nils-herrmann/clean_scirepeval huggingface dataset:
Inexpensive 1024-channel 3D telesonography system on FPGA

Volumetrie ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today's 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this study, we propose the first telesonography-capable medical imaging system that supports up to 1024 channels, on par with the state of the art. As a first embodiment, we have implemented our design in a single development FPGA board of 26.7cm×14cm×0.16cm, with an estimated power consumption of 6.1 W. Moreover, we have equipped our platform with an automatic positioning module to help any operator defining the scan location, hence allowing for better remote diagnosis. Our design supports two types of data inputs: real-time via an optical connection and offline over Ethernet. The reconstructed images can be visualized on an HDMI screen. The estimated cost of the proposed prototype materials is less than 4000€.
A realistic Computed Tomography simulator for small motion analysis of cerebral aneurysms

This paper describes a realistic simulator for the Computed Tomography (CT) scan process for motion analysis. In fact, we are currently developing a new framework to find small motion from the CT scan. In order to prove the fidelity of this framework, or potentially any other algorithm, we present in this paper a simulator to simulate the whole CT acquisition process with a priori known parameters. In other words, it is a digital phantom for the motion analysis that can be used to compare the results of any related algorithm with the ground-truth realistic analytical model. Such a simulator can be used by the community to test different algorithms in the biomedical imaging domain. The most important features of this simulator are its different considerations to simulate the best the real acquisition process and its generality.
Discriminant multi-label manifold embedding for facial Action Unit detection

This article describes a system for participation in the Facial Expression Recognition and Analysis (FERA2015) sub-challenge for spontaneous action unit occurrence detection. The problem of AU detection is a multi-label classification problem by its nature, which is a fact overseen by most existing work. The correlation information between AUs has the potential of increasing the detection accuracy. We investigate the multi-label AU detection problem by embedding the data on low dimensional manifolds which preserve multi-label correlation. For this, we apply the multi-label Discriminant Laplacian Embedding (DLE) method as an extension to our base system. The system uses SIFT features around a set of facial landmarks that is enhanced with the use of additional non-salient points around transient facial features. Both the base system and the DLE extension show better performance than the challenge baseline results for the two databases in the challenge, and achieve close to 50% as F1-measure on the testing partition in average (9.9% higher than the baseline, in the best case). The DLE extension proves useful for certain AUs, but also shows the need for more analysis to assess the benefits in general.
Shape prior based on statistical map for active contour segmentation

We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the probability for each point to belong to the object. This statistical map is treated as a prior distribution and an energy functional is defined such that the object reaches the most probable shape knowing the model. The optimization is done in the level-set framework. Results on both synthetic and medical images are shown.
Extraction of Audio Features Specific to Speech Production for Multimodal Speaker Detection

A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an Mi-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdfs) of the features, nor for the cost function itself. The pdfs are estimated from the samples using a nonparametric approach. The challenging optimization problem is solved using a global method: the differential evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech production is discussed. Using these specific audio features, candidate video features are then classified as member of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on in-house test sequences, and of 85% on most commonly used sequences.
Joining a Discrete Radiative Transfer Model and a Kernel Retrieval Algorithm for Soil Moisture Estimation From SAR Data

This paper investigates the problem of retrieving soil moisture under crops using Synthetic Aperture Radar (SAR) data. First, we simulated the time series of L-band SAR signals over agricultural fields using a discrete radiative transfer model (RTM). Full growth cycles of winter wheat, maize, and sugar beet fields sampled during the AgriSAR2006 (Agricultural bio/geophysical retrievals from frequent repeat pass SAR and optical imaging) field campaign were considered. A generally good correspondence between the simulated crop backscattering coefficients and those measured by the airborne L-band E-SAR (Experimental-SAR) system was observed with an average rootmean-square error (RMSE) of 2.32 dB. The highest RMSE of 3.63 dB was obtained by the RTM simulations of 11V polarized signals in the wheat field, whereas the smallest RMSE of 1.63 dB is achieved in RTM simulations of 11V backscattering coefficients in the field of sugar beet. All discrepancies were critically discussed and interpreted. Then, soil moisture was estimated using a nonlinear inversion technique, support vector regression (ν-SVR). The model was trained with the backscatter model simulations obtained by the RTM. For all fields considered, the RMSE of the predicted soil moisture was smaller than 5.5% Vol. and the corresponding correlation coefficient (r) was equal to or higher than 0.71.
Binary Active Contours using both inside and outside texture descriptors

