nils-herrmann/clean_scirepeval huggingface dataset:
1024-Channel 3D ultrasound digital beamformer in a single 5W FPGA

3D ultrasound, an emerging medical imaging technique that is presently only used in hospitals, has the potential to enable breakthrough telemedicine applications, provided that its cost and power dissipation can be minimized. In this paper, we present an FPGA architecture suitable for a portable medical 3D ultrasound device. We show an optimized design for the digital part of the imager, including the delay calculation block, which is its most critical part. Our computationally efficient approach requires a single FPGA for 3D imaging, which is unprecedented. The design is scalable; a configuration supporting a 32×32-channel probe, which enables high-quality imaging, consumes only about 5W.
Towards Convenient Calibration for Cross-Ratio Based Gaze Estimation

Eye gaze movements are considered as a salient modality for human computer interaction applications. Recently, cross-ratio (CR) based eye tracking methods have attracted increasing interest because they provide remote gaze estimation using a single uncalibrated camera. However, due to the simplification assumptions in CR-based methods, their performance is lower than the model-based approaches . Several efforts have been made to improve the accuracy by compensating for the assumptions with subject specific calibration. This paper presents a CR-based automatic gaze estimation system that accurately works under natural head movements. A subject-specific calibration method based on regularized least-squares regression (LSR) is introduced for achieving higher accuracy compared to other state-of-the-art calibration methods. Experimental results also show that the proposed calibration method generalizes better when fewer calibration points are used. This enables user friendly applications with minimum calibration effort without sacrificing too much accuracy. In addition, we adaptively fuse the estimation of the point of regard (PoR) from both eyes based on the visibility of eye features. The adaptive fusion scheme reduces accuracy error by around 20% and also increases the estimation coverage under natural head movements.
Crop backscatter modeling and soil moisture estimation with support vector regression

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.
Ultrafast Ultrasound Imaging as an Inverse Problem: Matrix-Free Sparse Image Reconstruction

Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution to the image reconstruction problem. An alternative to DAS consists in using iterative techniques, which require both an accurate measurement model and a strong prior on the image under scrutiny. Toward this goal, much effort has been deployed in formulating models for US imaging, which usually require a large amount of memory to store the matrix coefficients. We present two different techniques, which take advantage of fast and matrix-free formulations derived for the measurement model and its adjoint, and rely on sparsity of US images in well-chosen models. Sparse regularization is used for enhanced image reconstruction. Compressed beamforming exploits the compressed sensing framework to restore high-quality images from fewer raw data than state-of-the-art approaches. Using simulated data and in vivo experimental acquisitions, we show that the proposed approach is three orders of magnitude faster than non-DAS state-of-the-art methods, with comparable or better image quality.
Improved local binary pattern based action unit detection using morphological and bilateral filters

Automatic facial action unit (AU) detection in videos is the key ingredient to all systems that utilize a subject face for either interaction or analysis purposes. With the ever growing range of possible applications, achieving a high accuracy in the simplest possible manner gains even more importance. In this paper, we present new features obtained by applying local binary patterns to images processed by morphological and bilateral filters. We use as features the variations of these patterns between the expressive and neutral faces, and show that we can gain a considerable amount of accuracy increase by simply applying these fundamental image processing tools and choosing the right way of representing the patterns. We also use these features in conjunction with additional features based on facial point geometrical relations between frames and achieve detection rates higher than methods previously proposed, using a small number of features and basic support vector machine classification.
Accelerated microstructure imaging via convex optimisation for regions with multiple fibres (AMICOx)

This paper reviews and extends our previous work to enable fast axonal diameter mapping from diffusion MRI data in the presence of multiple fibre populations within a voxel. Most of the existing mi-crostructure imaging techniques use non-linear algorithms to fit their data models and consequently, they are computationally expensive and usually slow. Moreover, most of them assume a single axon orientation while numerous regions of the brain actually present more complex configurations, e.g. fiber crossing. We present a flexible framework, based on convex optimisation, that enables fast and accurate reconstructions of the microstructure organisation, not limited to areas where the white matter is coherently oriented. We show through numerical simulations the ability of our method to correctly estimate the microstructure features (mean axon diameter and intra-cellular volume fraction) in crossing regions.
Efficient Sample Delay Calculation for 2-D and 3-D Ultrasound Imaging

