nils-herrmann/clean_scirepeval huggingface dataset:
Discharge characteristics of one-dimensional plasma jet array at atmospheric pressure

Atmospheric pressure low-temperature plasma jet has become one of the hot research issues in plasma science and technology, and a plasma jet array is a tendency for plasma jet application. In this paper, atmospheric pressure low-temperature plasma jet array is generated by a one-dimensional device of two quartz tubes with a single ring electrode configuration, and the feed gas is helium. The discharge excitation power is a repetitive nanosecond-pulse generator. The main discharge parameters such as applied voltage, total discharge current, emission spectra were measured. Results show that nanosecond pulses can produce a stable one-dimensional plasma jet array. The produced two plasma jets fired in the same initiate voltage and show the well uniformity. When the quartz tube placed extremely closely, with the increase of gas flow, the visual length of one tube remain unchanged, which has much weaker intensity than another. Because the asymmetric electric field distribution, a great quantity of charges shift to one tube, forming the coupling phenomenon. Experimental results show that with the distance increase between two tubes, both the jet length and intensity are decreased.
A new approach for multiphase piecewise smooth image segmentation

Image segmentation is to separate the image domain into sub-domains according to some optimization rule. Variational level-set image segmentation based on the Mumford-Shah model, together with level set formulation, leads to complicate implementation and expensive computational time. To overcome the difficulty in solving the complex coupled level set functions and handle any number of phases in the image, we improve the region-base active contour model in this paper. We formulate the image segmentation as a new multiphase minimization problem to handle images containing any integer phases of intensities. We also propose efficient numerical algorithms to solve such optimization problem without extra steps of convex relaxation. Numerical validation experiments on various images show better accuracy and robustness over existing methods.
A review of the continuing evolution of coastal ocean radar remote sensing in the Eastern Canadian context

Research in ocean remote sensing using radar has been ongoing at Memorial University, Newfoundland since about 1980. Using rough surface scattering models derived in-house for high frequency surface wave radars (HFSWR), inversion techniques were developed to extract various ocean surface parameters from the radar sea echo. Within the last few years this research has been expanded to include ocean parameter estimation using nautical radar. This paper provides a summary of the initiatives that have been undertaken over the last dozen years as well as those being addressed currently.
A High-Frequency Surface Wave Radar Ionospheric Clutter Model for Mixed-Path Propagation With the Second-Order Sea Scattering

A theoretical second-order monostatic high-frequency radar power model is derived for the case of mixed-path ionosphere-ocean propagation. First, the general form of the received electric field is investigated by considering the ionospheric reflection and the second-order sea scattering, which involves both electromagnetic and hydrodynamic contributions. Then, this field is inversely Fourier transformed to the time domain, and a pulsed source is incorporated. Subsequently, the second-order received power model is developed by assuming that the ionospheric reflection coefficient and the ocean surface can be described as Fourier series whose coefficients are random variables. In order to investigate the power spectrum of the mixed-path propagation and its relative intensity to that of the surface propagation under a variety of ionospheric conditions and sea states, a normalized power is simulated.
Kernel-based method for tracking objects with rotation and translation

This paper addresses the issue of tracking translation and rotation simultaneously. Starting with a kernel-based spatial-spectral model for object representation, we define an l/sub 2/-norm similarity measure between the target object and the observation, and derive a new formulation to the tracking of translational and rotational object. Based on the tracking formulation, an iterative procedure is proposed. We also develop an adaptive kernel model to cope with varying appearance. Experimental results are presented for both synthetic data and real-world traffic video.
Gaussian mixture probability hypothesis density for visual people racking

This paper presents our work which involves the application of a recursive Bayesian filter, the Gaussian mixture probability hypothesis density (GMPHD) filter, to a visual tracking problem. Foreground objects are detected using statistical background modeling to obtain measurements which are input into the filter. The GMPHD filter explicitly models the birth, survival and death of objects by managing the number of Gaussian components and jointly estimates the time-varying number of objects and their states. A scene-driven method is proposed to initialize the GMPHD filter and model the birth of new objects. The results shows when a person or a group appeared, merged, split, and disappeared in the field of view, the GMPHD filter can track the number and positions at the most time. The scene-driven GMPHD filter can track the birth of new objects faster than the particle PHD filter.
Extraction of swell parameters from simulated noisy HF radar signals

