nils-herrmann/clean_scirepeval huggingface dataset:
Image deconvolution by stein block thresholding

In this paper, we propose a fast image deconvolution algorithm that combines adaptive block thresholding and Vaguelet-Wavelet Decomposition. The approach consists in first denoising the observed image using a wavelet-domain Stein block thresholding, and then inverting the convolution operator in the Fourier domain. Our main theoretical result investigates the minimax rates over Besov smoothness spaces, and shows that our block estimator can achieve the optimal minimax rate, or is at least nearly-minimax in the least favorable situation. The resulting algorithm is simple to implement and fast. Its computational complexity is dominated by that of the FFT in the Fourier-domain inversion step. We report a simulation study to support our theoretical findings. The practical performance of our block vaguelet-wavelet deconvolution compares very favorably to existing competitors on a large set of test images.
Inverse problems with poisson noise: Primal and primal-dual splitting

In this paper, we propose two algorithms for solving linear inverse problems when the observations are corrupted by Poisson noise. A proper data fidelity term (log-likelihood) is introduced to reflect the Poisson statistics of the noise. On the other hand, as a prior, the images to restore are assumed to be positive and sparsely represented in a dictionary of waveforms. Piecing together the data fidelity and the prior terms, the solution to the inverse problem is cast as the minimization of a non-smooth convex functional. We establish the well-posedness of the optimization problem, characterize the corresponding minimizers, and solve it by means of primal and primal-dual proximal splitting algorithms originating from the field of non-smooth convex optimization theory. Experimental results on deconvolution and comparison to prior methods are also reported.
A Proximal Iteration for Deconvolving Poisson Noisy Images Using Sparse Representations

We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transforms. Our key contributions are as follows. First, we handle the Poisson noise properly by using the Anscombe variance stabilizing transform leading to a nonlinear degradation equation with additive Gaussian noise. Second, the deconvolution problem is formulated as the minimization of a convex functional with a data-fidelity term reflecting the noise properties, and a nonsmooth sparsity-promoting penalty over the image representation coefficients (e.g., lscr1 -norm). An additional term is also included in the functional to ensure positivity of the restored image. Third, a fast iterative forward-backward splitting algorithm is proposed to solve the minimization problem. We derive existence and uniqueness conditions of the solution, and establish convergence of the iterative algorithm. Finally, a GCV-based model selection procedure is proposed to objectively select the regularization parameter. Experimental results are carried out to show the striking benefits gained from taking into account the Poisson statistics of the noise. These results also suggest that using sparse-domain regularization may be tractable in many deconvolution applications with Poisson noise such as astronomy and microscopy.
Rodent brain extraction using B-spline based deformable model.

Accurate rodent brain extraction is one of the basic steps for many translational study using Magnetic Resonance Imaging (MRI). In this paper, we present a new approach to model the rodent brain variation using non-rigid B-spline image registration for the brain extraction in MRI images. We model the shape and appearance with the B-spline parameters together with a mean brain image. Followed by a method using multi-expert, we refine the brain extraction region. Compared with the image-based template model using cross-correlation, the performance for rodent brain extraction has shown much improvement on one data set while maintaining the similar yet more consistent performance for another. Both template based methods however outperform the voxel based method (3D PCNN) and a modified BET version for rodent brain extraction.
Determination of sea surface wind speed for a fetch-limited sea using high frequency radar

High frequency surface wave radars have been used to measure ocean surface parameters, among which wind speed is the most challenging to estimate. Existing wind methods are only applicable to fully-developed, unlimited fetch seas. Here, a method is proposed for determining wind speeds for a fetch-limited sea. By differentiating the second-order radar cross section, an expression is derived, relating wind speed and the second-order peak Doppler frequency. The algorithm was tested using simulated data and has produced promising results.
The second-order bistatic high frequency radar scattering cross section of the ocean surface for the case of floating platform

