nils-herrmann/clean_scirepeval huggingface dataset:
Anti-interference waveform design for automotive radar

With the increasing amount of car equipped with collision avoidance radar, the mutual interference is becoming a problem when the cars run in the roads. The mutual interference cancellation techniques are getting more and more important to preserve the reliability of the automotive radar. Considering conventional radar with frequency agile, this paper proposes a new signal design method for automotive radar that uses combination from a fixed number of constant frequency and triangular frequency modulation waveform, and the constant frequency and triangle wave slope random change. This way can most suppress mutual interference between automotive radar. This paper analyzes the waveform random combination method and the principle of waveform design. The results demonstrate that this method can not only realize the function of the vehicle collision avoidance radar multi-target detection, and also can effectively reduce the false alarm rate resulting from mutual interference.
A Robust Homography Estimation Method Based on Keypoint Consensus and Appearance Similarity

In this paper, a robust homography estimation method is proposed to match multiview images in the uncalibrated case. This method formulates a new loss function to verify homography hypothesis, which combines models of key-point consensus and appearance similarity. In the consensus model, Lap lace distribution is exploited to better characterize the imprecision of key points. And in the appearance model, a truncated exponential function is utilized to represent the distribution of image similarity values. With these improvements, our method can be more robust in complex situations when a rather high percentage of key points are ambiguous and unreliable, and can output a more accurate homography that satisfies most pixels' geometric relationship. The experimenttal results highlight the robustness and accuracy of our method in the matching tasks of both synthetic images and real life photos.
Evaluation of Different Algorithms of Nonnegative Matrix Factorization in Temporal Psychovisual Modulation

Temporal psychovisual modulation (TPVM) is a newly proposed information display paradigm, which can be implemented by nonnegative matrix factorization (NMF) with additional upper bound constraints on the variables. In this paper, we study all the state-of-the-art algorithms in NMF, extend them to incorporate the upper bounds and discuss their potential use in TPVM. By comparing all the NMF algorithms with their extended versions, we find that: 1) the factorization error of the truncated alternating least squares algorithm always fluctuates throughout the iterations, 2) the alternating nonnegative least squares based algorithms may slow down dramatically under the upper bound constraints, and 3) the hierarchical alternating least squares (HALS) algorithm converges the fastest and its final factorization error is often the smallest among all the algorithms. Based on the experimental results of the HALS, we propose a guideline of determining the parameter setting of TPVM, that is, the number of viewers to support and the scaling factor for adjusting the light intensity of the images formed by TPVM. This paper will facilitate the applications of TPVM.
Publication Popularity Modeling via Adversarial Learning of Profile-Specific Dynamic Process

Due to the rapidly growing volume of publications, it is an urgent task to evaluate the potential impact of emerging works and identify valuable publications for the benefit of researchers with limited time. The impact of authors is significant in terms of personal promotion and fund raising. However, the criterion used to evaluate the impact of authors is largely based on credits they have received, not potential credits. We take the citation count, the most direct and widely used metric for publication popularity, as the potential impact and evaluate its predictability. To this end, we propose a neural network-based point process model for predicting the citation count of individual publications. Our approach integrates signals from paper-specific features and their citation traces, which reflect the trend of either losing or gaining popularity. The model is learned in an adversarial way, which mitigates the bias-exposure efficiently. We verify our model on the largest publicly available academic publication repository, and our model outperforms alternatives with a notable margin.
Observation of Matthew Effects in Sina Weibo microblogger

This paper researches on Matthew Effect in Sina Weibo microblogger. We choose the microblogs in the ranking list of Hot Microblog App in Sina Weibo microblogger as target of our study. The differences of repost number of microblogs in the ranking list between before and after the time when it enter the ranking list of Hot Microblog app are analyzed. And we compare the spread features of the microblogs in the ranking list with those hot microblogs not in the list and those ordinary microblogs of users who have some microblog in the ranking list before. Our study proves the existence of Matthew Effect in social network.
Object Geometry Based Error Resilient Video Coding

