nils-herrmann/clean_scirepeval huggingface dataset:
Longitudinal in-vivo volumetry study for porcine liver regeneration from CT data

The use of hepatic-like cloned cord lining epithelial cells (CLEC) to enhance liver regeneration has been proposed, but has not been properly investigated in a large animal study. The paper presents a system developed for the longitudinal in-vivo volumetry study on porcine liver regeneration from computed tomography (CT) data. In this system, a rough 3D liver volume is firstly automatically segmented by a 3D mesh deformation-based method. Then a refinement step to eliminate the segmentation error is carried out by a 3D post-editing tool, followed by mesh-volume conversion and volume calculation. This system was applied in a pilot study, which was composed of 4/4 pigs in the Experimental/Control Groups, to measure liver volumes over pre- to post-operative time course. Experimental results suggest that (1) the developed system can perform CT-based porcine liver volumetry efficiently, and (2) the infusion of CLEC to liver remnant may potentially enhance the liver regeneration.
Efficient and robust image segmentation with a new piecewise-smooth decomposition model

Image segmentation is to separate the image domain into local regions. Implicit active contour models via the level-set method can provide smooth and closed contours. The major category is based on the Mumford-Shah image component decomposition model. Approximating the image domain by a set of homogeneous regions, segmentation based on the piecewise constant (PC) models fail in intensity inhomogeneous images. Requiring intensity smoothness in local regions, segmentation based on the piecewise smooth (PS) models can perform better for intensity inhomogeneity than those PC based. Existing PS-based techniques are inefficient and not robust to complicate intensity scenarios with noise. Here we introduce a new functional model by decomposing an image into three parts: PS components, PC components and noise components. We convexify the decomposition model by incorporating with relaxation techniques and optimize the PS components over the whole image domain. New numerical algorithms are also proposed to implement the above approaches efficiently. Numerical validation experiments show that the proposed approaches can achieve much faster, more robust and more accurate image segmentation than existing arts.
Tsunami Detection and Parameter Estimation From GNSS-R Delay-Doppler Map

In this paper, a scheme is developed to detect tsunamis and estimate tsunami parameters from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs) of a tsunami-dominant sea surface. First, a procedure to determine tsunami-induced sea surface height anomalies (SSHAs) from DDMs is presented, and a tsunami detection precept is proposed. Subsequently, the tsunami parameters (wave amplitude, direction and speed of propagation, wavelength, and the tsunami source location) are estimated based upon the detected tsunami-induced SSHAs. In application, the sea surface scattering coefficients are unambiguously retrieved by employing the spatial integration approach (SIA) and the dual-antenna technique. Next, the effective wind speed distribution can be restored from the scattering coefficients. Assuming that all DDMs are of a tsunami-dominated sea surface, the tsunami-induced SSHAs can be derived with the knowledge of background wind speed distribution. In addition, the SSHA distribution resulting from the tsunami-free DDM (which is supposed to be zero) is considered as an error map introduced during the overall retrieving stage and is utilized to mitigate such errors from influencing subsequent SSHA results. In particular, a tsunami detection procedure is conducted to judge the SSHAs to be truly tsunami-induced or not through a fitting process, which makes it possible to decrease the false alarm. After this step, tsunami parameter estimation is processed based upon the fitted results in the former tsunami detection procedure. Furthermore, an additional method is proposed for estimating tsunami propagation velocity and is believed to be more desirable in real-world scenarios. The above-mentioned tsunami detection precept and parameter estimation have been tested with simulated data based on the 2004 Sumatra-Andaman tsunami event. The errors of estimated tsunami wave amplitude, propagation direction and velocity, wavelength, and source location are 0.03 cm, 1.16°, 1.45 m/s, 5.82 km, and (-0.22°N, -0.67°E), respectively.
Dual Antenna Space-Based GNSS-R Ocean Surface Mapping: Oil Slick and Tropical Cyclone Sensing

A technique to sense sea surface features using the reflections from Global Navigation Satellite System (GNSS) satellites is presented in this paper, in which a novel method for compensating the distortions induced during the delay-Doppler map (DDM) deconvolution process of scattering coefficient retrieval is proposed. A distortion distribution generated using a clean ocean surface is used as a baseline for the inaccuracy due to the constrained least square (CLS) filter. This baseline distribution is used to correct the CLS distortions of “deblurred” DDMs generated from various ocean surface conditions. Subsequently, a spatial integration approach (SIA) is applied to retrieve the scattering coefficients unambiguously using the DDMs obtained by two separate antenna beams. Additionally, the proposed technique is then applied to oil slick detection and cyclone mapping. A performance characterization including retrieval accuracy and resolution is demonstrated with respect to wind speed (WS), oil slick size, and cyclone location. Various simulations show that the retrieval error can be reduced by the SIA after the distortion correction. The technique proposed here can be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDMs from a single space-based platform.
Wind Direction Estimation From Rain-Contaminated Marine Radar Data Using the Ensemble Empirical Mode Decomposition Method

