pinecone/core-2020-05-10-deduplication huggingface dataset:
Segmentation of nerve bundles and ganglia in spine MRI using particle filters

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIINational Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971
Connectivity-informed fMRI Activation Detection

International audienceA growing interest has emerged in studying the correlation structure of spontaneous and task-induced brain activity to elucidate the functional architecture of the brain. In particular, functional networks estimated from resting state (RS) data were shown to exhibit high resemblance to those evoked by stimuli. Motivated by these findings, we propose a novel generative model that integrates RS-connectivity and stimulus-evoked responses under a unified analytical framework. Our model permits exact closed-form solutions for both the posterior activation effect estimates and the model evidence. To learn RS networks, graphical LASSO and the oracle approximating shrinkage technique are deployed. On a cohort of 65 subjects, we demonstrate increased sensitivity in fMRI activation detection using our connectivity-informed model over the standard univariate approach. Our results thus provide further evidence for the presence of an intrinsic relationship between brain activity during rest and task, the exploitation of which enables higher detection power in task-driven studies
Improvements in data quality for decision support in intensive care

Nowadays, there is a plethora of technology in hospitals and, in particular, in intensive care units. The clinical data produced everyday can be integrated in a decision support system in real-time to improve quality of care of the critically ill patients. However, there are many sensitive aspects that must be taken into account, mainly the data quality and the integration of heterogeneous data sources. This paper presents INTCare, an Intelligent Decision Support System for Intensive Care in real-time and addresses the previous aspects, in particular, the development of an Electronic Nursing Record and the improvements in the quality of monitored data.Fundação para a Ciência e a Tecnologia (FCT
Experimental evaluation of IEEE 802.15.4/ZigBee for multi-patient ECG monitoring

IEEE 802.15.4/ZigBee wireless sensor networks (WSNs) are a promising alternative to cabled systems for patient monitoring in hospitals. Some areas where monitoring systems based on WSNs can be successfuly used are ambulatory, waiting and triage rooms, post-op, and emergency rooms. The low power and small size ZigBee devices have the ability to form self-configuring networks that can extend themselves through a hospital wing or floor. Using spatially distributed networks, it is possible to cover an extended area and serve several patients. However, the low data rate protocols provided by IEEE 802.15.4 poses several challenges, mainly because its protocols were primarily designed to operate in low traffic load scenarios but some vital signs sensors generate a large volume of data. This work presents an experimental evaluation of the performance of multi-hop ZigBee networks comprised of several nodes that carry the traffic of wearable electrocardiogram (ECG) sensors. The results indicate that star networks can relay 100% of the traffic generated by at least 12 ECG nodes. In tree topologies, the increase of the network traffic load reduces the performance but even these networks can reliably relay the traffic of a considerable number of ECG nodes.Fundação para a Ciência e a Tecnologia (FCT)Grupo AMI – Assistência Médica Integral (Casa de Saúde Guimarães, SA
#Swineflu: Twitter Predicts Swine Flu Outbreak in 2009

Early warning systems for the identification and tracking of infections disease outbreaks have become an important tool in the field of epidemiology. While government lead initiatives to increase the sharing of surveillance data have improved early detection and control, along with advanced web monitoring and analytics services, the recent swine flu outbreak of 2009 demonstrated the important role social media has and the wealth of data it exposes. In this paper, we present an investigation into Twitter, using around 3 Million tweets gathered between May and December 2009, as a possible source of surveillance data and its feasibility to serve as an early warning system. By performing simple filtering and normalization, we demonstrate that Twitter can serve as a self-reporting tool, and hence, provide indications of increased infection spreading. Our initial findings indicate that Twitter can detect such events up to one week before conventional GP reported surveillance data
One-dimensional staged self-assembly

We introduce the problem of staged self-assembly of one-dimensional nanostructures, which becomes interesting when the elements are labeled (e.g., representing functional units that must be placed at specific locations). In a restricted model in which each operation has a single terminal assembly, we prove that assembling a given string of labels with the fewest stages is equivalent, up to constant factors, to compressing the string to be uniquely derived from the smallest possible context-free grammar (a well-studied O(logn)-approximable problem). Without this restriction, we show that the optimal assembly can be substantially smaller than the optimal context-free grammar, by a factor of Ω √n/log n  even for binary strings of length n. Fortunately, we can bound this separation in model power by a quadratic function in the number of distinct glues or tiles allowed in the assembly, which is typically small in practice.17th International Conference, DNA 17, Pasadena, CA, USA, September 19-23, 2011. Proceeding
Stochastic Analysis for the Complex Monge-Ampère Equation. (An Introduction to Krylov's Approach.)