In this paper, we propose a new framework for Binary Active Contours (AC) that incorporates a new texture descriptor. The texture descriptor is split into inside/ outside region descriptors. Both the inside and outside texture descriptors discriminate the texture using Kullback-Leibler distance. Using these two descriptors, the AC incorporates both learned textures. This formulation has two main advantages. Firstly, by discriminating independently the foreground/background textures. Secondly, by incorporating both the learned inside/outside texture. Our segmentation model based AC model is formulated in Total variation framework using characteristic function framework. We propose a fast Bregman split implementation of our segmentation algorithm based on the primal-dual formulation. Finally, we show results on some challenging images to illustrate texture segmentations that are possible.
Non-Euclidean image-adaptive Radial Basis Functions for 3D interactive segmentation

In the context of variational image segmentation, we propose a new finite-dimensional implicit surface representation. The key idea is to span a subset of implicit functions with linear combinations of spatially-localized kernels that follow image features. This is achieved by replacing the Euclidean distance in conventional Radial Basis Functions with non-Euclidean, image-dependent distances. For the minimization of an objective region-based criterion, this representation yields more accurate results with fewer control points than its Euclidean counterpart. If the user positions these control points, the non-Euclidean distance enables to further specify our localized kernels for a target object in the image. Moreover, an intuitive control of the result of the segmentation is obtained by casting inside/outside labels as linear inequality constraints. Finally, we discuss several algorithmic aspects needed for a responsive interactive workflow. We have applied this framework to 3D medical imaging and built a real-time prototype with which the segmentation of whole organs is only a few clicks away.
Hierarchical sparse representation for dictionary-based classification of hyperspectral images

The recent advances in sparse coding and dictionary learning have shown extremely good performances and robustness in high-dimensional classification problems. Most often, dictionary-based methods rely either on the reconstruction power of the dictionary or on the structure of the sparse representation. In this paper we jointly exploit the discrimination power of both approaches by combining the reconstruction error with the hierarchical information of the sparse codes collected during the learning stage. The proposed method performs similarly to state-of-the-art classifiers and outperforms them sharply in small sample situations, where the number of patterns used to learn the dictionaries is much smaller than the number of dimensions.
Unsupervised change detection via hierarchical support vector clustering

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system.
Variational image segmentation by unifying region and boundary information

This paper presents a novel variational image segmentation technique that unifies both geodesic active contours and geodesic active regions. The originality of the method is the automatic and dynamic global weighting of the respective local equations of motion. A new stopping function for the geodesic active contours is also introduced, which proves to have a better behavior in the vicinity of the object boundaries. Instead of minimizing the standard energy functional, we use a normalized version, which strongly reduces the shortening effect, improving thus the coupling with the region model. Results and method effectiveness are shown on real and medical images.
Variational Segmentation using Fuzzy Region Competition and Local Non-Parametric Probability Density Functions

We describe a novel variational segmentation algorithm designed to split an image in two regions based on their intensity distributions. A functional is proposed to integrate the unknown probability density functions of both regions within the optimization process. The method simultaneously performs segmentation and non-parametric density estimation. It does not make any assumption on the underlying distributions, hence it is flexible and can be applied to a wide range of applications. Although a boundary evolution scheme may be used to minimize the functional, we choose to consider an alternative formulation with a membership function. The latter has the advantage of being convex in each variable, so that the minimization is faster and less sensitive to initial conditions. Finally, to improve the accuracy and the robustness to low-frequency artifacts, we present an extension for the more general case of local space-varying probability densities. The approach readily extends to vectorial images and 3D volumes, and we show several results on synthetic and photographic images, as well as on 3D medical data.
Comparison of energy minimization methods for 3-D brain tissue classification

