nils-herrmann/clean_scirepeval huggingface dataset:
Teleoperation over a shared network: When does it work?

The development of telehaptic communication protocols has gained widespread focus over the past decade. Several protocols have been proposed for carrying out telehaptic activities over a shared network. However, a comprehensive analysis of the impact of the network crosstraffic on the telehaptic stream, and the feasibility of Quality of Service (QoS) compliance, is lacking in the literature. In this paper, we explore the interplay between the telehaptic stream produced by two classes of telehaptic protocols and the cross-traffic that is commonly present in a shared network. Based on this, we formulate general conditions for QoS compliance of the telehaptic stream. These conditions provide guidelines for the design of telehaptic protocols, as well as for the configuration of shared networks for ensuring QoS-compliant telehaptic communication.
Stabilisation of Active Contours Using Tangential Evolution: An Application to Tracking

Active contours are very widely used in computer vision problems. Their usage has a typical problem, that of bunching together of curve points. This becomes apparent especially when we use active contours for tracking, leading to instability in curve evolution. In this paper, we propose an additional tangential term to stabilise the evolution while at the same time ensure that the curve shape is not changed. The proposed method is simple and the computational overhead is minimal, while the results are good
Flex Seal Stiffness Estimation in Flex Nozzle Control System Using Extended Kalman Filter

This paper presents the application of extended Kalman filter (EKF) for detection of flex seal stiffness fault in electro hydraulic actuator (EHA) driven flex nozzle control (FNC) system. The extended Kalman filter is applied to the EHA to estimate the states as well as parameter (flex seal stiffness).It should be highlighted that the servo valve current is used instead of the desired piston position as the input to the EKF in order to simplify the state estimation problem which would have otherwise involved a fifth order model. The EKF-based estimator includes complete nonlinear models of hydraulic functions. It is shown that, firstly, under normal (no fault) operating condition, the developed estimator closely predicts the states of the system, using only a few basic measurements. Secondly, in the presence of seal stiffness fault, the level of residual errors between the estimated and the actual seal stiffness, increase significantly indicating the occurrence of fault. The results demonstrate the efficacy of the proposed EKF-based fault detection scheme to promptly and reliably respond to flex nozzle control system's seal stiffness fault.
A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method
High dynamic range imaging under noisy observations

We propose a radiance domain denoising frame work for the high dynamic range (HDR) imaging problem. The proposed method uses a maximum aposteriori probability (MAP) based reconstruction of the HDR image with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided. Our method yields better results, with the edges well preserved and noise reduced considerably.
Low Loss Transmission in Negative Curvature Optical Fibers With Elliptical Capillary Tubes

Regular and nested negative curvature optical fibers with elliptical capillary tubes have been proposed in this paper for ultralow loss guidance at the telecommunication wavelength. Finite element modeling shows that these fibers can have lower loss compared to their counterparts with circular tubes. These hollow core fibers with low loss and large bandwidth can have huge potential applications in data transmission, sensing, high power delivery, and serve as an ideal platform for the study of a wide variety of nonlinear optical effects.
Multidimensional Probability Density Function Matching for Preprocessing of Multitemporal Remote Sensing Images

This paper addresses the problem of matching the statistical properties of the distributions of two (or more) multi-spectral remote sensing images acquired on the same geographical area at different times. An N-D probability density function (pdf) matching technique for the preprocessing of multitemporal images is introduced in the remote sensing domain by defining and analyzing three important application scenarios: 1) supervised classification; 2) partially supervised classification; and 3) change detection. Unlike other methods adopted in remote sensing applications, the procedure considered performs the matching process by properly taking into account the correlation among spectral channels, thus retaining the data correlation structure after the pdf matching. Experimental results obtained on real multitemporal remote sensing data sets confirm the validity of the presented technique in all the considered scenarios.
A feature-based approach to monitor motor-operated valves used in nuclear power plants

Degradation and failure of motor-operated valves (MOV) compromise operational readiness of the safety related systems of a nuclear power plant. Motor current signature analysis has been found to be a selective and early indicator of a number of mechanical and electrical failures/abnormalities related to the MOV. We present an unsupervised and fully automated method for the extraction of the motor current signature and an analysis to diagnose possible failures in MOVs. The reference line frequency is obtained by sampling the line voltage, and is used to demodulate the current waveform. Having obtained the current signature, a set of features are extracted from the signature. A discriminant analysis is performed on these primitives to detect and classify various types of failures. The proposed method is nonintrusive, computationally efficient and yields good results. It can be easily installed as a part of an expert system for preventive maintenance of MOVs in nuclear power plants.
Simultaneous estimation of super-resolved intensity and depth maps from low resolution defocused observations of a scene

This paper presents a novel technique to simultaneously estimate the depth map and the focused image of a scene, both at a super-resolution, from its defocused observations. Given a sequence of low resolution, blurred and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true focused, super-resolved image. Both the depth and the intensity maps are modeled as separate Markov random fields (MRF) and a maximum a posteriori estimation method is used to recover the high resolution fields. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution and structure recovery techniques, we do away with the correspondence problem.
Can we improve over weber sampling of haptic signals?

