nils-herrmann/clean_scirepeval huggingface dataset:
Colour image segmentation using adaptive mean shift filters

A novel adaptive mean shift filter is proposed for unsupervised color image segmentation. Segmentation is obtained by iteratively estimating local clusters and modifying (filtering) pixels along the steepest ascent towards their nearest clusters using local data from randomly partitioned image, followed by a simple post processing. Several new steps are introduced to avoid pixels drifting towards shaded regions, pixel filtering using outliers and other local mode, and the blur of image structures. In each local area, the number of clusters is set to be adaptive according to the dynamic range of the local histogram. The filter is applied only to a radial set of pixels chosen from the color histogram, instead of all pixels in the local area. Further, the random partitions of the image make it possible for parallel processing of each local area, hence the potential of fast processing. Experiments were performed on color images with different complexity, and good segmentation results were obtained. Some preliminary evaluations were also performed using the uniformity measure.
Human fall detection using segment-level cnn features and sparse dictionary learning

This paper addresses issues in human fall detection from videos. Unlike using handcrafted features in the conventional machine learning, we extract features from Convolutional Neural Networks (CNNs) for human fall detection. Similar to many existing work using two stream inputs, we use a spatial CNN stream with raw image difference and a temporal CNN stream with optical flow as the inputs of CNN. Different from conventional two stream action recognition work, we exploit sparse representation with residual-based pooling on the CNN extracted features, for obtaining more discriminative feature codes. For characterizing the sequential information in video activity, we use the code vector from long-range dynamic feature representation by concatenating codes in segment-levels as the input to a SVM classifier. Experiments have been conducted on two public video databases for fall detection. Comparisons with six existing methods show the effectiveness of the proposed method.
Categorization and analysis of power system transients

Power system transients are power-quality disturbances that can be harmful to electronic equipment. This paper contributes and provides some solutions to the following issues: 1) to introduce a new way to identify different categories of power system transients based on their underlying causes; 2) to propose a model and analysis tool for oscillatory transients, where emphasis is on finding phenomena and characteristics associated with the underlying causes of transients. A model-based approach, ESPRIT, is applied to a number of simulated voltage waveforms to extract the parameters of oscillatory transients, and the results may be used for identifying and understanding the causes of transients by correlating the major components in transients with the phenomena that may appear in different types of transients and some a priori knowledge of power system settings.
Fall detection in RGB-D videos for elderly care

This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement and displacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video dataset for fall detection. Tests and evaluations show the effectiveness of the proposed method.
Determinant of the information matrix: A new rotation invariant optimality metric to design gradient encoding schemes

Minimum condition number (CN) gradient encoding scheme was introduced to diffusion MRI community more than a decade ago. It's computation requires tedious numerical optimization which usually leads to sub-optimal solutions. The CN does not reflect any benefits in acquiring more measurements, i.e. it's optimal value is constant for any number of measurements. Further, it is variable under rotation. In this paper we (i) propose an accurate method to compute minimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new opti-mality metric that scales with number of measurements and does reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotation invariant. Evaluations on state-of-the-art encoding schemes proves the relevance and superiority of the proposed metric compared to condition number.
Exploiting riemannian manifolds for daily activity classification in video towards health care

This paper addresses the problem of classifying activities of daily living in video. The proposed method uses a tree structure of two layers, where in each node of the tree there resides a Riemannian manifold that corresponds to different part-based covariance features. In the first layer, activities are classified according to the dynamics of upper body parts. In the second layer, activities are further classified according to the appearance of local image patches at hands in key frames, where the interacting objects are likely to be attached. The novelties of this paper include: (i) characterizing the motion of upper body parts by a covariance matrix of distances between each pair of key points and the orientations of lines that connect them; (ii) describing human-object interaction by the appearance of local regions around hands in key frames that are selected based on the proximity of hands to other key points; (iii) formulating a pairwise geodesics-based kernel for activity classification on Riemannian manifolds under the log-Euclidean metric. Experiments were conducted on a video dataset containing a total number of 426 video events (activities) from 4 classes. The proposed method is shown to be effective by achieving high classification accuracy (93.79% on average) and small false alarms (1.99% on average) overall, as well as for each individual class.
Classification of power system transients: synchronised switching

