nils-herrmann/clean_scirepeval huggingface dataset:
Efficient parallel message computation for MAP inference

First order Markov Random Fields (MRFs) have become a predominant tool in Computer Vision over the past decade. Such a success was mostly due to the development of efficient optimization algorithms both in terms of speed as well as in terms of optimality properties. Message passing algorithms are among the most popular methods due to their good performance for a wide range of pairwise potential functions (PPFs). Their main bottleneck is computational complexity. In this paper, we revisit message computation as a distance transformation using a more formal setting than  to generalize it to arbitrary PPFs. The method is based on [20] yielding accurate results for a specific class of PPFs and in most other cases a close approximation. The proposed algorithm is parallel and thus enables us to fully take advantage of the computational power of parallel processing architectures. The proposed scheme coupled with an efficient belief propagation algorithm  and implemented on a massively parallel coprocessor provides results as accurate as state of the art inference methods, though is in general one order of magnitude faster in terms of speed.
On subjective and objective quality evaluation of point cloud geometry

Point clouds have emerged as a promising solution for immersive representation of 3D contents. In this paper an interactive subjective quality assessment for point clouds is proposed. Quality assessment of geometry information in point clouds subject to realistic types of degradations is performed and correlation between state-of-the-art objective metrics and ground truth subjective scores is investigated. Preliminary results suggest that there is a need for more appropriate objective metrics, since the current solutions are not able to provide accurate predictions of quality for every type of degradations and contents.
Application of the evidence framework to brain-computer interfaces

A brain-computer interface (BCI) is a communication system, that implements the principle of "think and make it happen without any physical effort". This means a BCI allows a user to act on his environment only by using his thoughts, without using peripheral nerves and muscles. Nearly all BCIs contain as a core part a machine learning algorithm, which learns from training data a function, that can be used to discriminate different brain activities. In the present work we use a Bayesian framework for machine learning, the evidence framework to develop a variant of linear discriminant analysis for the use in a BCI based on electroencephalographic measurements (EEG). Properties of the resulting algorithm are: a) a continuous probabilistic output is given, b) fast estimation of regularization constants, and c) the possibility to select among different feature sets, the one which is most promising for classification. The algorithm has been tested on one dataset from the BCI competition 2002 and two datasets from the BCI competition 2003 and provides a classification accuracy of 95%, 81%, and 79% respectively.
Testbed for subjective evaluation of omnidirectional visual content

Omni-directional visual content is a form of representing graphical and cinematic media content which provides subjects with the ability to freely change their direction of view. Along with virtual reality, omnidirectional imaging is becoming a very important type of the modern media content. This brings new challenges to the omnidirectional visual content processing, especially in the field of compression and quality evaluation. More specifically, the ability to assess quality of omnidirectional images in reliable manner is a crucial step to provide a rich quality of immersive experience. In this paper we introduce a testbed suitable for subjective evaluations of omnidirectional visual contents. We also show the results of a conducted pilot experiment to illustrate the applicability of the proposed testbed.
Change detection and background extraction by linear algebra

Change detection plays a very important role in real-time image analysis, e.g., detection of intruders. One key issue is robustness to varying illumination conditions. We propose two techniques for change detection that have been developed to deal with variations in illumination and background, with real-time capabilities. The foundations of these techniques are based on a vector model of images and on the exploitation of the concepts of linear dependence and linear independence. Furthermore, the techniques are compatible with physical photometry. A detailed description of the proposed detector and three state-of-the art change detectors is also provided. For the purposes of comparison, an evaluation procedure is presented consisting of both objective and subjective parts. This evaluation procedure results in a final performance value for each detector analyzed.
A simple and efficient binary shape coding technique based on bitmap representation

This article presents a technique based on the JBIG algorithm for binary shape coding in both lossless and lossy modes. Because it is applied directly to the bitmap representing the shape information, it bypasses the overhead in computation of an intermediate contour representation and its associated conversions. This leads to a simpler algorithm which is more suitable for a larger class of shape data. In addition a mechanism is proposed which allows a rate control for lossy coding mode.
A Reliable and Reversible Image Privacy Protection Based on False Colors