We present an object geometry based error resilience method. We extract object geometry through analyzing motion vector field at the encoder using the iterative self-organizing data analysis technique algorithm. The extracted object geometry, in the form of an index map, is then embedded in the bitstream. At the decoder side, this information is exploited to recover motion vectors of corrupted macroblocks. The proposed method has been validated in MPEG2 codec in a standard-compliant way. Experimental results carried out on several sequences have shown that the proposed method outperforms two conventional temporal error concealment methods by an average PSNR gain of 0.9 dB and 1.5 dB, respectively.
Measuring orderliness based on social force model in collective motions

Collective motions, one of the coordinated behaviors in crowd system, widely exist in nature. Orderliness characterizes how well an individual will move smoothly and consistently with his neighbors in collective motions. It is still an open problem in computer vision. In this paper, we propose an orderliness descriptor based on correlation of interactive social force between individuals. In order to include the force correlation between two individuals in a distance, we propose a Social Force Correlation Propagation algorithm to calculate orderliness of every individual effectively and efficiently. We validate the effectiveness of the proposed orderliness descriptor on synthetic simulation. Experimental results on challenging videos of real scene crowds demonstrate that orderliness descriptor can perceive motion with low smoothness and locate disorder.
Learning dictionary via subspace segmentation for sparse representation

Sparse signal representation based on redundant dictionaries contributed to much progress in image processing in the past decades. But the common overcomplete dictionary model is not well structured and there is still no guideline for selecting the proper dictionary size. In this paper, we propose a new algorithm for dictionary learning based on subspace segmentation. Our algorithm divides the training data into sub-spaces and constructs the dictionary by extracting the shared basis from multiple subspaces. The learned dictionary is well structured and its size is adaptive to the training data. We analyze this algorithm and demonstrate its ability on some initial supportive experiments using real image data.
Real time and scene invariant crowd counting: Across a line or inside a region

In this paper, we propose a blob-based method of crowd counting across a line of interest (LOI), which can be further extended to counting inside a region of interest (ROI). Firstly, we detect moving blobs in which low-level features are extracted and grouped. Since features vary with different walking pace, blob velocity is estimated using optical flow, and principal velocity component is further extracted in case of the interference of local articulated motion. Besides, spatial normalization is implemented to compensate for different image depth and different scenes. Finally, we apply Gaussian Process Regression to model the global linear and local nonlinear relationships between the extracted features and crowd counts. The experimental results demonstrate that the proposed method has good applicability to both LOI and ROI crowd counting. As compared with the state-of-the-art methods, our method achieves higher counting accuracy over two representative datasets and meanwhile processes much faster.
CNN-based shot boundary detection and video annotation

With the explosive growth of video data, content-based video analysis and management technologies such as indexing, browsing and retrieval have drawn much attention. Video shot boundary detection (SBD) is usually the first and important step for those technologies. Great efforts have been made to improve the accuracy of SBD algorithms. However, most works are based on signal rather than interpretable features of frames. In this paper, we propose a novel video shot boundary detection framework based on interpretable TAGs learned by Convolutional Neural Networks (CNNs). Firstly, we adopt a candidate segment selection to predict the positions of shot boundaries and discard most non-boundary frames. This preprocessing method can help to improve both accuracy and speed of the SBD algorithm. Then, cut transition and gradual transition detections which are based on the interpretable TAGs are conducted to identify the shot boundaries in the candidate segments. Afterwards, we synthesize the features of frames in a shot and get semantic labels for the shot. Experiments on TRECVID 2001 test data show that the proposed scheme can achieve a better performance compared with the state-of-the-art schemes. Besides, the semantic labels obtained by the framework can be used to depict the content of a shot.
Motion Part Regularization: Improving action recognition via trajectory group selection

Dense local trajectories have been successfully used in action recognition. However, for most actions only a few local motion features (e.g., critical movement of hand, arm, leg etc.) are responsible for the action label. Therefore, highlighting the local features which are associated with important motion parts will lead to a more discriminative action representation. Inspired by recent advances in sentence regularization for text classification, we introduce a Motion Part Regularization framework to mine for discriminative groups of dense trajectories which form important motion parts. First, motion part candidates are generated by spatio-temporal grouping of densely extracted trajectories. Second, an objective function which encourages sparse selection for these trajectory groups is formulated together with an action class discriminative term. Then, we propose an alternative optimization algorithm to efficiently solve this objective function by introducing a set of auxiliary variables which correspond to the discriminativeness weights of each motion part (trajectory group). These learned motion part weights are further utilized to form a discriminativeness weighted Fisher vector representation for each action sample for final classification. The proposed motion part regularization framework achieves the state-of-the-art performances on several action recognition benchmarks.
A parallel-fusion RNN-LSTM architecture for image caption generation

