8,247 research outputs found
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
Nowadays, video cameras are deployed in large scale for spatial monitoring of
physical places (e.g., surveillance systems in the context of smart cities).
The massive camera deployment, however, presents new challenges for analyzing
the enormous data, as the cost of high computational overhead of sophisticated
deep learning techniques imposes a prohibitive overhead, in terms of energy
consumption and processing throughput, on such resource-constrained edge
devices. To address these limitations, this paper envisions a collaborative
intelligent cross-camera video analytics paradigm at the network edge in which
camera nodes adjust their pipelines (e.g., inference) to incorporate correlated
observations and shared knowledge from other nodes' contents. By harassing
redundant spatio-temporal to reduce the size of the inference search space in
one hand, and intelligent collaboration between video nodes on the other, we
discuss how such collaborative paradigm can considerably improve accuracy,
reduce latency and decrease communication bandwidth compared to
non-collaborative baselines. This paper also describes major opportunities and
challenges in realizing such a paradigm.Comment: First International Workshop on Challenges in Artificial Intelligence
and Machine Learnin
Generalized Category Discovery in Semantic Segmentation
This paper explores a novel setting called Generalized Category Discovery in
Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior
knowledge from a labeled set of base classes. The unlabeled images contain
pixels of the base class or novel class. In contrast to Novel Category
Discovery in Semantic Segmentation (NCDSS), there is no prerequisite for prior
knowledge mandating the existence of at least one novel class in each unlabeled
image. Besides, we broaden the segmentation scope beyond foreground objects to
include the entire image. Existing NCDSS methods rely on the aforementioned
priors, making them challenging to truly apply in real-world situations. We
propose a straightforward yet effective framework that reinterprets the GCDSS
challenge as a task of mask classification. Additionally, we construct a
baseline method and introduce the Neighborhood Relations-Guided Mask Clustering
Algorithm (NeRG-MaskCA) for mask categorization to address the fragmentation in
semantic representation. A benchmark dataset, Cityscapes-GCD, derived from the
Cityscapes dataset, is established to evaluate the GCDSS framework. Our method
demonstrates the feasibility of the GCDSS problem and the potential for
discovering and segmenting novel object classes in unlabeled images. We employ
the generated pseudo-labels from our approach as ground truth to supervise the
training of other models, thereby enabling them with the ability to segment
novel classes. It paves the way for further research in generalized category
discovery, broadening the horizons of semantic segmentation and its
applications. For details, please visit https://github.com/JethroPeng/GCDS
- …