Ultrasound imaging is a reference medical diagnostic technique, thanks to its blend of versatility, effectiveness, and moderate cost. The core computation of all ultrasound imaging methods is based on simple formulae, except for those required to calculate acoustic propagation delays with high precision and throughput. Unfortunately, advanced three-dimensional (3-D) systems require the calculation or storage of billions of such delay values per frame, which is a challenge. In 2-D systems, this requirement can be four orders of magnitude lower, but efficient computation is still crucial in view of low-power implementations that can be battery-operated, enabling usage in numerous additional scenarios. In this paper, we explore two smart designs of the delay generation function. To quantify their hardware cost, we implement them on FPGA and study their footprint and performance. We evaluate how these architectures scale to different ultrasound applications, from a low-power 2-D system to a next-generation 3-D machine. When using numerical approximations, we demonstrate the ability to generate delay values with sufficient throughput to support 10 000-channel 3-D imaging at up to 30 fps while using 63% of a Virtex 7 FPGA, requiring 24 MB of external memory accessed at about 32 GB/s bandwidth. Alternatively, with similar FPGA occupation, we show an exact calculation method that reaches 24 fps on 1225-channel 3-D imaging and does not require external memory at all. Both designs can be scaled to use a negligible amount of resources for 2-D imaging in low-power applications and for ultrafast 2-D imaging at hundreds of frames per second.
Interactive delineation of brain sulci and their merging into functional PET images

A set of software tools has been developed to assist the neuro(physio)logist in the analysis of a series of cerebral images from single subjects by fusing sulci manually delineated on MRI brain surface into PET functional data. The procedure requires coregistered datasets and involves 4 steps: (i) segmentation of anatomical MRI data in order to extract the brain surface, (ii) generate the brain surface views by parallel ray casting, (iii) manual delineation of the relevant sulci from the surface views and (iv) fusion of the landmarks into any coregistered dataset from the same subject. The brain surface is segmented automatically from 3D MRI data using a new "Directional Watershed Transform" algorithm. From the segmented brain surface, 8 orthogonal surface views are calculated as visual support for interactive stereo definition of the major brain sulci. Each sulcus is built as a 3D trace line using a few vertices which are manually defined on one or several surface views. This technique allows one to follow the brain surface curvature rather independently of the number and the position of the vertices. The sulci are saved in an individual file for further use. The brain surface viewer is linked (via the 3D cursor position) to an independent volume viewer containing a coregistered (anatomical or functional) volume. Sulcal landmarks are finally projected onto this volume allowing further volume of interest definition. The use of the tool set is illustrated by a single subject brain activation study after /sup 15/O water injection.
Automatic Extraction of Geometric Lip Features with Application to Multi-Modal Speaker Identification

In this paper we consider the problem of automatic extraction of the geometric lip features for the purposes of multi-modal speaker identification. The use of visual information from the mouth region can be of great importance for improving the speaker identification system performance in noisy conditions. We propose a novel method for automated lip features extraction that utilizes color space transformation and a fuzzy-based c-means clustering technique. Using the obtained visual cues closed-set audio-visual speaker identification experiments are performed on the CUAVE database, showing promising results
Supervised nonparametric information theoretic classification

In this paper, supervised nonparametric information theoretic classification (ITC) is introduced. Its principle relies on the likelihood of a data sample of transmitting its class label to data points in its vicinity. ITC's learning rule is linked to the concept of information potential and the approach is validated on Ripley's data set. We show that ITC may outperform classical classification algorithms, such as probabilistic neural networks and support vector machines.
Surface Reconstruction From Microscopic Images in Optical Lithography

This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of printed circuits taken with a scanning electron microscope. Our reconstruction method combines the physical model of the optical acquisition system with prior knowledge about the shapes of the patterns in the circuit; the result is a shape-from-shading technique with a shape prior. The reconstruction of the surface is formulated as an optimization problem with an objective functional that combines a data-fidelity term on the microscopic image with two prior terms on the surface. The data term models the acquisition system through the irradiance equation characteristic of the microscope; the first prior is a smoothness penalty on the reconstructed surface, and the second prior constrains the shape of the surface to agree with the expected shape of the pattern in the circuit. In order to account for the variability of the manufacturing process, this second prior includes a deformation field that allows a nonlinear elastic deformation between the expected pattern and the reconstructed surface. As a result, the minimization problem has two unknowns, and the reconstruction method provides two outputs: 1) a reconstructed surface and 2) a deformation field. The reconstructed surface is derived from the shading observed in the image and the prior knowledge about the pattern in the circuit, while the deformation field produces a mapping between the expected shape and the reconstructed surface that provides a measure of deviation between the circuit design models and the real manufacturing process.
Higher order autocorrelations for pattern classification

