nils-herrmann/clean_scirepeval huggingface dataset:
Tractable MIMO beampattern design under constant modulus waveform constraint

Multiple-Input Multiple-Output (MIMO) radar system allows each antenna element to transmit a different waveform. This waveform diversity can be utilized to enhance the beampattern design, in particular effective management of radar radiation power in directions of interest. In this paper, we address the problem of designing a beampattern for Multiple-Input Multiple-Output (MIMO) radar, which in turn is determined by the transmit waveform. While unconstrained design is straightforward, a key open challenge is enforcing the constant modulus constraint on the radar waveform. Existing beampattern design methods that address constant modulus invariably lead to a stiff trade-off between analytical tractability (achieved by relaxations and approximations) and realistic design that exactly achieves constant modulus but is computationally burdensome. A new approach is proposed in our work, which involves solving the hard non-convex problem of beampattern design using a sequence of convex Equality Constrained Quadratic Programs, each of which has a closed form solution. Constant modulus is achieved at convergence, which we prove formally is possible under mild and realistic assumptions. We evaluate the proposed successive closed forms (SCF) algorithm against state of the art MIMO beampattern design techniques and show that SCF breaks the trade-off between desirable performance and the associated computation cost.
Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture sonar. Sophisticated classification techniques can now be used in sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC), which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for sonar ATR that retains SRC’s robustness while also being able to handle targets with diverse geometric arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose-corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution toward use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the U.S. Naval Surface Warfare Center.
Ghost-Free High Dynamic Range Imaging via Rank Minimization

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm using a low-rank matrix completion framework, which we call RM-HDR. Based on the assumption that irradiance maps are linearly related to low dynamic range (LDR) image exposures, we formulate ghost region detection as a rank minimization problem. We incorporate constraints on moving objects, i.e., sparsity, connectivity, and priors on under- and over-exposed regions into the framework. Experiments on real image collections show that the RM-HDR can often provide significant gains in synthesized HDR image quality over state-of-the-art approaches. Additionally, a complexity analysis is performed which reveals computational merits of RM-HDR over recent advances in deghosting for HDR.
Object classification based on behaviour patterns

With the recent explosion of surveillance videos, media management has gained n increasing popularity. Addressing this challenge, in this paper, we propose a Surveillance Media Management framework for object detection and classification based on behaviour patterns. The objectives of the paper are: (i) demostrating the discriminative power of behaviour features for object recognition and classification, (ii) proposing a behavioural fuzzy classifier which progressively discriminate objects by including different degrees of uncertainty in the classification process and (iii) presenting a Surveillance Media Management system to extract semantic media information and provide unsupervised object classification from raw surveillance videos. The performance of the proposed system has been thoroughly evaluated on AVSS 2007 surveillance dataset and as the results indicate the proposed technique enhances object classification performance. (6 pages)
Pyramidal Model for Image Semantic Segmentation

We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination of an appearance-based pixel classification and a pixel context model. In our proposal, the images are initially over-segmented in dense patches. The proposed pyramidal model naturally embeds the compositional nature of a scene to achieve a multi-scale contextualisation of patches. This is obtained by imposing an order on the patches aggregation operations towards the final scene. The nodes of the pyramid (that is, a dendrogram) thus represent patch clusters, or super-patches. The probabilistic model favours the homogeneous labelling of super-patches that are likely to contain a single object instance, modelling the uncertainty in identifying such super-patches. The proposed model has several advantages, including the computational efficiency, as well as the expandability. Initial results place the model in line with other works in the recent literature.
Video-based camera registration for augmented reality

A video-based approach for camera scene registration in augmented reality systems is presented. The presented technique relies on the definition of a model, which is derived from an appropriate parametric linear optimization problem. The optimal parameters are sought in the solution space defined by physically meaningful constraints. Solving the underlying regularized linear problem, we expect to overcome the major shortcoming observed in image-based augmented reality and telepresence systems: the extreme lack of robustness due to the ill-posed nature of the calibration problem. Several computer experiments have been conducted in order to assess the performance of the introduced technique.
Enhanced inter-prediction using Merge Prediction Transformation in the HEVC codec