Based on the previously developed monostatic HF radar cross section model for swell contaminated seas, a new inversion algorithm for swell information from radar sea echoes is proposed. The received electric field time series with external white Gaussian noise is first simulated, from which the Doppler spectrum is obtained as a periodogram. Next, all swell peaks in the spectrum are identified and processed with a robust peak recognition routine, and the positions of those peaks are used to calculate the period and dominant direction of swell. Finally, the half-power width of each swell peak is extracted in order to derive the frequency spreading of the unknown swell wave height spectrum. Simulation results show that the method proposed in this paper provides precise estimates of the swell parameters under different sea states, and improves the generality of traditional inversion algorithms.
Planning of hepatectomy for tumor resection: Resection surface construction and optimization

An interactive liver surgery planning system has been developed to construct and optimize the resection plan. With this system, the segmentation results of the liver and its components (such as tumors and vessels) are comprehensively visualized for surgeons to have an intuitive understanding of the internal anatomical structure of the liver. This system will also allow surgeons to interactively create and modify a resection plan on the virtual liver model. The resection surface, whose boundary is a closed curve, will be automatically constructed with the safe resection margins of tumors. Different from other systems, our developed system is able to generate the safety margins to all tumors. During surgery, a larger resection surface may cause potentially more bleeding and other complications. Therefore, area minimization is applied during the resection surface construction by adopting the minimal area mesh, which is a smooth surface with minimal area. After these virtual modifications, the resultant resection surface indicates the route to cut the liver for tumor removal. The volumes for both resected liver and residual liver are calculated for clinical decision making.
Analysis for Irregularities of Listing Companies Based on Evolutionary Game Theory

This text analysis for the selection of strategies among listing companies by the evolutionary game theory, decide the stability of the balance of evolutionary game according to the difference of various parameter value, and then analyze the reason for selection of incompliance or compliance by listing companies. Following that, the text will make analysis for the selection of strategies among listing companies and supervisory authorities by the evolutionary game theory, decide the stability of the game according to the parameter value difference between the supervisory party and the supervised party, and then analyze the situation of strategy selection by listing companies and supervisory party based on the analysis of stability of the game. At last, the text will give policy suggestions for how to prevent the irregularities of listing companies from the angle of decision-making mechanism based on the conclusions of the analysis of evolutionary game theory.
Fast stitching of videos captured from freely moving devices by exploiting temporal redundancy

We investigate the problem of efficient panoramic video construction based on time-synchronized input video streams. No additional constraints are imposed regarding the motion of the capturing video cameras. The presented work is, to the best of our knowledge, the first attempt to construct in real-time a panoramic video stream from input video streams captured by freely moving cameras. The main contribution is in proposing an efficient panoramic video construction algorithm that exploits temporal information to avoid solving the stitching problem fully on a frame by frame basis. We provide detailed experimental evaluation of different methodologies that employ previous frames stitching results such as tracking interest points using optical flow and using areas of overlap to limit the search space for interest points. Our results clearly indicate that making use of temporal information in video stitching can achieve a significant reduction in execution time while providing a comparable effectiveness.
Higher order potentials with superpixel neighbourhood (HSN) for semantic image segmentation

Among the approaches for solving the semantic image segmentation problem that has proven successful is in formulating an energy minimization expressed on top of a conditional random field (CRF) over image pixels. Recently, high order potentials (cliques of size greater than 2) over superpixels have been incorporated in the CRF energy function yielding promising results. These potentials encourage pixels within the same superpixel to take the same label by penalizing inconsistent labeling within the superpixel. While some of the earlier attempts modeled higher order potentials without considering the conditional dependencies between superpixels, others modeled these dependencies at the cost of oversimplified models at higher levels. In this paper, we propose incorporating superpixel neighborhood information within the high order potential, hence modeling dependencies between superpixels without the need of oversimplifying or constraining the model. Results show that the proposed method achieves state-of-the-art results on the challenging PASCAL VOC 2007 dataset.
Active feedback for enhancing the construction of panoramic live mobile video streams

Constructing a panoramic video out of multiple incoming live mobile video streams is a challenging problem. This problem involves multiple users live streaming the same scene from different angles, using their mobile phones, with the objective of constructing a panoramic video of the scene. The main challenge in this problem is the lack of coordination between the streaming users, resulting in too much, too little, or no overlap between incoming streams. To add to the challenge, the streaming users are generally free to move, which means that the amounts of overlap between the different streams are dynamically changing. In this paper, we propose a method for automatically coordinating between the streaming users, such that the quality of the resulting panoramic video is enhanced. The method works by analyzing the incoming video streams, and automatically providing active feedback to the streaming users. We investigate different methods for generating the active feedback and presenting it to the streaming users resulting in an improved panoramic video output compared to the case where no feedback is utilized.
Graph-based superpixel labeling for enhancement of online video segmentation