The second-order bistatic high frequency radar cross section of ocean surface is derived for the case of a fixed receiver and a floating transmitter. The second-order radar cross section contains both the hydrodynamic contribution and the electromagnetic contribution. The second-order hydrodynamic contribution is obtained based on the first-order bistatic model with antenna motion. The derivation of the second-order electromagnetic contribution begins with a general expression for the bistatically received second-order electric field for the case of floating antenna. A new bistatic electromagnetic coupling coefficient, which unlike some earlier versions produces no nonphysical singularities in the radar cross section, is derived. The new radar cross section model is simulated and compared with that of monostatic case. The motion-induced second-order peaks appearing in the spectrum are seen to have significantly less energy than those in the first-order case.
On the Development of a High-Frequency Radar Cross Section Model for Mixed Path Ionosphere–Ocean Propagation

An analytic high-frequency (HF) radar cross section model for ionosphere-ocean propagation 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 field 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 the mixed path mode of ionospheric clutter is developed. The ionosphere reflection coefficient used in the analysis is assumed to be a stochastic process with an associated spectral density function to account for phase variations along the surface associated with nonuniformity of the signal path. 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 50-60 dB. Further, it is spread in Doppler, depending on the ionosphere horizontal velocity and reflecting path nonuniformity. Also, this component can be presented from a certain minimum range, depending on ionosphere virtual height, to the maximum radar range.
Multiple sensor multiple object tracking with GMPHD filter

Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density filter for multi-sensor multi-object tracking. We demonstrated the efficient of the approach by applications such as bearing and range tracking, and multiple speaker tracking.
Modeling cutting force of laparoscopic scissors

Modeling forces applied to cut biological material with laparoscopic scissors is important for haptic rendering in laparoscopic surgical simulation. The cutting process is characterized in deformation and fracture. An analytical model for cutting human iliac artery with laparoscopic scissors is derived with concepts of shearing deformation and fracture mechanics in this study. An experimental set-up was built to verify the analytical cutting force model. Three pieces of human iliac artery were cut with laparoscopic scissors. Force required to cut arterial wall and corresponding angular displacement of scissors' handle were collected to evaluate the parameters in the cutting force model. The cutting force model is able to fit the average normalized force-handle angle curve with coefficient of determination equals to 0.977. The starting point of fracture is modeled by energy based fracture mechanics method. The energy based method predicts the fracture point with only 3% deviation from the manual determination.
Scream detection for home applications

Audio signal is an important clue for the situation awareness. It provides complementary information for video signal. For home care, elder care, and security application, screaming is one of the events people (family member, care giver, and security guard) are especially interested in. We present here an approach to scream detection, using both analytic and statistical features for the classification. In audio features, sound energy is a useful feature to detect scream like audio. We adopt the log energy to detect the energy continuity of the audio to represent the screaming which is often lasting longer than many other sounds. Further, a robust high pitch detection based on the autocorrelation is presented to extract the highest pitch for each frame, followed by a pitch analysis for a time window containing multiple frames. Finally, to validate the scream like sound, a SVM based classifier is applied with the feature vector generated from the MFCCs across a window of frames. Experiments of screaming detection is discussed with promising results shown. The algorithm is ported and run in a Linux based set top box connected by a microphone array to capture the audio for live scream detection.
Shadowing-analysis-based wave height measurement from ship-borne X-band nautical radar images

In this paper, a method based on shadowing analysis is applied to estimate significant wave height from ship-borne X-band nautical radar images. Edge detection is first used to extract shadowed areas from the image. The root mean square (RMS) surface slope is then derived by curve fitting illumination ratios and Smith's function. From the RMS surface slope and the mean wave period, the significant wave height is estimated. Modifications including data quality control and subareas selection are made to the analysis. It has been found that the wave height retrieved from the algorithm is underestimated under rain and overestimated under low wind speed. The method produces promising results by comparing radar-derived wave heights with buoy data, and the RMS difference is found be 0.71 m.
Vision Based Hole Crack Detection

Parts quality inspection is important for manufacturing, servicing and repairing. Among them, hole crack detection is very important as hole crack may lead to critical situation. Normal crack detection is unable to be applied to hole crack because accompanying hole patterns would disturb crack detection thus cause severe false alarms. In this paper, we present an approach which is specially developed for hole crack detection on part surface. Firstly, it scans the whole images to localize the candidate hole crack regions with Cascade detectors. Then it extracts the candidate hole cracks with Hessian information. Finally it confirms the hole cracks with morphological operation and shape analysis. Experiments on engineering parts images demonstrate its robustness and efficiency.
GPU-friendly gallbladder modeling for laparoscopic cholecystectomy simulation