In applications such as telesurgery, it is required to transmit haptic signals to a remote location with a delay of at most few milliseconds. To reduce the packet rate and yet retain perceptual quality, adaptive sampling has been explored in the literature. In particular, in earlier work we proposed and analyzed an adaptive sampling scheme based on Weber's law of perception. In this paper, we explore other possible adaptive sampling candidates. We describe an experimental setup where users are subjected to piecewise constant haptic stimuli to which they can respond with a click. We record the clicks and ask the question: can we identify signal features and classiers to predict the clicks? The answer suggests adaptive sampling schemes that improve over Weber sampling.
Distraction Free Evolution of Active Contours

We propose a novel energy term to make the curve evolution quite robust to spurious edges. The physical intuition behind the formulation is that an object edge is generally continuous, but it could be composed of weak and strong segments. We then formulate the energy term which depends on a second order measure defined on the contour. Minimisation of this energy term  yields a space varying curvature based  curve evolution equation. An added advantage of the formulation is that this term also acts as the regularising term for smoothing the curve evolution. The proposed term can therefore be used in conjunction with any of the numerous gradient based active contour models. For our experimentation purpose, we have used the well-known gradient vector  force model as the external force. We have performed a number of experiments on images and obtained good results.
Image Restoration using Geometrically Stabilized Reverse Heat Equation

Blind restoration of blurred images is a classical ill-posed problem. There has been considerable interest in the use of partial differential equations to solve this problem. The blurring of an image has traditionally been modeled by Witkin  and Koenderink  by the heat equation. This has been the basis of the Gaussian scale space. However, a similar theoretical formulation has not been possible for deblurring of images due to the ill-posed nature of the reverse heat equation. Here we consider the stabilization of the reverse heat equation. We do this by damping the distortion along the edges by adding a normal component of the heat equation in the forward direction. We use a stopping criterion based on the divergence of the curvature in the resulting reverse heat flow. The resulting stabilized reverse heat flow makes it possible to solve the challenging problem of blind space varying deconvolution. The method is justified by a varied set of experimental results.
Impact Analysis of Body Movement in Ambulatory ECG

Ambulatory ECG analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts facilitates better ECG analysis. In , an unsupervised method using recursive principal component analysis (RPCA) was used to detect transitions between body movements. In this paper, we endeavour to quantify the impact of various types of body movements on the extent of ECG motion artifact using the RPCA error signal. For this purpose, acceleration data from different body parts i.e. arm(s), leg and waist, have been obtained using commercially available motion sensors, in conjunction with ECG signal, while carrying out routine body movement activities like climbing stairs, walking, twisting, and arm movements, at three different intensity levels: slow, medium and fast. The acceleration magnitudes and the RPCA error sequence are found to be well correlated, thus validating the body movement impact analysis, and also indicating the suitability of the method for quantification of body movement kinematics from the ECG signal itself in the absence of any accelerometer sensors.
A Technique for Simultaneous Visualization and Segmentation of Hyperspectral Data

In this paper, we propose an optimization-based method for simultaneous fusion and unsupervised segmentation of hyperspectral remote sensing images by exploiting redundancy in the data. The hyperspectral data set is visualized as a single image obtained by weighted addition of all spectral points at each pixel location in the data set. The weights are optimized to improve those statistical characteristics of the fused image, which invoke an enhanced response from a human observer. A piecewise-constant smoothness constraint is imposed on the weights instead of the fused image by minimization of its 3-D total-variation norm, thus preventing the fused image from blurring. The optimal recovery of the weight matrix additionally provides useful information in segmenting the hyperspectral data set spatially. We provide ample experimental results to substantiate the usefulness of the proposed method.
Performance analysis of total least squares methods in three-dimensional motion estimation

An algorithm is presented to obtain the total least squares (TLS) estimates of the motion parameters of an object from range/stereo data or perspective views in a closed form. TLS estimates are suitable when data in both time frames are corrupted by noise, which is an appropriate model for motion analysis in practice. The robustness of different linear least squares methods is analyzed for the estimation of motion parameters against the sensor noise and possible mismatches in establishing object feature point correspondence. As the errors in point correspondence increase, the performance of an ordinary least squares (LS) estimator was found to deteriorate much faster than that of the TLS estimator. The Cramer-Rao lower bound (CRLB) of the error covariance matrix was derived for the TLS model under the assumption of uncorrelated additive Gaussian noise. The CRLB for the TLS model is shown to be always higher than that for the LS model.
Salient object carving

