then, we demonstrate how the insertion
of Long Short Term Memory (LSTM) allows us to obtain temporally consistent
sequences, as the estimation depends on previous states. However, the use of
very deep networks does not allow the insertion into a real-time VO framework;
therefore, we also propose a Convolutional Neural Network (CNN) of reduced size
capable of performing faster. Finally, we validate the enhanced representations
by evaluating the sequences produced by the two architectures in several
state-of-art VO algorithms, such as ORB-SLAM and DSO
Single and multiple stereo view navigation for planetary rovers
© Cranfield UniversityThis thesis deals with the challenge of autonomous navigation of the ExoMars rover.
The absence of global positioning systems (GPS) in space, added to the limitations
of wheel odometry makes autonomous navigation based on these two techniques - as
done in the literature - an inviable solution and necessitates the use of other approaches.
That, among other reasons, motivates this work to use solely visual data to solve the
robot’s Egomotion problem.
The homogeneity of Mars’ terrain makes the robustness of the low level image
processing technique a critical requirement. In the first part of the thesis, novel solutions
are presented to tackle this specific problem. Detection of robust features against
illumination changes and unique matching and association of features is a sought after
capability. A solution for robustness of features against illumination variation is proposed
combining Harris corner detection together with moment image representation.
Whereas the first provides a technique for efficient feature detection, the moment images
add the necessary brightness invariance. Moreover, a bucketing strategy is used
to guarantee that features are homogeneously distributed within the images. Then, the
addition of local feature descriptors guarantees the unique identification of image cues.
In the second part, reliable and precise motion estimation for the Mars’s robot is
studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous
Localisation And Mapping (VSLAM) is investigated, proposing enhancements
and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation
techniques are explored. Alternative photogrammetry reprojection concepts
are tested. Lastly, data fusion techniques are proposed to deal with the integration of
multiple stereo view data.
Our robust visual scheme allows good feature repeatability. Because of this,
dimensionality reduction of the feature data can be used without compromising the
overall performance of the proposed solutions for motion estimation. Also, the developed
Egomotion techniques have been extensively validated using both simulated and
real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot
motion estimation are introduced, presenting interesting benefits. The obtained results
prove the innovative methods presented here to be accurate and reliable approaches
capable to solve the Egomotion problem in a Mars environment
People counting using an overhead fisheye camera
As climate change concerns grow, the reduction of energy consumption is seen as one of many potential solutions. In the US, a considerable amount of energy is wasted in commercial buildings due to sub-optimal heating, ventilation and air conditioning that operate with no knowledge of the occupancy level in various rooms and open areas. In this thesis, I develop an approach to passive occupancy estimation that does not require occupants to carry any type of beacon, but instead uses an overhead camera with fisheye lens (360 by 180 degree field of view). The difficulty with fisheye images is that occupants may appear not only in the upright position, but also upside-down, horizontally and diagonally, and thus algorithms developed for typical side-mounted, standard-lens cameras tend to fail. As the top-performing people detection algorithms today use deep learning, a logical step would be to develop and train a new neural-network model. However, there exist no large fisheye-image datasets with person annotations to facilitate training a new model. Therefore, I developed two people-counting methods that leverage YOLO (version 3), a state-of-the-art object detection method trained on standard datasets. In one approach, YOLO is applied to 24 rotated and highly-overlapping windows, and the results are post-processed to produce a people count. In the other approach, regions of interest are first extracted via background subtraction and only windows that include such regions are supplied to YOLO and post-processed. I carried out extensive experimental evaluation of both algorithms and showed their superior performance compared to a benchmark method
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