24 research outputs found
Recent advances in imprecise-probabilistic graphical models
We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities. Robust inferences can be made in so-called credal networks when the local models attached to their nodes are imprecisely specified as conditional lower previsions, by using exact algorithms whose complexity is comparable to that for the precise-probabilistic counterparts
Model-Based Deep Learning
Signal processing, communications, and control have traditionally relied on
classical statistical modeling techniques. Such model-based methods utilize
mathematical formulations that represent the underlying physics, prior
information and additional domain knowledge. Simple classical models are useful
but sensitive to inaccuracies and may lead to poor performance when real
systems display complex or dynamic behavior. On the other hand, purely
data-driven approaches that are model-agnostic are becoming increasingly
popular as datasets become abundant and the power of modern deep learning
pipelines increases. Deep neural networks (DNNs) use generic architectures
which learn to operate from data, and demonstrate excellent performance,
especially for supervised problems. However, DNNs typically require massive
amounts of data and immense computational resources, limiting their
applicability for some signal processing scenarios. We are interested in hybrid
techniques that combine principled mathematical models with data-driven systems
to benefit from the advantages of both approaches. Such model-based deep
learning methods exploit both partial domain knowledge, via mathematical
structures designed for specific problems, as well as learning from limited
data. In this article we survey the leading approaches for studying and
designing model-based deep learning systems. We divide hybrid
model-based/data-driven systems into categories based on their inference
mechanism. We provide a comprehensive review of the leading approaches for
combining model-based algorithms with deep learning in a systematic manner,
along with concrete guidelines and detailed signal processing oriented examples
from recent literature. Our aim is to facilitate the design and study of future
systems on the intersection of signal processing and machine learning that
incorporate the advantages of both domains
Discrete and Continuous Optimization for Motion Estimation
The study of motion estimation reaches back decades and has become one of the central topics of research in computer vision. Even so, there are situations where current approaches fail, such as when there are extreme lighting variations, significant occlusions, or very large motions. In this thesis, we propose several approaches to address these issues. First, we propose a novel continuous optimization framework for estimating optical flow based on a decomposition of the image domain into triangular facets. We show how this allows for occlusions to be easily and naturally handled within our optimization framework without any post-processing. We also show that a triangular decomposition enables us to use a direct Cholesky decomposition to solve the resulting linear systems by reducing its memory requirements. Second, we introduce a simple method for incorporating additional temporal information into optical flow using inertial estimates of the flow, which leads to a significant reduction in error. We evaluate our methods on several datasets and achieve state-of-the-art results on MPI-Sintel. Finally, we introduce a discrete optimization framework for optical flow computation. Discrete approaches have generally been avoided in optical flow because of the relatively large label space that makes them computationally expensive. In our approach, we use recent advances in image segmentation to build a tree-structured graphical model that conforms to the image content. We show how the optimal solution to these discrete optical flow problems can be computed efficiently by making use of optimization methods from the object recognition literature, even for large images with hundreds of thousands of labels
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
Where am I? This is one of the most critical questions that any intelligent
system should answer to decide whether it navigates to a previously visited
area. This problem has long been acknowledged for its challenging nature in
simultaneous localization and mapping (SLAM), wherein the robot needs to
correctly associate the incoming sensory data to the database allowing
consistent map generation. The significant advances in computer vision achieved
over the last 20 years, the increased computational power, and the growing
demand for long-term exploration contributed to efficiently performing such a
complex task with inexpensive perception sensors. In this article, visual loop
closure detection, which formulates a solution based solely on appearance input
data, is surveyed. We start by briefly introducing place recognition and SLAM
concepts in robotics. Then, we describe a loop closure detection system's
structure, covering an extensive collection of topics, including the feature
extraction, the environment representation, the decision-making step, and the
evaluation process. We conclude by discussing open and new research challenges,
particularly concerning the robustness in dynamic environments, the
computational complexity, and scalability in long-term operations. The article
aims to serve as a tutorial and a position paper for newcomers to visual loop
closure detection.Comment: 25 pages, 15 figure