In this paper, we propose an unsupervised two-stage algorithm to extract salient objects from images. In the first stage, the image is segmented into superpixels that are grouped together through k-means clustering, based on histogram features of superpixels. The saliency of each cluster is calculated using inter-cluster and intra-cluster feature dissimilarities. In the second stage, we use seam carving to obtain an object level segmentation of the image in the form of a bounding box around the salient object. We propose an automated approach for seam carving based on a novel energy function obtained by combining the saliency output with a texture removed input image. We compute the optimal number of seams to be removed to extract the salient object instead of manually providing it. The performance of the proposed method is demonstrated by processing different types of images.
Fast compensation of illumination changes for background subtraction

Background subtraction is a common technique used for motion tracking. It involves segmenting the foreground from the background in a given set of video frames. Background subtraction as proposed by Stauffer-Grimson  models each pixel using a mixture of Gaussians. The parameters of the Gaussian model are adaptive, and can adjust to gradual changes in image intensity over time. But in cases when the lighting change in the captured video sequence is sudden, like when the camera's automatic gain control (AGC) self adjusts the intensity of the overall image, such model based methods of background subtraction fail to adapt quickly to such a sudden change in image intensity. We propose a technique to automatically estimate the extent of camera AGC, and then use the information to add an additional block into any model based method such as the Stauffer-Grimson method to compensate for camera AGC while doing background subtraction.
Simultaneous Estimation of Super-Resolved Scene and Depth Map from Low Resolution Defocused Observations

This paper presents a novel technique to simultaneously estimate the depth map and the focused image of a scene, both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high spatial resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted mostly to the intensity domain. In this paper, we extend the scope of super-resolution imaging to acquire depth estimates at high spatial resolution simultaneously. Given a sequence of low resolution, blurred, and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true high resolution focused image. Both the depth and the image are modeled as separate Markov random fields (MRF) and a maximum a posteriori estimation method is used to recover the high resolution fields. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution and structure recovery techniques, we do away with the correspondence problem.
Recursive estimation of illuminant motion from flow field

We consider the problem of estimating the motion of a light source, given a sequence of images of a stationary, unknown object illuminated by it. The apparent flow field induced on the image plane is used to recover the motion parameters. The motion is assumed to be uniform between the successive image frames. A recursive total least squares estimator is developed to track the motion of the illuminant. We present typical simulation results to validate the proposed scheme.
Robust estimation of multi-component motion in image sequences using the epipolar constraint

Given two frames of a dynamic scene with several rigid body objects undergoing different motions in the three-dimensional space, we robustly estimate the motion and structure of each object. The least median of squares (LMedS) estimator is integrated into a robust 3D motion parameter estimation and scene structure recovery framework to deal with the multi-motion problem. Experimental results underline the capability of the approach to deal successfully with multi-component motion. We apply the approach presented in this paper to the problem of automatic insertion of artificial objects in real image sequences.
HoIP: A point-to-point haptic data communication protocol and its evaluation

Telehaptics applications are usually characterized by a strict imposition of a round trip haptic data latency of less than 30 ms. In this paper, we present Haptics over Internet Protocol (HoIP) - a low latency application layer protocol that enables haptic, audio and video data transmission over a network between two remotely connected nodes. The evaluation of the protocol is carried out through a set of three experiments, each with distinct objectives. First, a haptic-audio-visual (HAV) interactive application, involving two remotely located human personnel communicating via haptic, auditory and visual media, to evaluate the Quality of Service (QoS) violation due to the protocol. Second, a haptic sawing experiment with the goal of assessing the impact of HoIP and network delays in telehaptics applications, by taking the example of a typical telesurgical activity. Third, a telepottery system to determine the protocol's ability in reproducing a real-time interactive user experience with a remote virtual object, in presence of perceptual data compression and reconstruction techniques. Our experiments reveal that the transmission scheduling of multimedia packets performs well in terms of maintaining the latencies well under the QoS thresholds.
Linear cyclic pursuit based prediction of personal space violation in surveillance video

Analysis of human interaction in a social gathering is of high interest in security and surveillance applications. It is also of psychological interest to study the interaction to get a better understanding of the participant behavior. This paper is an attempt to explore and analyze interactions among the individuals from a single calibrated camera. We are particularly interested in trajectory prediction. These predicted trajectories of individuals are then used in predicting personal space violation. Each individual, represented by a feature point in a 2.5D coordinate system, is tracked using Lucas-Kanade tracking algorithm. We use the linear cyclic pursuit framework to model this point motion. This model is used for short-term prediction of individual trajectory. We demonstrate these ideas on different types of datasets.
A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy

This letter addresses the problem of unsupervised land-cover classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.
Content Based Image Retrieval in Presence of Foreground Disturbances

The image retrieval problem in the presence of possible foreground disturbances is considered. The foreground may be irrelevant for the retrieval purposes but it occludes the background and hence reduces the retrieval accuracy. The accrued inaccuracy is quantified in terms of cardinality of the occluding region. The use of the sprite generated from a video clip is proposed as a query, so that the effect of moving foreground can be eliminated. The segmented foreground region is filled in by constructing the background sprite to increase the retrieval accuracy. The performance of a retrieval scheme under foreground disturbance is presented here.