In this paper, we propose a novel approach for video segmentation. The proposed work is based on exploiting a superpixel-based image segmentation approach to improve the performance of state-of-the-art foreground/background segmentation techniques. A fusion between a bilayer segmentation and a geodesic segmentation approaches with a graph-based superpixel segmentation method is performed. Four different combination alternatives are investigated in terms of performance and efficiency. Manually-labeled ground truth video sequences as well as our own recorded video sequences were used for evaluation purposes. The evaluation results confirm the potential of the proposed method in enhancing the accuracy of the video segmentation over the state-of-the-art.
Employing 3D Accelerometer Information for Fast and Reliable Image Features Matching on Mobile Devices

Image matching is a cornerstone technology in many image understanding, augmented reality and recognition applications. The state-of-the-art techniques follow a feature-based approach by extracting interest points and describing them by either rotation or affine invariant descriptors. However, requiring rotation or affine invariance comes at an additional computational cost as well as inaccurate estimates in some cases such as out-of-plane rotations. Fortunately, today most mobile devices incorporate 3-D accelerometers that measure the acceleration values along the three axes. In this paper, we propose to employ the acceleration values to calculate the in-plane and tilting rotation angles of the capturing device, in order to alleviate the need for constructing rotationally invariant descriptors. We describe an approach for incorporating the calculated rotation angles in the process of interest point extraction and description. Furthermore, we evaluate empirically the proposed approach, both in terms of computational time and accuracy on standard datasets as well as a dataset collected using a mobile phone. Our results show that the proposed approach provides savings in computational time while providing accuracy gains.
Facial expression recognition in the wild using rich deep features

Facial Expression Recognition is an active area of research in computer vision with a wide range of applications. Several approaches have been developed to solve this problem for different benchmark datasets. However, Facial Expression Recognition in the wild remains an area where much work is still needed to serve real-world applications. To this end, in this paper we present a novel approach towards facial expression recognition. We fuse rich deep features with domain knowledge through encoding discriminant facial patches. We conduct experiments on two of the most popular benchmark datasets; CK and TFE. Moreover, we present a novel dataset that, unlike its precedents, consists of natural - not acted - expression images. Experimental results show that our approach achieves state-of-the-art results over standard benchmarks and our own dataset.
Multi-view human action recognition system employing 2DPCA

A novel algorithm for view-invariant human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) applied directly on the Motion Energy Image (MEI) or the Motion History Image (MHI) in both the spatial domain and the transform domain. This method reduces the computational complexity by a factor of at least 66, achieving the highest recognition accuracy per camera, while maintaining minimum storage requirements, compared with the most recent reports in the field. Experimental results performed on the Weizmann action and the INIRIA IXMAS datasets confirm the excellent properties of the proposed algorithm, showing its robustness and ability to work with small number of training sequences. The dramatic reduction in computational complexity promotes the use in real time applications.
Object matching using feature aggregation over a frame sequence

Object instance matching is a cornerstone component in many computer vision applications such as image search, augmented reality and unsupervised tagging. The common flow in these applications is to take an input image and match it against a database of previously enrolled images of objects of interest. This is usually difficult as one needs to capture an image corresponding to an object view already present in the database, especially in the case of 3D objects with high curvature where light reflection, viewpoint change and partial occlusion can significantly alter the appearance of the captured image. Rather than relying on having numerous views of each object in the database, we propose an alternative method of capturing a short video sequence scanning a certain object and utilize information from multiple frames to improve the chance of a successful match in the database. The matching step combines local features from a number of frames and incrementally forms a point cloud describing the object. We conduct experiments on a database of different object types showing promising matching results on both a privately collected set of videos and those freely available on the Web such that on YouTube. Increase in accuracy of up to 20% over the baseline of using a single frame matching is shown to be possible.
Improved optimal seam selection blending for fast video stitching of videos captured from freely moving devices