The models based on deep convolutional networks and recurrent neural networks have dominated in recent image caption generation tasks. Performance and complexity are still eternal topic. Inspired by recent work, by combining the advantages of simple RNN and LSTM, we present a novel parallel-fusion RNN-LSTM architecture, which obtains better results than a dominated one and improves the efficiency as well. The proposed approach divides the hidden units of RNN into several same-size parts, and lets them work in parallel. Then, we merge their outputs with corresponding ratios to generate final results. Moreover, these units can be different types of RNNs, for instance, a simple RNN and a LSTM. By training normally using NeuralTalk1 platform on Flickr8k dataset, without additional training data, we get better results than that of dominated structure and particularly, the proposed model surpass GoogleNIC in image caption generation.
Joint bit-rate allocation strategy for parallel HDTV video encoder

This paper presents a joint bit allocation strategy for parallel HDTV video encoders. The basic idea is to partition a HDTV picture into six smaller sub-pictures, then compress each sub-picture using a cheap MPEG-2 MP@ML encoder. Without coordination among sub-encoders, these encoded sub-pictures may have unequal picture qualities. To maintain a uniform quality HDTV picture, the HDTV video encoder uses the joint bit allocation strategy based on the following two issues: (1) scene change detection and (2) picture complexity.
Crowd instability analysis using velocity-field based social force model

This paper proposes a novel method to locate crowd behavior instability spatio-temporally using a velocity-field based social force model. Considering the impacts of velocity field on interaction force between individuals, we establish an improved social force model by introducing collision probability in view of velocity distribution. As compared with commonly-used social force model, which defines interaction force as a dependent variable of relative geometric (physical) position of the individuals, this improved model can provide a better prediction of interactions using the collision probability in a dynamic crowd. With spatio-temporal instability analysis, we can extract video clips with potential abnormality and as well locate region of interest where abnormality is likely to happen. The experimental results demonstrate that the proposed method can be applied to detection of abnormal events with high accuracy of instability estimation due to the velocity-field based social force model.
Unified framework for colourising corrupted image

Colourisation is a process of adding colours to greyscale image. It is noticed that the colourisation process for corrupted image usually needs image to be completed and then colourised. In this study the authors present a novel scribble-based colourisation algorithm of corrupted greyscale image. The authors construct a block-based bilateral filter (BBF) framework for image completion and colourisation. Distance transform and an adaptive weight selection scheme are introduced to the BBF framework in order to achieve improvements of accuracy and speed. The rationale behind the authors' algorithm is that image completion and colourisation can be both regarded as missing data recovery problem. Unlike existing sequential image completion method and colourisation method, the authors'algorithm can accomplish image completion and colourisation work under a proposed novel unified framework. Experiments results show the benefits of the authors'method.
Multi-Scale Gabor Phase-Based Stereo Matching using Graph Cuts

In this paper, we present a multi-scale Gabor phase-based stereo matching scheme. Unlike the mechanism in the existing phase-based stereo matching methods, where disparity is formulated as the ratio of phase difference between two views to the local frequency at the given position, we set up a robust data measure from multi-scale Gabor phases to greatly alleviate the negative effect of phase singularity. A cost function is then advanced based on this robust data measure. To further improve the accuracy of disparity estimation, we formulate the cost function as three coupled Markov Random Field (MRF) cost terms in frequency domain. To obtain globally optimized disparity map in wide range, graph cut is employed to perform the minimization of the cost function. Compared with the state-of-the-art stereo matching methods, experimental results demonstrate that our approach gets comparable matching performance in indoor scenes and achieves much better results in aerial scenes.
Video Segmentation via Multiple Granularity Analysis

We introduce a Multiple Granularity Analysis framework for video segmentation in a coarse-to-fine manner. We cast video segmentation as a spatio-temporal superpixel labeling problem. Benefited from the bounding volume provided by off-the-shelf object trackers, we estimate the foreground/ background super-pixel labeling using the spatiotemporal multiple instance learning algorithm to obtain coarse foreground/background separation within the volume. We further refine the segmentation mask in the pixel level using the graph-cut model. Extensive experiments on benchmark video datasets demonstrate the superior performance of the proposed video segmentation algorithm.
Person Re-Identification Over Camera Networks Using Multi-Task Distance Metric Learning

Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.
Cross-Layered Hidden Markov Modeling for Surveillance Event Recognition

In this paper, a novel Cross-Layered Hidden Markov Model (CLHMM) is proposed for high accuracy and low complexity Surveillance Event Recognition (SER). Unlike existing Layered HMM (LHMM) whose inferences are limited in adjacent layers, cross-layer inferences are designed in CLHMM to strengthen reasoning efficiency and reduce computational complexity. One Common Feature Particle Set (CFPS) is also developed in CLHMM to offer the model an assembly of pixel level observations, expert knowledge and Baum-Welch algorithm are combined to achieve optimized performance in CLHMM learning. Experimental results on typical surveillance test sequences showed that CLHMM outperforms LHMM in terms of accuracy and computational complexity.
Cooperative Stereo Matching using Quaternion Wavlets and Top-Down Segmentation

We explore the principles of quaternion wavelet construction for achieving multiscale analysis of geometric image features. Then the quaternion wavelets are applied to propose a cooperative stereo matching algorithm using top-down segmentation-based disparity propagation. Without bidirectional matching to remove ambiguous outliers, uniqueness constraint is enforced on cost function by inhibiting the matches along similar sightlines. To produce smooth disparity maps with the discontinuities well-preserved, cost aggregation is performed in segmentation-based local support and high confidence matches serve as heavyweight seeds for disparity propagation in the supports. Compared with the current matching methods based on quaternion wavelets, the main merit of the proposed algorithm is that the matching results are encouraging in extensive comparison data, ranging from calibrated images to uncalibrated images, indoor images to aerial images.
Micro-Doppler analysis and parameter estimation of the rotating linear rigid target

Micro-Doppler signature produced by micro-motion contains movement and structure information which is useful for radar classification and recognition. Aimed to classification and recognition of the target with the blades, the motion dynamics and echo models of the rotating linear rigid body in single frequency are set up. The micro-Doppler modulations of the rotating single point and whole linear rigid body are quantitatively analyzed. Furthermore, the methods by Smoothed Pseudo Wigner-Ville distribution and image processing are proposed for micro-Doppler modulation extraction and parameter estimation. Finally, simulation results are covered to demonstrate the correctness of theoretical analysis and the validity of the parameter estimation.
Learning reconfigurable scene representation by tangram model

This paper proposes a method to learn reconfigurable and sparse scene representation in the joint space of spatial configuration and appearance in a principled way. We call it the tangram model, which has three properties: (1) Unlike fixed structure of the spatial pyramid widely used in the literature, we propose a compositional shape dictionary organized in an And-Or directed acyclic graph (AOG) to quantize the space of spatial configurations. (2) The shape primitives (called tans) in the dictionary can be described by using any “off-the-shelf” appearance features according to different tasks. (3) A dynamic programming (DP) algorithm is utilized to learn the globally optimal parse tree in the joint space of spatial configuration and appearance. We demonstrate the tangram model in both a generative learning formulation and a discriminative matching kernel. In experiments, we show that the tangram model is capable of capturing meaningful spatial configurations as well as appearance for various scene categories, and achieves state-of-the-art classification performance on the LSP 15-class scene dataset and the MIT 67-class indoor scene dataset.
High efficiency quasi-optical mode converter for a 140 GHz 1 MW gyrotron

Current high power gyrotrons employ an internal quasi-optical mode converter to extract the rotating high-order cavity modes into a linearly polarized fundamental Gaussian beam. Conversion efficiencies close to unity are required since trapped RF energy jeopardizes the stable operation regime and may lead to heating of the tube structure. For the 1 MW continuous wave (CW) gyrotron, operated in the TE/sub 28,8/ mode at 140 GHz, in order to achieve a usable fundamental Gaussian distribution of the output beam with low diffraction losses, a high efficiency quasi-optical mode converter has been optimized and designed at Forschungszentrum Karlsruhe (FZK). It consists of an advanced dimple-wall antenna as a launcher, one quasi-elliptical mirror and two toroidal mirrors as the beam-forming system.