This paper presents 3-D brain tissue classification schemes using three recent promising energy minimization methods for Markov random fields: graph cuts, loopy belief propagation and tree-reweighted message passing. The classification is performed using the well known finite Gaussian mixture Markov Random Field model. Results from the above methods are compared with widely used iterative conditional modes algorithm. The evaluation is performed on a dataset containing simulated T1-weighted MR brain volumes with varying noise and intensity non-uniformities. The comparisons are performed in terms of energies as well as based on ground truth segmentations, using various quantitative metrics.
A Regression-Based User Calibration Framework for Real-Time Gaze Estimation

Eye movements play a very significant role in human–computer interaction (HCI) as they are natural and fast, and contain important cues for human cognitive state and visual attention. Over the last two decades, many techniques have been proposed to accurately estimate the gaze. Among these, video-based remote eye trackers have attracted much interest, since they enable nonintrusive gaze estimation. To achieve high estimation accuracies for remote systems, user calibration is inevitable in order to compensate for the estimation bias caused by person-specific eye parameters. Although several explicit and implicit user calibration methods have been proposed to ease the calibration burden, the procedure is still cumbersome and needs further improvement. In this paper, we present a comprehensive analysis of regression-based user calibration techniques. We propose a novel weighted least squares regression-based user calibration method together with a real-time cross-ratio based gaze estimation framework. The proposed system enables to obtain high estimation accuracy with minimum user effort, which leads to user-friendly HCI applications. Experimental results conducted on both simulations and user experiments show that our framework achieves a significant performance improvement over the state-of-the-art user calibration methods when only a few points are available for the calibration.
Robust gaze estimation based on adaptive fusion of multiple cameras

Gaze movements play a crucial role in human-computer interaction (HCI) applications. Recently, gaze tracking systems with a wide variety of applications have attracted much interest by the industry as well as the scientific community. The state-of-the-art gaze trackers are mostly non-intrusive and report high estimation accuracies. However, they require complex setups such as camera and geometric calibration in addition to subject-specific calibration. In this paper, we introduce a multi-camera gaze estimation system which requires less effort for the users in terms of the system setup and calibration. The system is based on an adaptive fusion of multiple independent camera systems in which the gaze estimation relies on simple cross-ratio (CR) geometry. Experimental results conducted on real data show that the proposed system achieves a significant accuracy improvement, by around 25%, over the traditional CR-based single camera systems through the novel adaptive multi-camera fusion scheme. The real-time system achieves <;0.9° accuracy error with very few calibration data (5 points) under natural head movements, which is competitive with more complex systems. Hence, the proposed system enables fast and user-friendly gaze tracking with minimum user effort without sacrificing too much accuracy.
A Surface-Based Approach to Quantify Local Cortical Gyrification

The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11  Syndrome, compared to control individuals.
Overcoming asynchrony in Audio-Visual Speech Recognition

In this paper we propose two alternatives to overcome the natural asynchrony of modalities in Audio-Visual Speech Recognition. We first investigate the use of asynchronous statistical models based on Dynamic Bayesian Networks with different levels of asynchrony. We show that audio-visual models should consider asynchrony within word boundaries and not at phoneme level. The second approach to the problem includes an additional processing of the features before being used for recognition. The proposed technique aligns the temporal evolution of the audio and video streams in terms of a speech-recognition system and enables the use of simpler statistical models for classification. On both cases we report experiments with the CUAVE database, showing the improvements obtained with the proposed asynchronous model and feature processing technique compared to traditional systems.
IMIS: A multi-platform software package for telediagnosis and 3D medical image processing