Protection of visual privacy has become an indispensable component of video surveillance systems due to pervasive use of video cameras for surveillance purposes. In this paper, we propose two fully reversible privacy protection schemes implemented within the JPEG architecture. In both schemes, privacy protection is accomplished by using false colors with the first scheme being adaptable to other privacy protection filters while the second is false color-specific. Both schemes support either a lossless mode in which the original unprotected content can be fully extracted or a lossy mode, which limits file size while still maintaining intelligibility. Our method is not region-of-interest (ROI)-based and can be applied on entire frames without compromising intelligibility. This frees the user from having to define ROIs and improves security as tracking ROIs under dynamic content may fail, exposing sensitive information. Our experimental results indicate the favorability of our method over other commonly used solutions to protect visual privacy.
A Novel Replica Detection System using Binary Classifiers, R-Trees, and PCA

Replica detection is a prerequisite for the discovery of copyright infringement and detection of illicit content. For this purpose, content-based systems can be an efficient alternative to watermarking. Rather than imperceptibly embedding a signal, content-based systems rely-on image similarity. Certain content-based systems use adaptive classifiers to detect replicas. In such systems, a suspect image is tested against every original, which can become computationally prohibitive as the number of original images grows. In this paper, we propose using R-tree indexing to decrease the necessary number of comparisons and rapidly select the most likely originals. Experimental results show that the proposed system performs very satisfactorily and that up to 99.3% of the originals can be discarded before applying the binary classifiers.
Recent advances in brain-computer interfaces

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The major goal of BCI research is to develop systems that allow disabled users to communicate with other persons, to control artificial limbs, or to control their environment. To achieve this goal, many aspects of BCI systems are currently being investigated. Research areas include evaluation of invasive and noninvasive technologies to measure brain activity, evaluation of control signals (i.e. patterns of brain activity that can be used for communication), development of algorithms for translation of brain signals into computer commands, and the development of new BCI applications. In this paper we give an overview of the aspects of BCI research mentioned above and highlight recent developments and open problems.
Probabilistic parameter-free motion detection

We propose an original probabilistic parameter-free method for the detection of independently moving objects in an image sequence. We apply a probabilistic perceptual principle, the Helmholtz principle, whose main advantage is the automatization of the detection decision, by providing a light control of the number of false alarms. Not only does this method localize the moving objects but it also answers the preliminary question of the presence of motion. In particular the method works even when no assumption on motion presence is made. The algorithm is composed of three independent steps: estimation of the dominant image motion, spatial segmentation of object boundaries and independent motion detection itself We emphasize that none of these steps needs any parameter tuning. Results on real image sequences are reported and validate the proposed approach.
Driver-response relationships between frontal EEG and Respiration during affective audiovisual stimuli

The complementary nature and the coordinative tendencies of brain and body are essential to the way humans function. Although static features from brain and body signals have been shown to reflect emotions, the dynamic interrelation of the two systems during emotional processes is still in its infancy. This study aims at investigating the way brain signals captured by Electroencephalography (EEG) and bodily responses reflected in respiration interact when watching music clips. A non-linear measure is applied to frontal EEG and respiration to determine the driver/driven relationship between these two modalities. The results reveal a unidirectional dependence from respiration to EEG which adds evidence to the bodily-feedback theory.
Introduction to the Special Section on Visual Computing in the Cloud: Fundamentals and Applications

Cloud computing involves a large number of terminals connected through a real-time high-speed network (such as the Internet). The adoption rates for private and hybrid cloud services increased to 40% in 2013, with computing shifting from on-premise infrastructure to the cloud. To keep pace with the ever-accelerating rate of innovation, companies are moving to the cloud. However, visual computing in the cloud brings great challenges, such as how to measure and then improve the quality of experience in cloud computing. This Special Section provides the image/video community a forum to present new academic research and industrial development in running visual computing services in the cloud. This Special Section aims to address fundamental and practical aspects of visual computing in the cloud, such as how to build cloud platforms that can cope with seemingly unlimited supply of content coming from traditional media sources as well as new media uploaded to the Internet (YouTube, Facebook, etc.); how to leverage cloud technology to build high-quality image/video browsing and delivery experiences for a global audience; how to ingest, encode, process, adapt, as well as protect contents and privacy of users; how to provide both on-demand and live-streaming capabilities; how to tag image/video and allow consumers to access the image/video contents with high availability; how to support image/video services in mobile devices; and how to perform real-time image/video analytics in the cloud, to mention a few among a diverse range of challenges.
Improved side information generation with iterative decoding and frame interpolation for Distributed Video Coding