A challenge in virtual reality based laparoscopic cholecystectomy simulation is to construct a fast and accurate deformable gallbladder model. This paper proposes a multi-layer mass-spring model which can adapt well to the built-in accelerating algorithms in PhysX-engine of GPU. The gallbladder is first segmented from clinical CT images. A multi-layer model based on the anatomical structure of gallbladder is subsequently constructed. We configure the parameters of the springs based on the biomechanical properties of gallbladder to ensure the realism of the deformation results. Preliminary experiments demonstrate that our model can achieve satisfactory results in terms of both visual perception and time performance.
A Self-Adaptive Wavelet-Based Algorithm for Wave Measurement Using Nautical Radar

In this paper, a self-adaptive 2-D continuous-wavelet-transform-based algorithm for extracting wave information from X-band nautical radar images is presented. After investigating the 2-D continuous wavelet transform and its application for radar image processing, it is found that the wavelet scaling parameters will affect the results of wave field analysis. The relation of the scaling parameters to the minimum distinguishable wavenumber is developed using a calibration factor. Optimal empirical values of such calibration factors are determined from a series of simulation data tests for variable wave conditions. An iterative algorithm is then proposed that enables the system to automatically select the optimal calibration factor without requiring a reference to other instrumentation. The algorithm is evaluated using dual-polarized radar data collected on the east coast of Canada. Results of the proposed algorithm are analyzed and compared with in situ TRIAXYS wave buoy data as well as that obtained from the conventional 3-D fast Fourier transform (FFT)-based method. The impact of signal polarization on the results is explored. The agreement between the buoy and FFT results indicates that the proposed algorithm is practical and effective as an alternative to the classic 3-D FFT-based method for retrieving ocean wave information.
Further analysis of the second-order high frequency radar ocean surface cross section for an antenna on a floating platform

When the transmitting and/or receiving antennas of a high high-frequency (HF) radar are mounted on a floating platform, which is subject to sway, it is known that motion introduces additional features in the Doppler spectra of the signal scattered from the ocean surface. Following techniques in earlier work which examined these features up to second order for scatter from a patch of ocean remote from the antennas, we consider second-order effects arising from a single scatter near the antennas followed or preceded by a scatter on the remote patch. The derivation begins with a general expression for the bistatically received second-order electric field in which platform sway is introduced. This is then reduced to the monostatic case. Having developed the monostatic equations, and assuming the ocean surface to be representable as zero-mean Gaussian process, the corresponding second-order monostatic radar cross section (RCS) is developed. As in the earlier analyses for patch scatter, the new contributions to the RCS appear as Bessel functions, which give rise to extra spectral content not appearing in the fixed-antenna results. However, simulations of the new RCS, including antenna sway under a variety of sea states, suggest that while the new second-order effects are visible in the spectrum, they are generally smaller than first-order effects, except at specific Doppler frequencies.
Data-Driven Probability Hypothesis Density Filter for Visual Tracking

We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time. To meet this challenge, we have modeled the targets into two categories: survival objects and spontaneous birth objects. Based on the model, we have derived the data-driven importance function for a particle PHD filter and applied to pedestrians tracking where people or groups appear, merge, split, and disappear in the field of view of a camera. The experimental results have demonstrated the effectiveness of the particle PHD filter using the proposed importance function in tracking random number of pedestrians and deriving their locations.
Extraction of sea surface wind direction from bistatic high-frequency radar Doppler spectra

A method for extracting sea surface wind direction information from bistatic high frequency (HF) radar Doppler spectra is presented. By analogy to the monostatic case, the ratio of the intensities of the positive and negative bistatic Bragg peaks is used to derive the (ambiguous) wind direction. For bistatic operation, the reference is taken with respect to the scattering ellipse normal rather than the radar beam direction. The method is shown to be valid based on simulated bistatic HF radar Doppler spectra.
Motion compensation for high-frequency surface wave radar on a floating platform