The use of higher-order local autocorrelations as features for pattern recognition has been acknowledged for many years, but their applicability was restricted to relatively low orders (2 or 3) and small local neighborhoods, due to combinatorial increase in computational costs. A new method for using these features is presented, which allows the use of autocorrelations of any order and of larger neighborhoods. The method is closely related to the classifier used, a support vector machine (SVM), and exploits the special form of the inner products of autocorrelations and the properties of some kernel functions used by SVM. Using SVM, linear and nonlinear classification functions can be learned, extending the previous works on higher-order autocorrelations which were based on linear classifiers.
A deep learning approach to ultrasound image recovery

Compressed sensing (CS) has drawn many interest in the field ultrasound (US) image recovery. It has demonstrated promising results in the recovery of radio-frequency element raw-data [Liebgott et. al. ULTRAS13, Besson et. al. SPARS17]. The objective of such approaches is to recover the raw-data from undersampled random measurements. It is achieved by means of convex optimization or greedy methods which require a high number of iterations to converge and whose accuracy highly depends on hyper-parameters fine-tuning. Recently, deep-neural networks (DNN) has redefined the paradigm of signal recovery, leading to remarkable results for CS reconstruction of natural images [Mousavi et. al. ALBERTON15].
On Dynamic Stream Weighting for Audio-Visual Speech Recognition

The integration of audio and visual information improves speech recognition performance, specially in the presence of noise. In these circumstances it is necessary to introduce audio and visual weights to control the contribution of each modality to the recognition task. We present a method to set the value of the weights associated to each stream according to their reliability for speech recognition, allowing them to change with time and adapt to different noise and working conditions. Our dynamic weights are derived from several measures of the stream reliability, some specific to speech processing and others inherent to any classification task, and take into account the special role of silence detection in the definition of audio and visual weights. In this paper, we propose a new confidence measure, compare it to existing ones, and point out the importance of the correct detection of silence utterances in the definition of the weighting system. Experimental results support our main contribution: the inclusion of a voice activity detector in the weighting scheme improves speech recognition over different system architectures and confidence measures, leading to an increase in performance more relevant than any difference between the proposed confidence measures.
Evaluation of atlas fusion strategies for segmentation of head and neck lymph nodes for radiotherapy planning

Accurate segmentation of lymph nodes in head and neck (H&N) CT images is essential for the radiotherapy planning of the H&N cancer. Atlas-based segmentation methods are widely used for the automated segmentation of such structures. Multi-atlas approaches are proven to be more accurate and robust than using a single atlas. We have recently proposed a general Markov random field (MRF)-based framework that can perform edge-preserving smoothing of the labels at the time of fusing the labels itself. There are three main contributions of this paper: First, we reformulate the “shape based averaging” (SBA) fusion method to fit into the general MRF-based fusion framework. Second, we evaluate the following fusion algorithms for the segmentation of H&N lymph nodes: (i) STAPLE, (ii) SBA, (iii) SBA+MRF, (iv) majority voting (MV), (v) MV+MRF, (vi) global weighted voting (GWV), (vii) GWV+MRF, (viii) local weighted voting (LWV) and (ix) LWV+MRF. Finally, we also study the effect varying the number of atlases on the performance of the above algorithms.
Counting Pedestrians in Video Sequences Using Trajectory Clustering

In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented
Semi-Supervised Novelty Detection Using SVM Entire Solution Path

Very often, the only reliable information available to perform change detection is the description of some “unchanged” regions. Since, sometimes, these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform semi-supervised novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the cost-sensitive support vector machine (CS-SVM), but this requires a heavy parameter search. Here, we propose the use of entire solution path algorithms for the CS-SVM in order to facilitate and accelerate parameter selection for SSND. Two algorithms are considered and evaluated. The first algorithm is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, optimization of a separate model for each hyperparameter set is avoided. The second algorithm forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low-density (LD) criterion for selecting optimal classification boundaries, thus avoiding recourse to cross validation (CV) that usually requires information about the “change” class. Experiments are performed on two multitemporal change detection data sets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The proposed LD criterion achieves results that are close to the ones obtained by CV but without using information about the changes.
USSR: An ultrasound sparse regularization framework

