nils-herrmann/clean_scirepeval huggingface dataset:
Region-based satellite image classification: method and validation

We propose an algorithm for very high-resolution satellite image classification that combines non-supervised segmentation with a supervised classification. Both multi-spectral data and local spatial priors are used in the Gaussian hidden Markov random field (GHMRF) model for the segmentation. Then, two classifiers, Mahalanobis distance classifier and SVM, are studied using intensity, texture and shape features. Validation is done qualitatively and quantitatively by comparison with a manual classification used as a ground truth. Results show very good performance of our approach in comparison to existing techniques. Also, we demonstrate that spectral and spatial features calculated on segmented regions are much more discriminant than the spectral features of the pixels taken individually for the classification task.
Non-linear low-rank and sparse representation for hyperspectral image analysis

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the pixels to be expressed as a low-rank and sparse combination of the others in a reproducing kernel Hilbert spaces (RKHS). This captures the global (low-rank) and local (sparse) structures. Spectral clustering is applied on the graph to assign the pixels to the different manifolds. A large scale approach is proposed, in which the optimization is first performed on a subset of the data and then it is applied to the whole image using a non-linear collaborative representation respecting the manifolds structure. Experiments on two hyperspectral images show very good unsupervised classification results compared to competitive approaches.
A convex optimization framework for global tractography

In this article we present a novel approach for diffusion MRI global tractography. Our formulation models the signal in each voxel as a linear combination of fiber-tract basis functions, which consist of a comprehensive set of plausible fiber tracts that are locally compatible with the measured MR signal. This large dictionary of candidate fibers is directly estimated from the data and, subsequently, efficient convex optimization techniques are used for recovering the smallest subset globally best fitting the measured signal. Experimental results conducted on a realistic phantom demonstrate that our approach significantly reduces the computational cost of global tractography while still attaining a reconstruction quality at least as good as the state-of-the-art global methods.
Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

In this paper, we present a graph representation that is based on the assumption that data live on a union of manifolds. Such a representation is based on sample proximities in reproducing kernel Hilbert spaces and is thus linear in the feature space and nonlinear in the original space. Moreover, it also expresses sample relationships under sparse and low-rank constraints, meaning that the resulting graph will have limited connectivity (sparseness) and that samples belonging to the same group will be likely to be connected together and not with those from other groups (low rankness). We present this graph representation as a general representation that can be then applied to any graph-based method. In the experiments, we consider the clustering of hyperspectral images and semi-supervised classification (one class and multiclass).
Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.
Multi-modal medical image registration: from information theory to optimization objective

A relatively large class of information theoretical measures, including e.g. mutual information or normalized entropy, has been used in multi-modal medical image registration. Even though the mathematical foundations of the different measures were very similar, the final expressions turned out to be surprisingly different. Therefore one of the main aims of this paper is to enlight the relationship of different objective functions by introducing a mathematical framework from which several known optimization objectives can be deduced. Furthermore we extend existing measures in order to be applicable on image features different than image intensities and introduce "feature efficiency" as a very general concept to qualify such features. The presented framework is very general and not at all restricted to medical images. Still we want to discuss the possible impact of our theoretical framework for the particular problem of medical image registration, where the feature space has traditionally been fixed to image intensities. Our theoretical approach is very general though and can be used for any kind of multi-modal signals, such as for the broad field of multi-media applications.
PCA in autocorrelation space

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component Analysis (PCA) which, however, cannot be applied directly in autocorrelation space. In this paper we develop a new method for performing PCA in autocorrelation space, without explicitly computing the autocorrelations. Connections with nonlinear PCA and possible extensions are also discussed.
Atlas-based segmentation of pathological brain MR images

A method for brain atlas deformation in presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point is proposed. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. Finally, the seeded atlas is deformed, combining a method derived from optical flow principles and a model of lesion growth (MLG). Results show that the method can be applied to the automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery and radiotherapy.
Event-driven Scheduling for Parallel Stream Processing

To optimize real-time stream-processing applications for chip-level multi processors, several challenges have to be met. Poor scalability and poor internal data pressure may result from serial dependencies within or between the algorithms. Load imbalances introduced by the parallel-processing hardware and execution environment may also limit performance. To maximize the throughput and minimize the latency of parallel stream-processing applications, we propose an approach that complements run-time dynamic load balancing with static pre-compile partitioning. In our solution, the dynamic features are based on event-driven scheduling, while the static features benefit from profile-guided automatic optimizations. In this paper, we present some recent enhancements of DSPE, an open-source development environment, featuring model and source code generators for prototyping, refining and customizing real-time stream-processing applications. By using our approach on micro-benchmarks and sample applications, we also show that it is possible to reduce the impact of the different speed-up constrainers.
Automatic segmentation of internal structures of the brain in MR images using a tandem of affine and non-rigid registration of an anatomical brain atlas