A method for mitigating antenna motion effects in high-frequency radar Doppler spectra developed from ocean backscatter is proposed. On the basis of the established radar cross-section models for a fixed antenna and for an antenna on a floating platform, the relationship between these models is examined. Through this relationship, motion compensation can be achieved by deconvolving the radar cross-section data with the derived transfer function. Four different deconvolution methods are illustrated and discussed in this study. Calculations involving a radar cross-section model, incorporating external noise, for an antenna on a floating platform are conducted in order to simulate field data and to examine this motion compensation method. The external noise is characterised as a white Gaussian zero-mean process. By using this newly developed radar cross-section model with external noise, motion compensation results under different sea states are examined. The outcomes indicate that an iterative Tikhonov regularisation deconvolution technique is superior to the other compensation methods implemented in this study.
The derivation of high frequency radar cross sections for swell-contaminated seas

The first- and second-order monostatic cross sections of swell contaminated seas for high-frequency ground wave pulsed radar operation are derived based on the fundamental electric field equations. The ocean surface, a mixture of both swell and wind wave components, is first represented with a Fourier series. The corresponding equations of the received electric field are then obtained. Fourier transformation of the autocorrelations of the electric field gives the Doppler power spectral density, which ultimately leads to the radar cross sections. Simulated results are also provided and are observed to be highly consistent with real Doppler spectra.
Eye tracking with statistical learning and sequential Monte Carlo sampling

Many ways have been proposed for eye tracking. These methods are either based on detecting low-level image characteristic or pattern recognition techniques. The first approach is fast yet lack of accuracy. The second approach is accurate but slow. This paper presents a novel method proposed for fast and accurate eye-tracking by a combination of the above methods. A Gaussian mixture model for skin color is built to locate the eyes approximately in a fast speed. Then, probabilistic principal component analysis (PPCA) is applied to confirm the eye location accurately. Sequential Monte Carlo sampling, an enhanced sampling technique, is integrated with the system to further enhance the speed. Experimental results show that it can perform eye tracking accurately with fast speed and robust against the different degree of deformation, orientation gaze and shape of eyes.
An investigation of impropriety and noncircularity in high frequency radar data

Discarding the phase content of signals from single-channel high-frequency (HF) radar is commonplace among practitioners in the field. From the perspective of complex-valued statistics, this practice implicitly implies that the complex-valued HF data is second-order proper or circular. This paper presents a preliminary investigation into the validity of this assumption using HF surface wave radar (HF-SWR) field data. It is found that the HF-SWR data is indeed second-order improper or noncircular. This negates the common belief regarding the non-informativness of the phase, and it warrants more in-depth analysis into characterizing impropriety/noncircularity for relevant HF radar applications.
Attentive Behavior Detection by Non-Linear Head Pose Embedding and Mapping

In this paper, we present a new scheme to robustly detect a human attentive behavior, i.e., a frequent change in focus of attention (FCFA) from video sequences. The FCFA behavior can be easily perceived by people as temporal changes of human head pose. Here, we propose a non-linear head pose embedding and mapping algorithm to detect the pose in each frame of the sequence. Developed from ISOMAP, we learn a person-independent and non-linear embedding space (we call it a 2-D feature space) for different head poses. A non-linear interpolation mapping followed by an adaptive local fitting method is designed to map new frames into the 2-D feature space where head poses can be further obtained. An entropy classifier is then proposed on each sequence to detect the FCFA behavior. Experiments reported in this paper showed robust results
Intensity-level-selection-based wind retrieval from shipborne nautical X-band radar data

In this paper, a modified intensity-level-selection-based algorithm for sea surface wind retrieval from shipborne X-band nautical radar images is presented. A data quality control process is introduced to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. Moreover, a harmonic function is used to least-squares fit the selected range vector to antenna look directions to improve the wind direction determination. The wind information obtained from the radar data is compared with shipborne anemometer results. The modified algorithm reduces the mean differences between the radar and the anemometer results for wind direction and speed by about 7.4° and 1.2 m/s, respectively.
Stereo-based human detection for mobile service robots

Without knowledge of background or motion feature, detecting humans from a 2D image is still a tough task. In this paper, a novel stereo-based method to detect human objects for mobile service robots is proposed. Human objects are detected from the stereo spatial space through three distinct steps: (i) human oriented scale-adaptive filtering to aggregate and enhance the evidence of human presence, (ii) human like object segmentation, and (iii) human object identification based on the matching of a deformable head shoulder template to the evidence from both stereo and edge information. Systematic evaluation of the experimental results show that the high accuracy rates for human detection have been achieved with fewer constraints on the human operator, robot and the environment which they are in.