We present a project developed in our University in order to provide Medical Imaging Departments with efficient software tools for 3D medical image processing and transmission. In this context, the IMIS software package has been developed, combining, in a modular programming strategy, an easily upgradable graphical user interface, using the Tcl/Tk toolkit, with high performance image processing techniques, such as 3D lossless multiresolution image compression for telediagnosis on narrow-band ISDN networks. IMIS is based on a set of tools allowing the handling of 3D medical images such as magnetic resonance images (MRI), positron emission tomography (PET), and computed tomography (CT) of functional MRI (fMRI).
Morphological classification of cancerous cells

We present a new method for the automatic recognition of cancerous cells from a digitized picture of a microscopic section. The method is based on the analysis of four criteria of malignancy, in relation with the shape and the size of the observed cells. It provides the physician with nonsubjective numerical values for the four concerned criteria of malignancy, in order to help him to decide whether the tissue is cancerous or not. The automatic approach described uses mathematical morphology, first to remove the background noise from the image and to operate a segmentation of the nuclei of the cells, which contain most of the features of malignancy, next to analyse the shape and the size of these nuclei and lastly to evaluate their texture. From the values of the four extracted criteria, an automatic classification of the image is using a Kohonen neural network.<<ETX>>
Semi-supervised and unsupervised novelty detection using nested support vector machines

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using the One-Class SVM (OC-SVM). We propose here to use nested entire solution path algorithms for the OC-SVM and CS-SVM in order to accelerate the parameter selection and alleviate the dependency to labeled “changed” samples. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and the performance of the two methods proposed compared.
Detecting emotional stress from facial expressions for driving safety

Monitoring the attentive and emotional status of the driver is critical for the safety and comfort of driving. In this work a real-time non-intrusive monitoring system is developed, which detects the emotional states of the driver by analyzing facial expressions. The system considers two negative basic emotions, anger and disgust, as stress related emotions. We detect an individual emotion in each video frame and the decision on the stress level is made on sequence level. Experimental results show that the developed system operates very well on simulated data even with generic models. An additional pose normalization step reduces the impact of pose mismatch due to camera setup and pose variation, and hence improves the detection accuracy further.
Automatic prostate segmentation in cone-beam computed tomography images using rigid registration

We propose to evaluate automatic three-dimensional gray-value rigid registration (RR) methods for prostate localization on cone-beam computed tomography (CBCT) scans. In total, 103 CBCT scans of 9 prostate patients have been analyzed. Each one was registered to the planning CT scan using different methods: (a) global RR, (b) pelvis bone structure RR, (c) bone RR refined by local soft-tissue RR using the CT clinical target volume (CTV) expanded with a 1, 3, 5, 8, 10, 12, 15 or 20-mm margin. To evaluate results, a radiation oncologist was asked to manually delineate the CTV on the CBCT scans. The Dice coefficients between each automatic CBCT segmentation - derived from the transformation of the manual CT segmentation - and the manual CBCT segmentation were calculated. Global or bone CT/CBCT RR has been shown to yield insufficient results in average. Local RR with an 8-mm margin around the CTV after bone RR was found to be the best candidate for systematically significantly improving prostate localization.
Combining LiDAR space clustering and convolutional neural networks for pedestrian detection

Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches. In this work, we develop a new pedestrian detector for autonomous vehicles that exploits LiDAR data, in addition to visual information. In the proposed approach, LiDAR data is utilized to generate region proposals by processing the three dimensional point cloud that it provides. These candidate regions are then further processed by a state-of-the-art CNN classifier that we have fine-tuned for pedestrian detection. We have extensively evaluated the proposed detection process on the KITTI dataset. The experimental results show that the proposed LiDAR space clustering approach provides a very efficient way of generating region proposals leading to higher recall rates and fewer misses for pedestrian detection. This indicates that LiDAR data can provide auxiliary information for CNN-based approaches.