Merge prediction is a novel technique introduced in the HEVC standard to improve inter-prediction exploiting redundancy of themotion information. We propose in this paper a new approach to enhance the Merge mode in a typical HEVC encoder using parametric transformations of the Merge prediction candidates. An Enhanced Inter-Prediction module is implemented in HEVC using Merge Prediction Transformation (MPT), integrated with the HEVC new features such as the large coding units (CU) and the recursive prediction unit partitioning. The MPT parameters are quantised according to the CU depth and the current QP. The optimal quantization steps are derived via statistical analysis as illustrated in the paper. Results show consistent improvements over conventional HEVC encoding in terms of rate-distortion performance, with a small impact on the encoding complexity and negligible impact on the decoding complexity.
Adaptive precision motion estimation for HEVC coding

Most video coding standards, including the state-of-the-art High Efficiency Video Coding (HEVC), make use of sub-pixel Motion Estimation (ME) with Motion Vectors (MV) at fractional precisions to achieve high compression ratios. Unfortunately, sub-pixel ME comes at very high computational costs due to the interpolation step and additional motion searches. In this paper, a fast sub-pixel ME algorithm is proposed. The MV precision is adaptively selected on each block to skip the half or quarter precision steps when not needed. The algorithm bases the decision on local features, such as the behaviour of the residual error samples, and global features, such as the amount of edges in the pictures. Experimental results show that the method reduces total encoding time by up to 17.6% compared to conventional HEVC, at modest efficiency losses.
Subspace clustering of images using Ant colony Optimisation

Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel approach for subspace clustering based on Ant Colony Optimisation and its learning mechanism. The proposed algorithm breaks the assumption that all of the clusters in a dataset are found in the same set of dimensions by assigning weights to features according to the local correlations of data along each dimension. Experiment results on real image datasets show the need for feature selection in clustering and the benefits of selecting features locally.
A Topology Preserving Approach for Image Classification

In this paper, an approach for image analysis and classification is presented. It is based on a topology preserving approach to automatically create a relevance map from salient areas in natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of this approach is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology preservation module based on a growing neural gas network. Classification is achieved by simulating the high-level top-down visual information perception in primates followed by incremental Bayesian parameter estimation. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.
Improving inter prediction in HEVC with residual DPCM for lossless screen content coding

Video content containing computer generated objects is usually denoted as screen content and is becoming popular in applications such as desktop sharing, wireless displays, etc. Screen content images and videos are characterized by high frequency details such as sharp edges and high contrast image areas. On these areas classical lossy encoding tools - spatial transform plus quantization - may significantly compromise their quality and intelligibility. Therefore, lossless coding is used instead and improved coding tools should be specifically devised for screen content. In this context this paper proposes a residual differential pulse code modulation (RDPCM) applied to inter predicted residuals and tested in the context of the HEVC range extension development. The proposed method exploits the spatial correlation present in blocks containing edges or text areas which are poorly predicted by motion compensation. In addition to the baseline inter RDCPM, two improvements to the compression efficiency and the overall throughput are presented and assessed. When compared to HEVC lossless coding as specified in Version 1 of the standard, the proposed algorithm achieves up to 8% average bitrate reduction while not increasing the overall decoding complexity.
Influence of downsampling filter characteristics on compression performance in wavelet-based scalable video coding

The application of different downsampling filters in video coding directly models visual information at lower resolutions and influences the compression performance of a chosen coding system. In wavelet-based scalable video coding the spatial scalability is achieved by the application of wavelets as downsampling filters. However, characteristics of different wavelets influence the performance at targeting spatio-temporal decoding points. An analysis of different downsampling filters in popular wavelet-based scalable video coding schemes is presented. Evaluation is performed for both intra- and inter-coding schemes using wavelets and standard downsampling strategies. On the basis of the obtained results a new concept of inter-resolution prediction is proposed, which maximises the average performance using a combination of standard downsampling filters and wavelet-based coding.
New perspectives on image compression using a cartoon - texture decomposition model

