59,419 research outputs found
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of DempsterâShafer evidence theory
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Removal of monocular interactions equates rivalry behavior for monocular, binocular, and stimulus rivalries
When the two eyes are presented with conflicting stimuli, perception starts to fluctuate over time (i.e., binocular rivalry). A similar fluctuation occurs when two patterns are presented to a single eye (i.e., monocular rivalry), or when they are swapped rapidly and repeatedly between the eyes (i.e., stimulus rivalry). Although all these cases lead to rivalry, in quantitative terms these modes of rivalry are generally found to differ significantly. We studied these different modes of rivalry with identical intermittently shown stimuli while varying the temporal layout of stimulation. We show that the quantitative differences between the modes of rivalry are caused by the presence of monocular interactions between the rivaling patterns; the introduction of a blank period just before a stimulus swap changed the number of rivalry reports to the extent that monocular and stimulus rivalries were inducible over ranges of spatial frequency content and contrast values that were nearly identical to binocular rivalry. Moreover when monocular interactions did not occur the perceptual dynamics of monocular, binocular, and stimulus rivalries were statistically indistinguishable. This range of identical behavior exhibited a monocular (âŒ50 ms) and a binocular (âŒ350 ms) limit. We argue that a common binocular, or pattern-based, mechanism determines the temporal constraints for these modes of rivalry
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Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.
The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
The consequences of replicating in the wrong orientation: Bacterial chromosome duplication without an active replication origin
Chromosome replication is regulated in all organisms at the assembly stage of the replication machinery at specific origins. In Escherichia coli the DnaA initiator protein regulates the assembly of replication forks at oriC. This regulation can be undermined by defects in nucleic acid metaÂŹbolism. In cells lacking RNase HI replication initiates indepenÂŹdently of DnaA and oriC, presumably at persisting R-loops. A similar mechanism was assumed for origin-independent synthesis in cells lacking RecG. However, recently we suggested that this synthesis initiates at intermediates resulting from replication fork fusions. Here we present data suggesting that in cells lacking RecG or RNase HI origin-independent synthesis arises by different mechanisms, indicative of these two proteins having different roles in vivo. Our data support the idea that RNase HI processes R-loops, while RecG is required to process replication fork fusion intermediates. However, regardless of how origin-independent synthesis is initiated, a fraction of forks will proceed in an orientation opposite to normal. We show that the resulting head-on encounters with transcription threaten cell viability, especially if taking place in highly-transcribed areas. Thus, despite their different functions, RecG and RNase HI are both important factors for maintaining replication control and orientation. Their absence causes severe replication problems, highlighting the advantages of the normal chromosome arrangement, which exploits a single origin to control the number of forks and their orientation relative to transcription, and a defined termination area to contain fork fusions. Any changes to this arrangement endanger cell cycle control, chromosome dynamics and, ultimately, cell viability.This work was supported by the Royal Society (RG110414 to C.J.R.) and The Biotechnology and Biological Sciences Research Council (BB/K015729/1 to C.J.R.)
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Exploring the components, asymmetry and distribution of relationship quality in wild Barbary macaques (Macaca sylvanus)
Social relationships between group members are a key feature of many animal societies. The quality of social relationships has been described by three main components: value, compatibility and security, based on the benefits, tenure and stability of social exchanges. We aimed to analyse whether this three component structure could be used to describe the quality of social relationships in wild Barbary macaques (Macaca sylvanus). Moreover, we examined whether relationship quality was affected by the sex, age and rank differences between social partners, and investigated the asymmetric nature of social relationships. We collected over 1,900 hours of focal data on seven behavioural variables measuring relationship quality,
and used principal component analysis to investigate how these variables clustered together. We found that relationship quality in wild Barbary macaques can be described by a three component structure that represents the value, compatibility and security of a relationship. Female-female dyads had more valuable relationships and same-age dyads more compatible relationships than any other dyad. Rank difference had no effect on the quality of a social relationship. Finally, we found a high degree of asymmetry in how members of a dyad exchange social behaviour. We argue that the asymmetry of social
relationships should be taken into account when exploring the pattern and function of social behaviour in animal societies
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