In pulse-echo ultrasound (US) imaging, recovering the tissue response from the element-raw data can be formulated as an ill-posed inverse problem. Standard methods rely on the delay-and-sum (DAS) which gives a fast, but rough, estimated solution of the problem. An alternative consists in using regularization approaches, which exploit a formulation of a measurement model and additional prior knowledge to leverage the ill-posedness of the problem. Among the regularization methods, sparse regularization has recently grown many interest [Besson et al. UFFC16, David et al. JASA15, Szasz et al. UFFC16]. However current methods require a tremendous amount of memory and are unachievable in realistic US imaging applications.
Multi-Layer Hierarchical Clustering of Pedestrian Trajectories for Automatic Counting of People in Video Sequences

In this paper we propose an approach to count the number of pedestrians, given a trajectory data set provided by a tracking system. The tracking process itself is treated as a black box providing us the input data. The idea is to apply a hierarchical clustering algorithm, using different data representations and distance measures, as a post-processing step. The final goal is to reduce the difference between the number of tracked pedestrians and the real number of individuals present in the scene.
A Scale-Space of Cortical Feature Maps

In this paper we define a scale-space for cortical mean curvature maps on the sphere, that offers a hierarchical representation of the brain cortical structures, useful in multiscale registration and analysis algorithms. A spherical feature map was obtained through inflation of the cortical surface of one hemisphere, extracted from structural MR images. Using the Beltrami framework, we embedded this spherical mesh in a higher dimensional space and the feature assigned to a mesh vertex became an additional component of its coordinates. This enhanced mesh then evolved under Beltrami flow. Imposing an appropriate aspect ratio for the feature components, we thus minimized an interpolation between the L 2 and TV-norm of the map. The collection of all maps produced by this PDE formed a scale-space. Our results suggest that this scale-space provides a generalization of the brain map suitable for use e.g., within a multiscale registration framework.
Source/Filter Factorial Hidden Markov Model, With Application to Pitch and Formant Tracking

Tracking vocal tract formant frequencies <formula formulatype="inline"> <tex Notation="TeX">$(f_{p})$</tex></formula> and estimating the fundamental frequency <formula formulatype="inline"><tex Notation="TeX">$(f_{0})$</tex> </formula> are two tracking problems that have been tackled in many speech processing works, often independently, with applications to articulatory parameters estimations, speech analysis/synthesis or linguistics. Many works assume an auto-regressive (AR) model to fit the spectral envelope, hence indirectly estimating the formant tracks from the AR parameters. However, directly estimating the formant frequencies, or equivalently the poles of the AR filter, allows to further model the smoothness of the desired tracks. In this paper, we propose a Factorial Hidden Markov Model combined with a vocal source/filter model, with parameters naturally encoding the <formula formulatype="inline"><tex Notation="TeX">$f_{0}$</tex></formula> and <formula formulatype="inline"> <tex Notation="TeX">$f_{p}$</tex></formula> tracks. Two algorithms are proposed, with two different strategies: first, a simplification of the underlying model, with a parameter estimation based on variational methods, and second, a sparse decomposition of the signal, based on Non-negative Matrix Factorization methodology. The results are comparable to state-of-the-art formant tracking algorithms. With the use of a complete production model, the proposed systems provide robust formant tracks which can be used in various applications. The algorithms could also be extended to deal with multiple-speaker signals.
Grid computing in 3D-EM image processing using Xmipp

Image processing in three-dimensional electron microscopy (3D-EM) is characterized by large amounts of data, and voluminous computing requirements. Here, we report our first experience with grid computing in this area. We present an interface between grid computing middleware and our image processing package Xmipp. The efficacy of this approach was illustrated with an Xmipp application for estimation of the contrast transfer function. In addition, we report our experience with grid computing in the development of a novel image refinement algorithm based on maximum likelihood principles. Its extensive CPU-requirements might have seriously hampered the algorithm development, if not for the far-reaching resources of grid computing. Our results suggest that electron microscopy image processing may be particularly well suited for grid computing.