A new method for multilayered image representation and its application in image compression are presented. The basic idea is to separate an image into two different layers having fundamentally different structures. Nonlinear diffusion is used to smooth out image texture in order to obtain a large scale piecewise smooth image representation called "cartoon". The residual part is considered as "pure texture". Since the best approximations of these two structurally different image layers can be realised by different transforms, higher compression ratios are achieved using suitably chosen transforms prior to individual encoding of each layer. Transform basis tailored to each layer are derived from a comprehensive empirical study. Several experiments were conducted to evaluate the performance of the proposed approach. The results show that in most cases the proposed technique achieves objectively slightly better and subjectively superior results than well-established image compression algorithms.
Cultural Artefacts in Education: Analysis, Ontologies and Implementation

Adaptive Web technologies are often used in distance learning scenarios with little regard for learnerspsila cultural background. The CAE questionnaire determines the cultural artefacts that influence a learners behaviour within an educational environment. This paper presents the CAE-L cultural ontology along with an analysis of three countries (China, Ireland and UK). This ontology is instantiated to determine their dasiacultural stereotypepsila which can then be used to define a layer of adaptation that traditional systems ignore.
Scalable Video Transmission Using Double Binary Turbo Code

In this paper, we propose a novel efficient scheme for robust video transmission over wireless channels. The schema consists of motion compensated spatio-temporal wavelet decomposition scalable coder and double binary turbo code. The scalable bitstream is adapted with unequal error protection according to the relevance of video layers in the scalable bitstream. Cyclic redundancy check bits are also added to check error free delivery of scalable bitstream. Double binary turbo codes were used for protection since they perform better than classical turbo coders in terms of better convergence for iterative decoding and computational cost. Selected results of experimental evaluation are reported in the paper.
Live Scalable Video Streaming on Peer-to-Peer Overlays with Network Coding

Scalable video coding is a paradigm that allows partial decoding of the video stream at reduced resolution, frame-rate or quality, adapting to display requirements and reception conditions of heterogeneous receivers. Transmission of scalable data with prioritization enhances the transmission performance, reducing the sensitivity to network congestions and exploiting the multirate characteristic of scalable coding. Network coding is a novel transmission technique that allows intermediate network nodes to perform coding operations on the information in transit, as opposed to traditional routing. This yields to maximization of the transmission rate and encoding of the information with spatial diversity. We employ an overlay network that uses network coding and delivers scalable video with prioritization. We test the performance of the scalable streaming against a non-scalable system, when the upload bandwidth of the nodes is not known.
Efficient background subtraction with low-rank and sparse matrix decomposition

Decomposition of a video scene into background and foreground is an old problem, for which novel approaches in the last years have been proposed. The robust subspace approach based on a low-rank plus sparse matrix decomposition has shown a great ability to identify static parts from moving objects in video sequences. However, those models are still insufficient in realistic environments. In this paper, we propose a modified approximated robust PCA algorithm that can handle moving cameras and takes advantage of the block sparse structure of the pixels corresponding to the moving objects. Additionally, we propose a novel SVD-free algorithm for the case of rank-1 background that outperforms current state-of-the-art methods in computation cost/time as well as performance. Finally, experiments and numerical results evaluating the proposed methods are demonstrated.
Transcoding from H.264/AVC to awavelet-based scalable video codec

Scalable Video Coding (SVC) enables low complexity adaptation according to transmission and display requirements, providing an efficient solution for video content delivery through heterogeneous networks. However, legacy video and most commercially available content capturing devices use conventional non-scalable coding, e.g., H.264/AVC, to compress and store video streams. As a consequence and in order to fully exploit the advantages of SVC technology, efficient transcoding from conventionally coded to scalable content is urgently needed. In this paper an efficient transcoder from H.264/AVC to a wavelet-based SVC is proposed. The complexity of the transcoder is kept very low by using information extracted directly from the decoded H.264/AVC bitstream, such as motion vectors and the presence of residual data. The proposed approach has been tested with well known benchmarking sequences, showing a good performance in terms of decoded video quality and system complexity.
Fast motion estimation based on neighbouring cost similarity