Distributed video coding (DVC) is a new paradigm in video coding, which is receiving a lot of interests nowadays. Side Information (SI) generation is a key function in the DVC decoder, and plays a key-role in determining the performance of the codec. This paper proposes an improved side information generation scheme, which exploits both spatial and temporal correlations in the sequences. Partially decoded Wyner-Ziv (WZ) frames, based on initial SI by motion compensation temporal interpolation (MCTI), are exploited to improve the performance of the whole SI generation. In addition, an enhanced temporal frame interpolation is proposed, including motion vector refinement and smoothing, optimal compensation mode selection, and a new matching criterion for motion estimation. Simulation results show that the proposed scheme can achieve up to 2.3 dB improvement in rate distortion (RD) performance for video with high motion, when compared to state-of-the-art DVC.
Comparative Study of Trust Modeling for Automatic Landmark Tagging

Many images uploaded to social networks are related to travel, since people consider traveling to be an important event in their life. However, a significant amount of travel images on the Internet lack proper geographical annotations or tags. In many cases, the images are tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In this paper, we present a system for automatic geotag propagation in images based on the similarity between image content (famous landmarks) and its context (associated geotags). In such a scenario, however, an incorrect or a spam tag can damage the integrity and reliability of the automated propagation system. Therefore, for reliable geotags propagation, we suggest adopting a user trust model based on social feedback from the users of the photo-sharing system. We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. Results demonstrate that relying on user feedback is more efficient, since the number of propagated tags more than doubles without loss of accuracy compared to using other models or propagating without trust modeling.
Dynamic coding of visual information

This paper introduces a novel approach to visual data compression. The approach, named dynamic coding, consists of an effective competition between several representation models used for describing data portions. The image data is represented as the union of several regions each approximated by a representation model locally appropriate. The dynamic coding concept leads to attractive features such as genericness, flexibility, and openness and is therefore particularly suited to a multimedia environment in which many types of applications are involved. Dynamic coding is a general proposal to visual data compression and many variations on the same theme may be designed. They differ by the particular procedure by which the data is segmented into objects and the local representation model selected. As an illustrative example, a video compression scheme based on the principles of dynamic coding is presented. This compression algorithm performs a joint optimization of the segmentation (restricted to a so-called generalized quadtree partition) together with the representation models associated with each data segment. Four representation models are competing namely, fractal, motion compensation, text and graphics, and background modes. Optimality is defined with respect to a rate-distortion tradeoff and the optimization procedure leads to a multicriterion segmentation.
In Tags We Trust: Trust modeling in social tagging of multimedia content

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.
Subjective Quality Evaluation via Paired Comparison: Application to Scalable Video Coding

Scalable video coding is a powerful solution for content delivery in many interactive multimedia services due to its adaptability to varying terminal and network constraints. In order to successfully exploit such adaptability, it is necessary to understand users' preference among various scalability options and consequently develop an optimal bit rate adaptation strategy. In this paper, we present a study of subjective quality assessment of scalable video coding, which investigates the influence of the combination of scalability options on perceived quality with the goal of providing guidelines for an adaptive strategy that selects the optimal combination for a given bandwidth constraint. In particular, the study is based on paired comparison of stimuli that is suitable for our goal due to its simplicity and easiness. We propose a new method, called Paired Evaluation via Analysis of Reliability (PEAR), which analyzes paired comparison results and produces not only quality scores but also intuitive measures of confidence of the scores for significance analysis. Results and analysis of extensive subjective tests for two different scalable video codecs and high definition contents are described, from which general consistent conclusions are drawn. The video and subjective data used in the paper are publicly available to the research community.
Analysis of the Limits of Graph-Based Object Duplicate Detection