International audienceWe here gather in a single note several original probabilistic works devoted to the analysis of the C^(1,1) regularity of the solution to the possibly degenerate complex Monge-Ampère equation. The whole analysis relies on a probabilistic writing of the solution as the value function of a stochastic optimal control problem. Such a representation has been introduced by Gaveau in the late 70's and used in an exhaustive way by Krylov in a series of papers published in the late 80's. All the arguments we here use follow from these seminal works
Robust visual odometry using uncertainty models

In dense, urban environments, GPS by itself cannot be relied on to provide accurate positioning information. Signal reception issues (e.g. occlusion, multi-path effects) often prevent the GPS receiver from getting a positional lock, causing holes in the absolute positioning data. In order to keep assisting the driver, other sensors are required to track the vehicle motion during these periods of GPS disturbance. In this paper, we propose a novel method to use a single on-board consumer-grade camera to estimate the relative vehicle motion. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Preliminary testing shows good accuracy and reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance. The effects of inaccurate calibration are examined using artificial datasets, suggesting a self-calibrating system may be possible in future work
Fuzzy cognitive maps applied to synthetic aperture radar image classifications

This paper proposes a method based on Fuzzy Cognitive Maps (FCM) for improving the classification provided by the Wishart maximum-likelihood based approach in Synthetic Aperture Radar (SAR) images. FCM\ud
receives the classification results provided by the Wishart approach and creates a network of nodes associating a pixel to a node. The activation levels of these nodes define the degree of membeship of each pixel to each class. These activations levels are iteratively reinforced or punished based on the existing relations among each node and its neighbours and also taking into account the own node under consideration. Through a quality coefficient we measure the\ud
performance of the proposed approach with respect to the Wishart classifier.Peer ReviewedPostprint (published version
A multi-layer 'gas of circles' Markov random field model for the extraction of overlapping near-circular objects.

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images
Robust active contour segmentation with an efficient global optimizer

Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed, combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information, but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for the proposed active contour method. It is experimentally shown that the proposed method has significant better results in the presence of noise and clutter
DTW for Matching Radon Features: A Pattern Recognition and Retrieval Method

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for pattern such as graphical symbol and shape recognition and retrieval. It is basically based on dynamic programming for matching the Radon features. The key characteristic of the method is to use DTW algorithm to match corresponding pairs of histograms at every projecting angle. This allows to exploit the Radon property to include both boundary as internal structure of shapes, while avoiding compressing pattern representation into a single vector and thus miss information, thanks to the DTW. Experimental results show that the method is robust to distortion and degradation including affine transformations
Quantifying appearance retention in carpets using geometrical local binary patterns

Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations has shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique performs better to distinguish consecutive AR grades in carpets
Virtual restoration of the Ghent altarpiece using crack detection and inpainting

In this paper, we present a new method for virtual restoration of digitized paintings, with the special focus on the Ghent Altarpiece (1432), one of Belgium's greatest masterpieces. The goal of the work is to remove cracks from the digitized painting thereby approximating how the painting looked like before ageing for nearly 600 years and aiding art historical and palaeographical analysis. For crack detection, we employ a multiscale morphological approach, which can cope with greatly varying thickness of the cracks as well as with their varying intensities (from dark to the light ones). Due to the content of the painting (with extremely many fine details) and complex type of cracks (including inconsistent whitish clouds around them), the available inpainting methods do not provide satisfactory results on many parts of the painting. We show that patch-based methods outperform pixel-based ones, but leaving still much room for improvements in this application. We propose a new method for candidate patch selection, which can be combined with different patch-based inpainting methods to improve their performance in crack removal. The results demonstrate improved performance, with less artefacts and better preserved fine details
A bio-inspired image coder with temporal scalability

We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensure, in a simple way, scalability and bit allocation. To do so, our main
source of inspiration will be the biologically plausible retina model called
Virtual Retina. Following a similar structure, our model has two stages. The
first stage is an image transform which is performed by the outer layers in the
retina. Here it is modelled by filtering the image with a bank of difference of
Gaussians with time-delays. The second stage is a time-dependent
analog-to-digital conversion which is performed by the inner layers in the
retina. Thanks to its conception, our coder enables scalability and bit
allocation across time. Also, our decoded images do not show annoying artefacts
such as ringing and block effects. As a whole, this article shows how to
capture the main properties of a biological system, here the retina, in order
to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS
  2011
An edge-based approach for robust foreground detection

Foreground segmentation is an essential task in many image processing applications and a commonly used approach to obtain foreground objects from the background. Many techniques exist, but due to shadows and changes in illumination the segmentation of foreground objects from the background remains challenging. In this paper, we present a powerful framework for detections of moving objects in real-time video processing applications under various lighting changes. The novel approach is based on a combination of edge detection and recursive smoothing techniques.We use edge dependencies as statistical features of foreground and background regions and define the foreground as regions containing moving edges. The background is described by short- and long-term estimates. Experiments prove the robustness of our method in the presence of lighting changes in sequences compared to other widely used background subtraction techniques
Comparison of Visual Registration Approaches of 3D Models for Orthodontics

International audienceWe propose to apply vision techniques to develop a main tool for orthodontics: the virtual occlusion of two dental casts. For that purpose, we process photos of the patient mouth and match points between these photos and the dental 3D models. From a set of 2D/3D matches of the two arcades, we calculate the projection matrix, before the mandible registration under the maxillary through a rigid transformation. We perform the mandible registration minimizing the reprojection errors. Two computation methods, depending on the knowledge of camera intrinsic parameters, are compared. Tests are carried out both on virtual and real images. In the virtual case, assumed as perfect, we evaluate the robustness against noise and the increase of performance using several views. Projection matrices and registration efficiency are evaluated respectively by reprojection errors and the differences between the rigid transformation and the reference pose, recorded on the six degrees of freedom
Abstract Domains of Affine Relations

This paper considers some known abstract domains for affine-relation
analysis, along with several variants, and studies how they relate to
each other.  The various domains represent sets of points that satisfy
affine relations over variables that hold machine integers, and are
based on an extension of linear algebra to modules over a ring (in
particular, arithmetic performed modulo 2^w, for some machine-integer
width w).