Two ensemble empirical mode decomposition (EEMD)-based methods are presented to retrieve wind direction from rain-contaminated X-band nautical radar sea surface images. Each radar image is first decomposed into disparate intrinsic mode function (IMF) components using 1-D EEMD or 2-D EEMD. Then, the standard deviation of one IMF component or the combination of several IMF components as a function of azimuth is least-squares fitted to a harmonic function to determine the wind direction. Tests of the proposed algorithms are conducted by employing radar and anemometer data collected in a sea trial during rain events off the east coast of Canada. The results show that compared with the 1-D discrete-Fourier-transform-based method, both the 1-D- and 2-D-EEMD-based algorithms improve the wind direction results in rain events, showing a reduction of 7.4° and 8.7°, respectively, in the root-mean-square difference with respect to the reference.
Analysis of the effects of rain on surface wind retrieval from X-band marine radar images

In this paper, the effects of rain on sea surface wind retrieval from X-band shipborne marine radar images are investigated. Two wind algorithms are applied to the shipborne radar data collected from the Eastern Canadian Coast. It is observed that rain enhances the backscatter intensity of the HH-polarized X-band radar images of the sea surface and results in the radar-retrieved wind speed being overestimated. In the presence of rain, the standard deviation (STD) of wind speed and wind direction using modified intensity-level-selection (ILS) based method increases by about 1.6 m/s and 10.7°, respectively, compared to the results obtained from rain-contamination-free data.
An analytical model for HF radar ionospheric clutter

In this paper, an analytic model for the mixed path mode of ionospheric clutter associated with high frequency surface wave radars (HFSWR) operating in an ocean environment is presented. Based on earlier work, an expression for the first-order received electric field after a single scatter from each of the ionosphere and sea surface is derived and reduced to integral form. The integrals are taken to the time domain, with the source field being that of a vertically polarized pulsed dipole antenna. Subsequently, the first-order radar cross section for ionosphere clutter is developed. Simulation results for the new cross section are also provided. It is shown numerically that the expected magnitude of the ionosphere clutter, under reasonable assumptions, exceeds the dominant first order ocean clutter for the same apparent range by 40-50 dB. Further, it is spread over several Hertz in Doppler, depending on ionosphere horizontal velocity and reflecting path non-uniformity. Also, this component can be present from a certain minimum range, depending on ionosphere virtual height, out to the maximum radar range.
Liver tumor detection and segmentation using kernel-based extreme learning machine

This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or nontumor class. A voxel is represented with a rich feature vector that distinguishes itself from voxels in different classes. A fast learning algorithm Extreme Learning Machine (ELM) is trained as a voxel classifier. In automatic liver tumor detection, we propose and show that ELM can be trained as a one-class classifier with only healthy liver samples in training. It results in a method of tumor detection based on novelty detection. We compare it with two-class ELM. To extract the boundary of a tumor, we adopt the semi-automatic approach by randomly selecting samples in 3D space within a limited region of interest (ROI) for classifier training. Our approach is validated on a group of patients' CT data and the experiment shows good detection and encouraging segmentation results.
First-order high frequency radar ocean surface cross section incorporating a dual-frequency platform motion model

The first-order high frequency radar cross section of the ocean surface is derived for an antenna on a floating platform. In this analysis, simulations are conducted for more complicated platform motion than appear in earlier work and comparisons are made to model outputs for a fixed antenna. Results show that motion-induced peaks appear symmetrically in the Doppler frequency. The magnitude and width of the Bragg peaks are seen to decrease and broaden, respectively, as compared to the case for a fixed antenna.
An alternate analysis for the second-order high frequency bistatic radar cross section of the ocean surface - patch scatter and its inversion

In this paper, the verification of the second-order high frequency (HF) ocean surface cross section for a surface patch remote from the transmitter and receiver is presented. The bistatic result is shown to reduce to the proper monstatic models when the appropriate geometry for backscatter is introduced. In particular, the electromagnetic coupling coefficient for the bistatic case is seen to incorporate its monostatic counterpart. It is well-known that the second order portion of the HF radar spectrum of the ocean echo contains substantial information on the surface gravity wave spectrum. Thus, the initial analysis forms the basis for the extraction of ocean surface parameters from bistatic HF radar data. An inversion technique for extraction of ocean surface wave parameters from bistatic HF radar data is briefly developed here. The inversion results from simulated spectra are seen to be very reasonable.
Cooperative Multitarget Tracking With Efficient Split and Merge Handling

