88 research outputs found
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Biologically-inspired hierarchical architectures for object recognition
PhD ThesisThe existing methods for machine vision translate the three-dimensional
objects in the real world into two-dimensional images. These methods
have achieved acceptable performances in recognising objects. However,
the recognition performance drops dramatically when objects are transformed, for instance, the background, orientation, position in the image,
and scale. The human’s visual cortex has evolved to form an efficient
invariant representation of objects from within a scene. The superior
performance of human can be explained by the feed-forward multi-layer
hierarchical structure of human visual cortex, in addition to, the utilisation of different fields of vision depending on the recognition task.
Therefore, the research community investigated building systems that
mimic the hierarchical architecture of the human visual cortex as an
ultimate objective.
The aim of this thesis can be summarised as developing hierarchical
models of the visual processing that tackle the remaining challenges of
object recognition. To enhance the existing models of object recognition
and to overcome the above-mentioned issues, three major contributions
are made that can be summarised as the followings
1. building a hierarchical model within an abstract architecture that
achieves good performances in challenging image object datasets;
2. investigating the contribution for each region of vision for object
and scene images in order to increase the recognition performance
and decrease the size of the processed data;
3. further enhance the performance of all existing models of object
recognition by introducing hierarchical topologies that utilise the
context in which the object is found to determine the identity of
the object.
Statement ofHigher Committee For Education Development in Iraq (HCED
Supervised learning and inference of semantic information from road scene images
Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist
Supervised learning and inference of semantic information from road scene images
Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist
Deep learning for internet of underwater things and ocean data analytics
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes
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