This paper presents a new method for the identification and classification of transients due to synchronised capacitor switching in three-phase systems. Signal processing techniques are introduced to detect the switching actions in the individual phases. Synchronised switching methods for capacitor energisation are considered. The proposed method enables detection and classification of synchronised capacitor switching events. This in turn will lead to a reduction in the amount of data to be reported from a power quality survey.
Practical applications of automatic image analysis for overhead lines

This paper proposes novel techniques for automatically extracting information from overhead lines based on automatic image/video processing. Three application scenarios are presented: (a) detecting snow and ice coverage on insulators for overhead lines; (b) detecting swing angles of insulators; (c) quantifying the visibility of insulators at transmission lines with typical backgrounds. Experiments have been conducted on recorded videos/images of insulators (with different prototypes) for overhead lines in a remote test station in Norway. Results show that the proposed automatic image analysis methods are promising for extracting the status (snow/ice coverage, swing angle, visibility) of insulators and might be applied in the future for other components of the overhead line.
Multi-view face pose classification by boosting with weak hypothesis fusion using visual and infrared images

This paper proposes a novel method for multi-view face pose classification through sequential learning and sensor fusion. The basic idea is to use face images observed in visual and thermal infrared (IR) bands, with the same sampling weight in a multi-class boosting structure. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from visual and infrared bands interactively complement each other. This is achieved by learning weak hypothesis for visual and IR band independently and then fusing the optimized hypothesis sub-ensembles. In addition, an effective feature descriptor is introduced to thermal IR images. Experiments are conducted on a visual and thermal IR image dataset containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm.
Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas

We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color histograms. By using a Gaussian distributed Bhattacharyya distance as the likelihood and mean shift updated parameters as the state vector, particle filters become more efficient in terms of tracking using a small number of particles (≪20). The combined scheme is able to maintain the merits of both methods. Experiments conducted on videos containing deformable objects with long-term partial occlusions and intersections have shown robust tracking performance. Comparisons with two existing methods have been made which showed marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drift.
Domain-shift tracking: Online learning and visual object tracking on smooth manifolds

This paper describes a novel domain-shift tracking scheme that includes Bayesian formulation on the Grass-mann/Riemannian manifold for tracking, and domain-shift online object learning as well as occlusion handling on the manifold. Since out-of-plane object images do not lie in a single vector space, smoothing manifolds are more suitable tools for describing domain-shift nature of such dynamic object images. The proposed domain-shift scheme is designed for tracking large-size dynamic objects (i.e. camera is close to the object) in video that contain significant out-of-plane pose changes, and may be accompanied with long-term partial occlusions. The main features of such domain-shift tracker include: (a) Bayesian formulation defined on a manifold instead of vector space, performing posterior state estimation on the manifold based on nonlinear state space modeling; (b) Two particle filters defined on the manifold, one for online learning, another for tracking; (c) Occlusion handling is added to the online learning process to prevent learning occluding objects/clutter. To show the variant of domain-shift trackers, two example schemes are described: one uses instantaneous data on Riemannian manifolds, another uses a sliding-window of data on Grassmann manifolds. Tests on videos from the proposed domain-shift trackers have shown very robust tracking performance when large-size objects contain significant out-of-plane pose changes accompanied with long-term partial occlusions. Comparisons with three existing state-of-the-art methods provide further support to the proposed scheme.
Automatic traffic sign recognition based on saliency-enhanced features and SVMs from incrementally built dataset

This paper proposes an automatic traffic sign recognition method based on saliency-enhanced feature and SVMs. As when human observe a traffic sign, a two-stage procedure is performed by first locating the region of sign according to its unique shape and color, and second paying attention to content inside the sign. The proposed saliency feature extraction attempts to resemble these two processing stages. We model the first stage via extracting salient regions of signs from detected bounding boxes contributed by sign detector. Salient region extraction is formed as an energy propagation process on local structured graph. The second stage is modeled by exploiting a non-linear color mapping under the guidance of the output of the first stage. As results, salient signature inside a sign is popped up and can be directly used by subsequent SVMs for classification. The proposed method is validated on Chinese traffic sign dataset that is incrementally built.
On the Analysis of Voltage and Current Transients in Three-Phase Power Systems

