68,329 research outputs found
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV)
workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube
demo at this: https://www.youtube.com/watch?v=zYypJPJipY
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
A multi-view approach to cDNA micro-array analysis
The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research
Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences
under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China
under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany
An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients
Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be
widely studied. While there is no consensus on whether MCIs actually "convert"
to AD, the more important question is not whether MCIs convert, but what is the
best such definition. We focus on automatic prognostication, nominally using
only a baseline image brain scan, of whether an MCI individual will convert to
AD within a multi-year period following the initial clinical visit. This is in
fact not a traditional supervised learning problem since, in ADNI, there are no
definitive labeled examples of MCI conversion. Prior works have defined MCI
subclasses based on whether or not clinical/cognitive scores such as CDR
significantly change from baseline. There are concerns with these definitions,
however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5,
even while physiological changes may be occurring. These works ignore rich
phenotypical information in an MCI patient's brain scan and labeled AD and
Control examples, in defining conversion. We propose an innovative conversion
definition, wherein an MCI patient is declared to be a converter if any of the
patient's brain scans (at follow-up visits) are classified "AD" by an
(accurately-designed) Control-AD classifier. This novel definition bootstraps
the design of a second classifier, specifically trained to predict whether or
not MCIs will convert. This second classifier thus predicts whether an
AD-Control classifier will predict that a patient has AD. Our results
demonstrate this new definition leads not only to much higher prognostic
accuracy than by-CDR conversion, but also to subpopulations much more
consistent with known AD brain region biomarkers. We also identify key
prognostic region biomarkers, essential for accurately discriminating the
converter and nonconverter groups
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