Several applications require accurate and efficient object duplicate detection methods, such as automatic video and image tag propagation, video surveillance, and high level image or video search. In this paper, we explore the limits of our recently proposed graph-based object duplicate detection method. The dependency of the performance with respect to the number of training images is assessed and the optimal detection parameters are determined. Furthermore, the differences among various object classes are analyzed. In this way, this paper provides an in-depth analysis of the graph based object duplicate detection method.
A simple method to obtain visual attention data in head mounted virtual reality

Automatic prediction of salient regions in images is a well developed topic in the field of computer vision. Yet, virtual reality omnidirectional visual content brings new challenges to this topic, due to a different representation of visual information and additional degrees of freedom available to viewers. Having a model for visual attention is important to continue research in this direction. In this paper we develop such a model for head direction trajectories. The method consists of three basic steps: First, a computed head angular speed is used to exclude the parts of a trajectory where motion is too fast to fixate viewer's attention. Second, fixation locations of different subjects are fused together, optionally preceded by a re-sampling step to conform to the equal distribution of points on a sphere. Finally, a Gaussian based filtering is performed to produce continuous fixation maps. The developed model can be used to obtain ground truth experimental data when eye tracking is not available.
Multimodal person search combining information fusion and relevance feedback

With the increasing amount of multimedia data, efficient tools for search and retrieval are needed. Since people are naturally one of the most interesting objects within these documents, a system for multimodal person search and retrieval has been developed. It combines the audiovisual analysis of persons with the query by example paradigm and relevance feedback to provide an efficient tool for searching multimedia data. For the relevance feedback, one and two class approaches are considered and compared to each other. Multimodal fusion techniques are used to exploit the complementary character of the audio and video information. The experimental results prove that multimodal person search and retrieval is feasible and more efficient than manual exploration.
Watermarking in the JPEG 2000 domain

There are several advantages to combine, at one end, the image coding and watermark insertion operations and, at the other end, the image decoding and watermark extraction. We describe a spread-spectrum-based watermarking technique in the framework of the JPEG 2000 still image compression. We also show that, by re-using the JPEG 2000 wavelet domain for watermark embedding, the proposed watermarking scheme exhibits a high robustness with respect to attacks which may occur in many applications.
Impact of video transcoding artifacts on the subjective quality

Video transcoding is an important step to enable interoperability between different networks, terminals, applications, and services for video communication. This paper studies the influence of typical video transcoding artifacts due to frame rate reduction and drift error on the subjective quality. Given a realistic dataset for a DVB-T to DVB-H transcoding scenario, the subjective quality before and after the transcoding is compared against each other. In order to quantify the influence of both artifacts, a pixel domain and an open loop transcoding solution have been considered. Since the strength of both artifacts depends largely on the initial encoding parameters, additional experiments have been conducted to quantify the influence of the distance between I frames and the number of consecutive B frames on the subjective quality.
A Framework for Evaluating Video Object Segmentation Algorithms

Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against an existing approach. Moreover, on the basis of subjective results, perceptual factors are introduced into the novel objective metric to meet the specificity of different segmentation applications such as video compression. Experimental results confirm the efficiency of the proposed evaluation criteria.
The JPEG 2000 still image compression standard

One of the aims of the standardization committee has been the development of Part I, which could be used on a royalty- and fee-free basis. This is important for the standard to become widely accepted. The standardization process, which is coordinated by the JTCI/SC29/WG1 of the ISO/IEC has already produced the international standard (IS) for Part I. In this article the structure of Part I of the JPFG 2000 standard is presented and performance comparisons with established standards are reported. This article is intended to serve as a tutorial for the JPEG 2000 standard. The main application areas and their requirements are given. The architecture of the standard follows with the description of the tiling, multicomponent transformations, wavelet transforms, quantization and entropy coding. Some of the most significant features of the standard are presented, such as region-of-interest coding, scalability, visual weighting, error resilience and file format aspects. Finally, some comparative results are reported and the future parts of the standard are discussed.