For applications such as behavior recognition it is important to maintain the identity of multiple targets, while tracking them in the presence of splits and merges, or occlusion of the targets by background obstacles. Here we propose an algorithm to handle multiple splits and merges of objects based on dynamic programming and a new geometric shape matching measure. We then cooperatively combine Kalman filter-based motion and shape tracking with the efficient and novel geometric shape matching algorithm. The system is fully automatic and requires no manual input of any kind for initialization of tracking. The target track initialization problem is formulated as computation of shortest paths in a directed and attributed graph using Dijkstra's shortest path algorithm. This scheme correctly initializes multiple target tracks for tracking even in the presence of clutter and segmentation errors which may occur in detecting a target. We present results on a large number of real world image sequences, where upto 17 objects have been tracked simultaneously in real-time, despite clutter, splits, and merges in measurements of objects. The complete tracking system including segmentation of moving objects works at 25 Hz on 352times288 pixel color image sequences on a 2.8-GHz Pentium-4 workstation
Face detection based on color and local symmetry information

As we know, a robust approach to face and facial features detection must be able to handle the variation issues such as changes in imaging conditions, face appearances and image contents. Here we present a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. These two maps are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. Experimental results have demonstrated its validity and robustness to identify faces under certain variations.
Multiple Objects Tracking with Multiple Hypotheses Graph Representation

We present a novel multi-object tracking algorithm based on multiple hypotheses about the trajectories of the objects. Our work is inspired by Reid's multiple hypothesis tracking algorithm which is an optimal solution to the motion correspondence that occurs in multi-object tracking. Unfortunately, the exponential growth of the hypotheses tree precludes practical applications. To restrict this growth, many approximations relying on a series of clustering and pruning operations have been proposed. The decisions for these operations are based solely on previous observations and are not guided by observations in later frames. We show that due to multiple splits and merges, relying solely on previous observations to guide these operations may inadvertently eliminate the correct hypothesis. Consequently, this leads to poor tracking performance. To overcome this problem, we determine the validity of a hypothesis by exploiting information in later frames and relating them to previous observations. Experimental results demonstrate the robustness and efficiency of our approach
Wind Speed Estimation From X-Band Marine Radar Images Using Support Vector Regression Method

A support vector regression (SVR)-based method for estimating wind speed from X-band marine radar images is proposed. The dependence of histogram pattern of radar images on wind speed and rain condition is first observed. Then, the feature vectors based on bin values of histograms are extracted and trained using an SVR algorithm. Radar images and anemometer data collected from several periods in a sea trial of the east coast of Canada are used for model training and testing. Experimental results show that compared with the ensemble empirical mode decomposition-based methods, the accuracy of wind speed estimation is improved with a reduction of about 0.14 m/s for rain-free images and 0.11 m/s for rain-contaminated images in root mean square error. Moreover, the proposed method also shows high efficiency by greatly reducing the computational time.
Volume Preserved Mass–Spring Model with Novel Constraints for Soft Tissue Deformation

An interactive surgical simulation system needs to meet three main requirements, speed, accuracy, and stability. In this paper, we present a stable and accurate method for animating mass-spring systems in real time. An integration scheme derived from explicit integration is used to obtain interactive realistic animation for a multiobject environment. We explore a predictor- corrector approach by correcting the estimation of the explicit integration in a poststep process. We introduce novel constraints on positions into the mass-spring model (MSM) to model the nonlinearity and preserve volume for the realistic simulation of the incompressibility. We verify the proposed MSM by comparing its deformations with the reference deformations of the nonlinear finite-element method. Moreover, experiments on porcine organs are designed for the evaluation of the multiobject deformation. Using a pair of freshly harvested porcine liver and gallbladder, the real organ deformations are acquired by computed tomography and used as the reference ground truth. Compared to the porcine model, our model achieves a 1.502 mm mean absolute error measured at landmark locations for cases with small deformation (the largest deformation is 49.109 mm) and a 3.639 mm mean absolute error for cases with large deformation (the largest deformation is 83.137 mm). The changes of volume for the two deformations are limited to 0.030% and 0.057%, respectively. Finally, an implementation in a virtual reality environment for laparoscopic cholecystectomy demonstrates that our model is capable to simulate large deformation and preserve volume in real-time calculations.
Bistatic High-Frequency Radar Ocean Surface Cross Section Incorporating a Dual-Frequency Platform Motion Model

