62 research outputs found
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
View synthesis is a process for generating novel views from a scene which has
been recorded with a 3-D camera setup. It has important applications in 3-D
post-production and 2-D to 3-D conversion. However, a central problem in the
generation of novel views lies in the handling of disocclusions. Background
content, which was occluded in the original view, may become unveiled in the
synthesized view. This leads to missing information in the generated view which
has to be filled in a visually plausible manner. We present an inpainting
algorithm for disocclusion filling in synthesized views based on Markov random
fields and efficient belief propagation. We compare the result to two
state-of-the-art algorithms and demonstrate a significant improvement in image
quality.Comment: Published version:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=673843
Analysis Operator Learning and Its Application to Image Reconstruction
Exploiting a priori known structural information lies at the core of many
image reconstruction methods that can be stated as inverse problems. The
synthesis model, which assumes that images can be decomposed into a linear
combination of very few atoms of some dictionary, is now a well established
tool for the design of image reconstruction algorithms. An interesting
alternative is the analysis model, where the signal is multiplied by an
analysis operator and the outcome is assumed to be the sparse. This approach
has only recently gained increasing interest. The quality of reconstruction
methods based on an analysis model severely depends on the right choice of the
suitable operator.
In this work, we present an algorithm for learning an analysis operator from
training images. Our method is based on an -norm minimization on the
set of full rank matrices with normalized columns. We carefully introduce the
employed conjugate gradient method on manifolds, and explain the underlying
geometry of the constraints. Moreover, we compare our approach to
state-of-the-art methods for image denoising, inpainting, and single image
super-resolution. Our numerical results show competitive performance of our
general approach in all presented applications compared to the specialized
state-of-the-art techniques.Comment: 12 pages, 7 figure
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Modern automation systems rely on closed loop control, wherein a controller
interacts with a controlled process, based on observations. These systems are
increasingly complex, yet most controllers are linear
Proportional-Integral-Derivative (PID) controllers. PID controllers perform
well on linear and near-linear systems but their simplicity is at odds with the
robustness required to reliably control complex processes. Modern machine
learning offers a way to extend PID controllers beyond their linear
capabilities by using neural networks. However, such an extension comes at the
cost of losing stability guarantees and controller interpretability. In this
paper, we examine the utility of extending PID controllers with recurrent
neural networks-namely, General Dynamic Neural Networks (GDNN); we show that
GDNN (neural) PID controllers perform well on a range of control systems and
highlight how they can be a scalable and interpretable option for control
systems. To do so, we provide an extensive study using four benchmark systems
that represent the most common control engineering benchmarks. All control
benchmarks are evaluated with and without noise as well as with and without
disturbances. The neural PID controller performs better than standard PID
control in 15 of 16 tasks and better than model-based control in 13 of 16
tasks. As a second contribution, we address the lack of interpretability that
prevents neural networks from being used in real-world control processes. We
use bounded-input bounded-output stability analysis to evaluate the parameters
suggested by the neural network, thus making them understandable. This
combination of rigorous evaluation paired with better interpretability is an
important step towards the acceptance of neural-network-based control
approaches. It is furthermore an important step towards interpretable and
safely applied artificial intelligence
Reinforcement Learning with Ensemble Model Predictive Safety Certification
Reinforcement learning algorithms need exploration to learn. However,
unsupervised exploration prevents the deployment of such algorithms on
safety-critical tasks and limits real-world deployment. In this paper, we
propose a new algorithm called Ensemble Model Predictive Safety Certification
that combines model-based deep reinforcement learning with tube-based model
predictive control to correct the actions taken by a learning agent, keeping
safety constraint violations at a minimum through planning. Our approach aims
to reduce the amount of prior knowledge about the actual system by requiring
only offline data generated by a safe controller. Our results show that we can
achieve significantly fewer constraint violations than comparable reinforcement
learning methods.Comment: Published in: Proc. of the 23rd International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2024
First Steps Towards an Intelligent Laser Welding Architecture Using Deep Neural Networks and Reinforcement Learning
AbstractTo address control difficulties in laser welding, we propose the idea of a self-learning and self-improving laser welding system that combines three modern machine learning techniques. We first show the ability of a deep neural network to extract meaningful, low-dimensional features from high-dimensional laser-welding camera data. These features are then used by a temporal-difference learning algorithm to predict and anticipate important aspects of the system's sensor data. The third part of our proposed architecture suggests using these features and predictions to learn to deliver situation-appropriate welding power; preliminary control results are demonstrated using a laser-welding simulator. The intelligent laser-welding architecture introduced in this work has the capacity to improve its performance without further human assistance and therefore addresses key requirements of modern industry. To our knowledge, it is the first demonstrated combination of deep learning and Nexting with general value functions and also the first usage of deep learning for laser welding specifically and production engineering in general. This work also provides a unique example of how predictions can be explicitly learned using reinforcement learning to support laser welding. We believe that it would be straightforward to adapt our approach to other production engineering applications
Statistical Testing for Disk Encryption Modes of Operations
In this paper we present a group of statistical tests that
explore the random behavior of encryption modes of operations, when
used in disk encryption applications. The results of these tests help us
to better understand how these modes work. We tested ten modes of
operations with the presented statistical tests, five of the narrow-block
type and the other five of the wide-block type. Our analysis shows some
weakness in some of these modes
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