This paper proposes a method for analyzing measurements of voltage transients in three-phase systems. The method is based on the Clarke transform introduced in 1950 for calculations of travelling waves along three-phase transmission lines. The proposed method also shows close similarities with the classification of three-phase unbalanced voltage dips. After extracting the actual transient (e.g., by using a notch filter centered on the power-system frequency), the three signals are decomposed into seven components. From the relation between these seven components, the dominant component is identified. The method is successfully applied to a number of measured transients. The paper also identifies the limitations of the method and gives suggestions for future work
Geodesic saliency propagation for image salient region detection

This paper proposes a novel geodesic saliency propagation method where detected salient objects may be isolated from both the background and other clutter by adding global considerations in the detection process. The method transmits saliency energy from a coarse saliency map to all image parts rather than from image boundaries in conventional cases. The coarse saliency map is computed using the combination of global contrast and Harris convex hull. Superpixels from pre-segmented image are used as pre-processing to further enhance the efficiency. The proposed propagation is geodesic distance assisted and retains the local connectivity of objects. It is capable of rendering a uniform saliency map while suppressing the background, leading to salient objects being popped out. Experiments were conducted on a benchmark dataset, visual comparisons and performance evaluations with 9 existing methods have shown that the proposed method is robust and achieves the state-of-the-art performance.
Analysis of power disturbances from monitoring multiple levels and locations in a power system

This paper proposes a new methodology for diagnosing the original source and underlying causes of power system disturbances, where voltage and current recordings from different locations of a power system are collected. In the proposed method, disturbances are first pre-classified based on the number of transition segments. The spatial zone of the source of disturbances is coarsely determined from voltage recordings only. Disturbances are then further analyzed and characterized by extracting information from both voltages and currents. Finally more accurate information about the location of the source of disturbances is obtained by different techniques depending on the type of disturbances. Several underlying causes are analyzed and classified by using the proposed features extracted from both voltage and current waveforms. Finally, the location of the source of disturbances is refined once the underlying causes are found. Case studies were performed on a large grid-connected wind farm with disturbances from several underlying causes, including: fault, unit tripping, transformer, capacitor, and cable energizing generated by PSCAD/EMTDC.
Error-resilient packet video coding using harmonic frame-expansions and temporal prediction

This paper proposes a novel error-resilient packet video codec through the use of harmonic frame-expansions. It provides robustness to packet video in erasure channels in an integrated framework without requiring inter/intra coding modes. In the proposed method, spatial redundancy is added by applying a frame-expansion to DCT coefficients while temporal redundancy is added by filtering the motion compensated reference blocks. The frame expansion provides excellent error-resilience at low frequencies, while filtering of motion-reference blocks prevents the propagation of errors at high frequencies. Mathematical expressions of the PSNR for packet video streams with arbitrary losses are derived using piecewise linear approximations. Preliminary simulation results have shown that the proposed codec has generated high quality video at low bit-rates with significant packet losses, and has much less visual artifact than the conventional codec's with similar PSNRs.
Estimating Interharmonics by Using Sliding-Window ESPRIT

A method is proposed for estimating inter-harmonic frequencies in power system voltage and current signals. The method is based on a spectrum-estimation method known as ldquoestimation of signal parameters via rotational invariance techniquesrdquo (ESPRIT). To allow for a more reliable spectral estimation and to cover nonstationarity in the signal, a sliding-window version of ESPRIT is introduced. This paper describes the basic ESPRIT method as well as sliding-window ESPRIT. The paper discusses the application of the method to one synthetic signal and three measurement signals. It is shown that the method allows for very accurate frequency estimation of interharmonic components. The limitations of the methods, such as line splitting and spurious components, can be overcome by using the coherent information obtained from the sliding-window method. A number of remaining issues are also discussed in this paper.
A statistical-based sequential method for fast online detection of fault-induced voltage dips