We show that the abstract domains of Mueller-Olm/Seidl (MOS) and
King/Sondergaard (KS) are, in general, incomparable.  However, we give
sound interconversion methods.  That is, we give an algorithm to
convert a KS element v_KS to an over-approximating MOS element v_MOS
-- i.e., gamma(v_KS) subseteq gamma(v_MOS) -- as well as an algorithm
to convert an MOS element w_MOS to an over-approximating KS element
w_KS -- i.e., gamma(w_MOS) subseteq gamma(w_KS).

The paper provides insight on the range of options that one has for
performing affine-relation analysis in a program analyzer.

 o We describe how to perform a greedy, operator-by-operator
   abstraction method to obtain KS abstract transformers.

 o We also describe a more global approach to obtaining KS abstract
   transformers that considers the semantics of an entire
   instruction, basic block or other loop-free program fragment.

The latter method can yield best abstract transformers, and hence can
be more precise than the former method.  However, the latter method is
more expensive.

We also explain how to use the KS domain for interprocedural program
analysis using a bit-precise concrete semantics, but without
bit-blasting
Probabilistically Accurate Program Transformations

The standard approach to program transformation involves the use of discrete logical reasoning to prove that the transformation does not change the observable semantics of the program. We propose a new approach that, in contrast, uses probabilistic reasoning to justify the application of transformations that may change, within probabilistic accuracy bounds, the result that the program produces.
Our new approach produces probabilistic guarantees of the form ℙ(|D| ≥ B) ≤ ε, ε ∈ (0, 1), where D is the difference between the results that the transformed and original programs produce, B is an acceptability bound on the absolute value of D, and ε is the maximum acceptable probability of observing large |D|. We show how to use our approach to justify the application of loop perforation (which transforms loops to execute fewer iterations) to a set of computational patterns.18th International Symposium, SAS 2011, Venice, Italy, September 14-16, 2011. ProceedingsNational Science Foundation (U.S.) (Grant CCF-0811397)National Science Foundation (U.S.) (Grant CCF-0905244)National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Dept. of Energy (Grant DE-SC0005288
Using Bounded Model Checking to Focus Fixpoint Iterations

International audienceTwo classical sources of imprecision in static analysis by abstract interpretation are widening and merge operations. Merge operations can be done away by distinguishing paths, as in trace partitioning, at the expense of enumerating an exponential number of paths. In this article, we describe how to avoid such systematic exploration by focusing on a single path at a time, designated by SMT-solving. Our method combines well with acceleration techniques, thus doing away with widenings as well in some cases. We illustrate it over the well-known domain of convex polyhedra
Mixed-Criticality Scheduling of Sporadic Task Systems

International audienceWe consider the scheduling of mixed-criticality task systems, that is, systems where each task to be scheduled has multiple levels of worst-case execution time estimates. We design a scheduling algorithm, EDF-VD, whose effectiveness we analyze using the processor speedup metric: we show that any 2-level task system that is schedulable on a unit-speed processor is correctly scheduled by EDF-VD using speed φ; here φ < 1.619 is the golden ratio. We also show how to generalize the algorithm to K > 2 criticality levels.We finally consider 2-level instances on m identical machines. We prove speedup bounds for scheduling an independent collection of jobs and for the partitioned scheduling of a 2-level task system
Approximation algorithms and hardness results for the joint replenishment Problepm with constant demands

In the Joint Replenishment Problem (JRP), the goal is to coordinate the replenishments of a collection of goods over time so that continuous demands are satisfied with minimum overall ordering and holding costs. We consider the case when demand rates are constant. Our main contribution is the first hardness result for any variant of JRP with constant demands. When replenishments per commodity are required to be periodic and the time horizon is infinite (which corresponds to the so-called general integer model with correction factor), we show that finding an optimal replenishment policy is at least as hard as integer factorization. This result provides the first theoretical evidence that the JRP with constant demands may have no polynomial-time algorithm and that relaxations and heuristics are called for. We then show that a simple modification of an algorithm by Wildeman et al. (1997) for the JRP gives a fully polynomial-time approximation scheme for the general integer model (without correction factor). We also extend their algorithm to the finite horizon case, achieving an approximation guarantee asymptotically equal to √9/8.19th Annual European Symposium, Saarbrücken, Germany, September 5-9, 2011. Proceeding