In the study of many neurological pathologies, the accurate quantization of the white matter (WM) and gray matter (GM) volumes of the brain is essential Moreover, regional volume calculations may bring even more useful diagnostic information. We present therefore the segmentation of internal structures of the brain for further regional WM and GM volume quantization. A priori information about the brain anatomy is included in the segmentation process by the registration of the patient MR images with a computerized brain atlas. We propose the combination of a global affine transformation used to initialize key boundary surfaces (lateral ventricles and cortical surfaces) of both images with a local free-form transformation based on an optical flow algorithm. We apply this technique to segment the cerebellum and the cerebral trunk in order to exclude them from our WM and GM volume quantization. Validation has been conducted on a large number of images, showing excellent results.
CEC designer: Domain specific modelling for the industrial automation based on the IEC 61499 standard

Support for evolutionary design approaches is not a very much investigated topic in the automation domain. Advanced facilities for semantic validation, refactoring and model transformations, which are necessary for rapid prototyping of control applications, are seldom found in design tools. Implementing such facilities is even more difficult when using the IEC 61499 standard, through some characteristics of the persistence model. This paper presents a specialized development environment for modelling and automatically generating applications for the industrial automation field, its unique facilities for supporting evolutionary design and the suggested improvements to the IEC 61499 model. This environment was developed inside a project for assessing the IEC 61499 principles and for verifying design approaches and execution models on a real plant.
Iterative Full Head Finite Element Model for Deep Brain Stimulation

In order to understand the mechanisms involved in deep brain stimulation, several finite element models have been made using reduced part of the head and simplified boundary conditions. In this study, we present a method to obtain the potential created by deep brain stimulation while including the natural boundary conditions on the head surface. The use of an iterative procedure allows to have a detailed model of the electrode and of the surrounding tissues based on DT-MRI while applying more realistic boundary conditions than previous studies
Single-FPGA complete 3D and 2D medical ultrasound imager

3D ultrasound (US) acquisition acquires volumetric images, thus alleviating a classical US imaging bottleneck that requires a highly-trained sonographer to operate the US probe. However, this opportunity has not been explored in practice, since 3D US machines are only suitable for hospital usage in terms of cost, size and power requirements. In this work we propose the first fully-digital, single-chip 3D US imager on FPGA. The proposed design is a complete processing pipeline that includes pre-processing, image reconstruction, and post-processing. It supports up to 1024 input channels, which matches or exceeds state of the art, in an unprecedented estimated power budget of 6.1 W. The imager exploits a highly scalable architecture which can be either downscaled for 2D imaging, or further upscaled on a larger FPGA. Our platform supports both real-time inputs over an optical cable, or test data feeds sent by a laptop running Matlab and custom tools over an Ethernet connection. Additionally, the design allows HDMI video output on a screen.
Morphological feature extraction for the classification of digital images of cancerous tissues

Presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.
Experimental verification of optimal frequency range for microwave head imaging

This work presents the experimental verification of the power transmission of a radiating field into the human head for Microwave Imaging (MWI) applications. We aim at finding the range of frequencies that optimize the power scattered by any anomalies from inside to outside of the brain when excited by an incoming radiating field and hence improve the quality of microwave imaging. For this purpose, a 3D printed head phantom composed of liquids emulating dispersive biological head tissues in the range of [0.5–4]GHz has been built. Measurement results show that the wave penetrates well up to 1.3GHz as there is a strong attenuation around 2GHz, and low levels of transmitted power above 3GHz.
Soil Moisture Estimation by SAR in Alpine Fields Using Gaussian Process Regressor Trained by Model Simulations

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84.
Shot boundary detection with mutual information

We present a novel approach for shot boundary detection that uses mutual information (MI) and affine image registration. The MI measures the statistical difference between consecutive frames, while the applied affine registration compensates for camera panning and zooming. Results for different sequences are presented to illustrate the motion and zoom compensation and the robustness of MI to illumination changes. Furthermore we show that the affine registration has no effect at the shot boundaries themselves and therefore doesn't corrupt the result. Because the presented algorithm analyses the frames sequentially, its parallelization is straightforward even for distributed memory architectures. We quantify the speed-up on a LINUX cluster and show that the communicational load of the implementation is almost negligible, resulting in an almost linear speed-up.
A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms

Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.
Modelling human perception of static facial expressions

Data collected through a recent web-based survey show that the perception (i.e. labeling) of a human facial expression by a human observer is a subjective process, which results in a lack of a unique ground-truth, as intended in the standard classification framework. In this paper we propose the use of discrete choice models (DCM) for human perception of static facial expressions. Random utility functions are defined in order to capture the attractiveness, perceived by the human observer for an expression class, when asked to assign a label to an actual expression image. The utilities represent a natural way for the modeler to formalize her prior knowledge on the process. Starting with a model based on facial action coding systems (FACS), we subsequently defines two other models by adding two new sets of explanatory variables. The model parameters are learned through maximum likelihood estimation and a cross-validation procedure is used for validation purposes.
Single-FPGA 3D ultrasound beamformer

In medical diagnosis, ultrasound (US) imaging is one of the most common, safe, and powerful techniques. Volumetric (3D) US imaging, an emerging technique, is even more attractive than standard 2D imaging, as it allows for imaging without the local presence of a trained sonographer finely positioning the probe. This would be particularly useful in rescue operations, remote areas and developing countries. Unfortunately, present-day 3D imagers are expensive, bulky and power-hungry, confining them to hospitals. There is therefore a strong motivation to develop efficient electronics to enable a portable US platform that is small, cheap, and battery-operated. Beamforming (BF) is the most computationally expensive of 3D imaging. Both commercial  and research  imagers have dealt with the challenge by reducing the number of receive channels, hence simplifying the computation through the usage of far fewer elements. This comes at the cost of image quality, and the resulting machines are nonetheless still non-portable and expensive. In turn, the bottleneck of the BF process is the calculation of acoustic delays, which requires up to trillions of square roots per second. We propose a drastically more efficient architecture . With geometric considerations, each delay is calculated from a small set of square roots (mapped onto CORDICs), plus two additions. In this demo, we will show the reconstruction of a 2.5M-voxel volume, supporting a transducer with 32×32 receive channels. We have fitted the architecture into a single Kintex UltraScale KU040 , which is unprecedented. We also extrapolated the utilization of a 80×80 instance on a Virtex UltraScale XCVU190 . Table I shows the implementation results. Fig. 1 shows our beamformer custom block connected to the other FPGA subsystems. The delay calculation architecture is shown in Fig. 2. The demo setup is presented in Fig. 3, where the 3D beamformer is implemented on the FPGA, while the pre- and post-processing stages are currently performed on Matlab.
EVALUATION OF BRAIN IMAGE NONRIGID REGISTRATION ALGORITHMS BASED ON LOG-EUCLIDEAN MR-DTI CONSISTENCY MEASURES

Several nonrigid registration algorithms have been proposed for inter-subject alignment, used to construct statistical atlases and to identify group differences. Assessment of the accuracy of nonrigid registration algorithms is an essential and complex issue due to its intricate framework and its application-dependent behavior. We demonstrate that the diffusion MRI provides an independent means of assessing the quality of alignment achieved on the structural MRI. Diffusion tensor MRI (DT-MRI) enables the comparison of the local position and orientation of regions that appear homogeneous in conventional MRI. We carried out inter-subject alignment of conventional T1-weighted MRI with three different registration algorithms. Consequently, we projected DT-MRI of each subject through the same inter-subject transformation. The quality of the inter-subject alignment is assessed by estimating the consistency of the aligned DT-MRI using the Log-Euclidean framework
Extraction of the optic disk boundary in digital fundus images

The methods of active contours ("snakes") and level sets were applied to images of the retina in order to locate the outer boundary of the optic disk. A gradient-vector-flow based active contour was used as it performed well over a large range of initial conditions. Images were pre-processed to lessen the influence of blood vessels on boundary detection. Both active contours and level set methods accurately located the correct boundary; level set methods were computationally more intensive.
Fast globally supervised segmentation by active contours with shape and texture descriptors

We present a new globally supervised segmentation method in the characteristic function framework based on an active contours (AC) model incorporating both shape prior and texture descriptors. The shape prior descriptor is formulated as the traditional Legendre moment and the texture descriptor as a linear combination of local inside/outside texture descriptor. Using these two descriptors, the AC energy incorporates both learned textures and training shapes. This formulation has two main advantages: 1) by discriminating independently the foreground/background textures. 2) by incorporating both the learned inside/outside texture and the training shape. The trade-off between inside and outside texture descriptor is ensured by balancing descriptor. We illustrate the performance of our segmentation algorithm using some challenging textured images.