This paper addresses the problem of detecting voltage dips regarding measurements consisting of fault events, transformer saturation events, and capacitor-switching events. A novel statistical-based sequential detection method is proposed for online classification of these events. The detector is based on the Neyman-Pearson criterion that maximizes the detection rate of fault-induced dips with constrained false alarm rate of the other two types of event. The sequential detector is able to give an earliest possible event discrimination together with the estimated confidence at the time instant ranging from 1/8,1/4,1/2, to 3/4 cycle of the fundamental frequency after detecting an initial voltage drop at 0.95 p.u. The performance of the proposed scheme is evaluated using measurements from medium voltage networks.
Positive and negative sequence estimation for unbalanced voltage dips

This paper proposes the use of a complex Kalman filter for the estimation of positive and negative sequences from three phase voltages. A complex voltage is obtained by applying the /spl alpha//spl beta/-transform followed by the dq-transform using a rotational operator. The algorithm for three phase voltages containing K harmonics is also given. In the conventional method, estimation of positive and negative sequences is performed through two steps: the magnitude and phase-angle in each individual phase of the voltages are first estimated and the symmetrical component transformation is then applied. The proposed method offers a direct estimation of the positive and negative sequences that may reduce the estimation errors. In addition, the proposed method has a reduced computational cost since the number of state variables is reduced to 2/3 as compared to that in the conventional method. An experiment was performed on measured three phase voltage data. Results have shown that the proposed method offers a good estimation.
On high order tensor-based diffusivity profile estimation

Diffusion weighted magnetic resonance imaging (dMRI) is used to measure, in vivo, the self-diffusion of water molecules in biological tissues. High order tensors (HOTs) are used to model the apparent diffusion coefficient (ADC) profile at each voxel from the dMRI data. In this paper we propose: (i) A new method for estimating HOTs from dMRI data based on weighted least squares (WLS) optimization; and (ii) A new expression for computing the fractional anisotropy from a HOT that does not suffer from singularities and spurious zeros. We also present an empirical evaluation of the proposed method relative to the two existing methods based on both synthetic and real human brain dMRI data. The results show that the proposed method yields more accurate estimation than the competing methods.
Geodesic distance transform-based salient region segmentation for automatic traffic sign recognition

Visual-based traffic sign recognition (TSR) requires first detecting and then classifying signs from captured images. In such a cascade system, classification accuracy is often affected by the detection results. This paper proposes a method for extracting a salient region of traffic sign within a detection window for more accurate sign representation and feature extraction, hence enhancing the performance of classification. In the proposed method, a superpixel-based distance map is firstly generated by applying a signed geodesic distance transform from a set of selected foreground and background seeds. An effective method for obtaining a final segmentation from the distance map is then proposed by incorporating the shape constraints of signs. Using these two steps, our method is able to automatically extract salient sign regions of different shapes. The proposed method is tested and validated in a complete TSR system. Test results show that the proposed method has led to a high classification accuracy (97.11%) on a large dataset containing street images. Comparing to the same TSR system without using saliency-segmented regions, the proposed method has yielded a marked performance improvement (about 12.84%). Future work will be on extending to more traffic sign categories and comparing with other benchmark methods.
Region-Based Statistical Background Modeling for Foreground Object Segmentation

This paper proposes a novel region-based scheme for dynamically modeling time-evolving statistics of video background, leading to an effective segmentation of foreground moving objects for a video surveillance system. In (L. Li et al., 2004) statistical-based video surveillance systems employ a Bayes decision rule for classifying foreground and background changes in individual pixels. Although principal feature representations significantly reduce the size of tables of statistics, pixel-wise maintenance remains a challenge due to the computations and memory requirement. The proposed region-based scheme, which is an extension of the above method, replaces pixel-based statistics by region-based statistics through introducing dynamic background region (or pixel) merging and splitting. Simulations have been performed to several outdoor and indoor image sequences, and results have shown a significant reduction of memory requirements for tables of statistics while maintaining relatively good quality in foreground segmented video objects.