We investigate the problem of stitching timely synchronized video streams captured by freely moving devices. Recently, it was shown that using frame-to-frame correlation can greatly enhance the efficiency and effectiveness of video stitching algorithms [19]. In this paper, we address some of the shortcomings in , namely the simple blending approach, causing almost a third of stitching errors and the fact that the stitching algorithm is only tested on a frame-by-frame basis which does not realistically mimic the user perception of the output system quality as a complete video. We propose the use of a modified blending technique based on optimal seam selection and experimentally validate its superiority using precision, recall and F1 measures on a frame-by-frame basis, while maintaining low computational complexity. Furthermore, we validate that the performance gains measured on a frame-by-frame basis are also evident when the stitched video output is evaluated as a single unit.
Human age estimation using enhanced bio-inspired features (EBIF)

The Aging process is a non-reversible process, causing human face characteristics to change over time as hair whitening, muscles drop and wrinkles. Recently, age estimation from facial images has emerged as a prominent research area. One of the most successful works is based on biologically inspired features (BIF). In this paper we extend BIF by incorporating fine detailed facial features, automatic initialization using active shape models and analyzing a more complete facial area by including the forehead details. Furthermore, we combine regression-based and classification-based models and test them experimentally on standard datasets showing the superiority of our proposed algorithm (extended BIF - EBIF) over the state-of-the-art methods.
Highly efficient human action recognition using compact 2DPCA-based descriptors in the spatial and transform domains

Human action recognition is considered as a challenging problem in the field of computer vision. Most of the reported algorithms are computationally expensive. In this paper, a novel system for human action recognition based on Two-Dimensional Principal Component Analysis (2DPCA) is presented. This method works directly on the optical flow and / or silhouette extracted from the input video in both the spatial domain and the transform domain. The algorithm reduces the computational complexity and storage requirements, while achieving high recognition accuracy, compared with the most recent reports in the field. Experimental results performed on the Weizmann action and the INIRIA IXMAS datasets confirm the excellent properties of the proposed algorithm.
Soccer video summarization using enhanced logo detection

We propose an automatic soccer video summarization engine which relies on an improved algorithm for the detection of replay shots which delineate interesting events. Video shots are first detected using dominant color and histogram intersection methods. Replay shots are detected using an improved technique, then processed through a set of mid-level descriptors (goal-mouth and score board with other cinematic features) and finally fed into a rule-based classifier. The proposed summarization has been experimentally tested on 6 hours from 10 videos in total. The proposed logo-based replay detection technique achieves 100% recall with 96.3% precision. Interesting events such as goals are detected with 100% recall and 100% precision, attacks with 91.7% recall and 86.7% precision) and other events such as (fouls, free kicks etc) with 90.8% recall and 95% precision.
Semantic segmentation as image representation for scene recognition

We introduce a novel approach towards scene recognition using semantic segmentation maps as image representation. Given a set of images and a list of possible categories for each image, our goal is to assign a category from that list to each image. Our approach is based on representing an image by its semantic segmentation map, which is a mapping from each pixel to a predefined set of labels. Among similar high-level approaches, ours has the capability of not only representing what semantic labels the scene contains, but also their shapes, sizes and locations. We also investigate the effect of varying experiment parameters, including varying labels used, semantic segmentation technique, and semantic training source. We obtain state-of-the-art results over Siftflow and MSRC-21 datasets.
Real-Time Multi-scale Action Detection from 3D Skeleton Data

In this paper we introduce a real-time system for action detection. The system uses a small set of robust features extracted from 3D skeleton data. Features are effectively described based on the probability distribution of skeleton data. The descriptor computes a pyramid of sample covariance matrices and mean vectors to encode the relationship between the features. For handling the intra-class variations of actions, such as action temporal scale variations, the descriptor is computed using different window scales for each action. Discriminative elements of the descriptor are mined using feature selection. The system achieves accurate detection results on difficult unsegmented sequences. Experiments on MSRC-12 and G3D datasets show that the proposed system outperforms the state-of-the-art in detection accuracy with very low latency. To the best of our knowledge, we are the first to propose using multi-scale description in action detection from 3D skeleton data.
Synthetic training in object detection

We introduce new approaches for augmenting annotated training datasets used for object detection tasks that serve achieving two goals: reduce the effort needed for collecting and manually annotating huge datasets and introduce novel variations to the initial dataset that help the learning algorithms. The methods presented in this work aim at relocating objects using their segmentation masks to new backgrounds. These variations comprise changes in properties of objects such as spatial location in the image, surrounding context and scale. We propose a model selection approach to arbitrate between the constructed model on a per class basis. Experimental results show gains that can be harvested using the proposed approach.