14,747 research outputs found
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Reconstruction of the tridimensional geometry of a visual scene using the
binocular disparity information is an important issue in computer vision and
mobile robotics, which can be formulated as a Bayesian inference problem.
However, computation of the full disparity distribution with an advanced
Bayesian model is usually an intractable problem, and proves computationally
challenging even with a simple model. In this paper, we show how probabilistic
hardware using distributed memory and alternate representation of data as
stochastic bitstreams can solve that problem with high performance and energy
efficiency. We put forward a way to express discrete probability distributions
using stochastic data representations and perform Bayesian fusion using those
representations, and show how that approach can be applied to diparity
computation. We evaluate the system using a simulated stochastic implementation
and discuss possible hardware implementations of such architectures and their
potential for sensorimotor processing and robotics.Comment: Preprint of article submitted for publication in International
Journal of Approximate Reasoning and accepted pending minor revision
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational Generative Adversarial Network (PPRL-VGAN) to learn an image
representation that is explicitly disentangled from the identity information.
At the same time, this representation is discriminative from the standpoint of
facial expression recognition and generative as it allows expression-equivalent
face image synthesis. We evaluate the proposed model on two public datasets
under various threat scenarios. Quantitative and qualitative results
demonstrate that our approach strikes a balance between the preservation of
privacy and data utility. We further demonstrate that our model can be
effectively applied to other tasks such as expression morphing and image
completion
The primacy of multiparametric MRI in men with suspected prostate cancer
Background: Multiparametric MRI (mpMRI) became recognised in investigating those with suspected prostate cancer between 2010 and 2012; in the USA, the preventative task force moratorium on PSA screening was a strong catalyst. In a few short years, it has been adopted into daily urological and oncological practice. The pace of clinical uptake, born along by countless papers proclaiming high accuracy in detecting clinically significant prostate cancer, has sparked much debate about the timing of mpMRI within the traditional biopsy-driven clinical pathways. There are strongly held opposing views on using mpMRI as a triage test regarding the need for biopsy and/or guiding the biopsy pattern. Objective: To review the evidence base and present a position paper on the role of mpMRI in the diagnosis and management of prostate cancer. Methods: A subgroup of experts from the ESUR Prostate MRI Working Group conducted literature review and face to face and electronic exchanges to draw up a position statement. Results: This paper considers diagnostic strategies for clinically significant prostate cancer; current national and international guidance; the impact of pre-biopsy mpMRI in detection of clinically significant and clinically insignificant neoplasms; the impact of pre-biopsy mpMRI on biopsy strategies and targeting; the notion of mpMRI within a wider risk evaluation on a patient by patient basis; the problems that beset mpMRI including inter-observer variability. Conclusions: The paper concludes with a set of suggestions for using mpMRI to influence who to biopsy and who not to biopsy at diagnosis. Key Points: • Adopt mpMRI as the first, and primary, investigation in the workup of men with suspected prostate cancer. • PI-RADS assessment categories 1 and 2 have a high negative predictive value in excluding significant disease, and systematic biopsy may be postponed, especially in men with low-risk of disease following additional risk stratification. • PI-RADS assessment category lesions 4 and 5 should be targeted; PI-RADS assessment category lesion 3 may be biopsied as a target, as part of systematic biopsies or may be observed depending on risk stratification
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and
Pattern Recognition Workshops, 201
Enhancing automatic maritime surveillance systems with visual information
Automatic surveillance systems for the maritime
domain are becoming more and more important due to a constant
increase of naval traffic and to the simultaneous reduction of
crews on decks. However, available technology still provides only
a limited support to this kind of applications. In this paper,
a modular system for intelligent maritime surveillance, capable
of fusing information from heterogeneous sources, is described.
The system is designed to enhance the functions of the existing
Vessel Traffic Services systems and to be deployable in populated
areas, where radar-based systems cannot be used due to the high
electromagnetic radiation emissions. A quantitative evaluation
of the proposed approach has been carried out on a large
and publicly available data set of images and videos, collected
from multiple real sites, with different light, weather, and traffic
conditions
MRIM-LIG at ImageCLEF 2009: Robotvision, Image annotation and retrieval tasks
International audienceThis paper describes mainly the experiments that have been conducted by the MRIM group at the LIG in Grenoble for the the ImageCLEF 2009 campaign, focusing on the work done for the Robotvision task. The proposal for this task is to study the behaviour of a generative approach inspired by the language model of information retrieval. To fit with the specificity of the Robotvi- sion task, we added post-processing in a way to tackle with the fact that images do belong only to several classes (rooms) and that image are not independent from each others (i.e., the robot cannot in one second be in three different rooms). The results obtained still need improvement, but the use of such language model in the case of Robotvision is showed. Some results related to the Image Retrieval task and the Image annotation task are also presented
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