Many video coding standards, including the state-of-the-art High Efficiency Video Coding (HEVC) standard, employ Motion Estimation (ME) at sub-pel precision level to improve the prediction accuracy. However, interpolation and additional Motion Vector (MV) search associated with sub-pel ME result in increased computational complexity. In this paper, an algorithm for fast ME is proposed based on neighbouring sample cost similarity. The costs corresponding to the neighbouring MVs around the optimal MV at different precision levels are analyzed and used to interpolate an error surface. The estimated location of the surface minimum is then used to either skip the remaining ME process, or to limit the number of points to test. Experimental evaluation on high resolution sequences shows that the proposed method can speed up the ME module by 55.5% for 1.5% BD rate losses. Furthermore, the proposed fast quarter-pel ME reduces the quarter-pel ME time by 79.6% while achieving only 17.6% of theoretical encoding losses.
Image annotation through gaming (TAG4FUN)

This paper introduces a new technique for image annotation in which social aspects of human-based computation are exploited. The proposed approach aims at exploiting what millions of single, online and cooperative gamers are keen to do, (in some cases gaming enthusiasts) to tackle the challenging image annotation task. The proposed approach deviates from the conventional “content-based image retrieval (CBIR)” paradigm, favored by the research community to tackle problems related to semantic annotation and tagging of multimedia content. The proposed approach focuses on social aspects of gaming and the use of humans in a widely distributed fashion through a process of human-based computation. It aims at motivating people towards image tagging while entertaining themselves. Regarding key aspect of label accuracy, a combination of computer vision techniques, machine learning and game strategies have been used.
Event based video coding architecture

In this work, scalable video codec (SVC) has been used for security surveillance video. The main event in the video is motion of an object. A strategy has been proposed to find out the different motion levels (events) in the video. The level of motion in a group of picture (GOP) is used to assign different scalability features to the GOP. The architecture of SVC has been modified to provide this support. The model of the modified SVC architecture is presented in detail. The improved system handles fewer amounts of data for processing and storage yet conveys all the important information related to surveillance video. The implementation and experimental results on the surveillance video has been presented. Results show that the proposed system efficiently detects motion and adapts to the scalability level accordingly.
Efficient side information encoding for text hardcopy documents

This paper proposes a new coding method that increases significantly the signal-to-watermark ratio in document watermarking algorithms. A possible approach to text document watermarking is to consider text characters as a data structure consisting of several modifiable features such as size, shape, position, luminance, among others. In existing algorithms, these features can be modified sequentially according to bit values to be embedded. In contrast, the solution proposed here uses a positional information coding approach to embed information. Using this approach, the information is related to the position of modified characters, and not to the bit embedded on each character. This coding is based on combinatorial analysis and it can embed more bits in comparison to the usual methods, given a distortion constraint. An analysis showing the superior performance of positional coding for this type of application is presented. Experiments validate the analysis and the applicability of the method.
Event Detection and Clustering for Surveillance Video Summarization

The target of surveillance summarization is to identify high-value information events in a video stream and to present it to a user. In this paper we present surveillance summarization approach using detection and clustering of important events. Assuming that events are main source of energy change between consecutive frames set of interesting frames is extracted and then clustered. Based on the structure of clusters two types of summaries are created static and dynamic. Static summary is build of key frames that are organized in clusters. Dynamic summary is created from short video segments representing each cluster and is used to lead user to the event of interest captures in key frames. We describe our approach and present experimental results.
Multiple Early Termination for fast HEVC coding of UHD content

The recently ratified High Efficiency Video Coding (HEVC) standard is significantly outperforming previous video coding standards in terms of compression efficiency. However, this comes at the cost of very high computational complexity, which may limit its real-time usage, particularly when targeting Ultra High Definition (UHD) applications. In this paper, an analysis of HEVC coding on UHD content is presented, showing that on average more than 18% of the total encoding time is spent performing uni-directional Motion Estimation (ME) even when using fast algorithms such as Enhanced Predictive Zonal Search (EPZS). In order to speed up the ME process, a novel approach for fast inter prediction is proposed in this paper based on a Multiple Early Termination (MET) decision process. EPZS is only performed in blocks in which it is needed based on local features of the encoded content, or it is skipped otherwise. Experimental results show that the algorithm achieves on average 9.3% speed-ups over conventional HEVC, at the cost of very small BD-rate losses.