The first- and second-order bistatic high-frequency radar cross sections of the ocean surface are derived with a dual-frequency platform motion model. Simulations are made to illustrate how the bistatic angle affects the cross section. Not surprisingly, the energy of the second-order radar cross section is seen to decrease as the bistatic angle increases. Simulations also depict the relation between the bistatic angle and the modulation level caused by the platform motion. This work provides a theoretical foundation for future practical investigations to determine suitable geometries for the deployment of a platform-mounted high-frequency bistatic surface wave radar.
Improving the accuracy of erroneous-plan recognition system for Activities of Daily Living

Using ambient intelligence to assist people with dementia in carrying out their Activities of Daily Living (ADLs) independently in smart home environment is an important research area, due to the projected increasing number of people with dementia. We present herein, a system and algorithms for the automated recognition of ADLs; the ADLs are in terms of plans made up encoded sequences of micro-context information gathered by sensors in a smart home. Previously, the Erroneous-Plan Recognition (EPR) system was developed to specifically handle the wide spectrum of micro contexts from multiple sensing modalities. The EPR system monitors the person with dementia and determines if he has executed a correct or erroneous ADL. However, due to the noisy readings of the sensing modalities, the EPR system has problems in accurately detecting the erroneous ADLs. We propose to improve the accuracy of the EPR system by two new key components. First, we model the smart home environment as a Markov decision process (MDP), with the EPR system built upon it. Simple referencing of this model allows us to filter erroneous readings of the sensing modalities. Second, we use the reinforcement learning concept of probability and reward to infer erroneous readings that are not filtered by the first key component.We conducted extensive experiments and showed that the accuracy of the new EPR system is 26.2% higher than the previous system, and is therefore a better system for ambient assistive living applications.
Wind direction retrieval from rain-contaminated X-band nautical radar images

In this paper, an algorithm for retrieving wind direction from rain-contaminated radar images collected under low wind speed conditions is presented. The algorithm investigates radar backscatter in the wavenumber domain and determines wind directions based on spectral components with wavenumbers of [0.01, 0.2]. The algorithm has been tested using rain-contaminated X-band marine radar images and shipborne anemometer data collected on the east coast of Canada. Comparison with the anemometer data shows the root mean square error of wind direction retrieved from rain-contaminated images collected under low wind speeds is reduced by 25.2°.
Surface Current Measurement Under Low Sea State Using Dual Polarized X-Band Nautical Radar

In this paper, a comparison of surface current velocity extraction under low sea state from horizontal and vertical polarized X-band nautical radar image sequences is presented. Three different current retrieval algorithms including the classical least-squares (LS) fitting, a modified iterative least-square fitting routine and an improved normalized scalar product (NSP) method have been employed. An adaptive iteration termination criterion provides optimal times of iteration for the iterative LS method. Variable-search ranges and resolutions are proposed to reduce the computational cost for the NSP method. Field data from two X-band radars deployed during a short experiment at the Skerries Bight near St. John's, Newfoundland is analyzed. Comparison of the results derived from the radar and in-situ buoy data shows that vertical polarization leads to better current measurements than horizontal polarization even under very low sea state. A performance comparison of LS, iterative LS and NSP algorithms indicates that the latter two provide reliable results, with the current measurements from the NSP method being the least variable.
Wind direction determination from rain-contaminated X-band radar images

A two-dimensional ensemble empirical mode decomposition (2D-EEMD)-based method is presented to improve wind direction retrieval from rain-contaminated X-band nautical radar sea surface images. 2D-EEMD is first implemented to decompose each rain-contaminated radar image into several intrinsic mode function (IMF) components. Then, a harmonic function that is least-squares fitted to the standard deviation of the first IMF component as a function of azimuth is used to retrieve the wind direction. Radar and anemometer data acquired in a sea trial off the east coast of Canada under rain conditions are employed to test the algorithm. The result shows that, compared to the traditional curve fitting method, the proposed method improves the wind direction results in rain events, showing a reduction of 35.9° in the root-mean-square (RMS) difference with respect to the reference.
Tracking Multiple Objects using Probability Hypothesis Density Filter and Color Measurements

Most methods for multiple object tracking in video represent the state of multi-object in a high dimensional joint state space. This leads to high computational complexity. This paper presents a method using the probability hypothesis density (PHD) filter to estimate the state of multiple objects in video. The method operates on the single object state space instead of the joint state space. A PHD recursion for visual observations with color measurements is proposed. Our method can track varying number of objects.