nils-herrmann/clean_scirepeval huggingface dataset:
Non-obscuring binocular eye tracking for wide field-of-view head-mounted-displays

We present a complete hardware and software solution for integrating binocular eye tracking into current state-of-the-art lens-based Head-mounted Displays (HMDs) without affecting the user's wide field-of-view off the display. The system uses robust and efficient new algorithms for calibration and pupil tracking and allows realtime eye tracking and gaze estimation. Estimating the relative gaze direction of the user opens the door to a much wider spectrum of virtual reality applications and games when using HMDs. We show a 3d-printed prototype of a low-cost HMD with eye tracking that is simple to fabricate and discuss a variety of VR applications utilizing gaze estimation.
A Nested Hierarchy of Localized Scatterplots

The simplicity and visual clarity of scatterplots makes them one of the most widely-used visualization techniques for multivariate data. In complex data sets the important information can be hidden in subsets of the data, often obscured in the typical projections of the whole dataset. This paper presents a new interactive method to explore spatially distinct subsets of a dataset within a given projection. Precisely, we introduce a hierarchy of localized scatterplots as a novel visualization technique that allows to create scatterplots within scatterplots. The resulting visualization bears additional information that would otherwise be hidden within the data. To aid the useful interactive creation of such a hierarchy of localized scatterplots by a user we display transitions between scatterplots as animated rotations in 3D. We show the applicability of our visualization and exploration technique or different tasks, including cluster detection, classification, and comparative analyses. Additionally, we introduce a new exploration tool which we call the cross-dimensional semantic lens. Our hierarchy of localized scatterplots preserves the visual clarity and simplicity of scatterplots while providing additional and easily interpretable information about local subsets of the data.
M/sup 3/: marker-free model reconstruction and motion tracking from 3D voxel data

In computer animation, human motion capture from video is a widely used technique to acquire motion parameters. The acquisition process typically requires an intrusion into the scene in the form of optical markers which are used to estimate the parameters of motion as well as the kinematic structure of the performer. Marker-free optical motion capture approaches exist, but due to their dependence on a specific type of a priori model they can hardly be used to track other subjects, e.g. animals. To bridge the gap between the generality of marker-based methods and the applicability of marker-free methods, we present a flexible non-intrusive approach that estimates both, a kinematic model and its parameters of motion from a sequence of voxel-volumes. The volume sequences are reconstructed from multi-view video data by means of a shape-from-silhouette technique. The described method is well-suited for but not limited to motion capture of human subjects.
Correspondence and Depth-Image Based Rendering a Hybrid Approach for Free-Viewpoint Video

We present a novel approach to free-viewpoint video. Our main contribution is the formulation of a hybrid approach between image morphing and depth-image based rendering. When rendering the scene from novel viewpoints, we use both dense pixel correspondences between image pairs as well as an underlying, view-dependent geometrical model. Our novel reconstruction scheme iteratively refines geometric and correspondence information. By combining the strengths of both depth and correspondence estimation, our approach enables free-viewpoint video also for challenging scenes as well as for recordings that may violate typical constraints in multiview reconstruction. For example, our method is robust against inaccurate camera calibration, asynchronous capture, and imprecise depth reconstruction. Rendering results for different scenes and applications demonstrate the versatility and robustness of our approach.
Combining 2D feature tracking and volume reconstruction for online video-based human motion capture

The acquisition of human motion data is of major importance for creating interactive virtual environments, intelligent user interfaces, and realistic computer animations. Today's performance of off-the-shelf computer hardware enables marker-free non-intrusive optical tracking of the human body. In addition, recent research shows that it is possible to efficiently acquire and render volumetric scene representations in real-time. This paper describes a system to capture human motion at interactive frame rates without the use of markers or scene-intruding devices. Instead, 2D computer vision and 3D volumetric scene reconstruction algorithms are applied directly to the image data. A person is recorded by multiple synchronized cameras, and a multilayer hierarchical kinematic skeleton is fitted to each frame in a two-stage process. We present results with a prototype system running on two PCs.
A toolchain for capturing and rendering stereo and multi-view datasets

We present our toolchain of free-viewpoint video and dynamic scene reconstruction for video and stereoscopic content creation. Our tools take video data from set of sparse unsynchronized cameras and give great freedom during the post production. From input data we can either generate new viewpoints employing the Virtual Video Camera a purely image based system or generate 3D scene models. The approaches are explained and guidelines for weighing their specific advantages and disadvantages in respect to a concrete application are given.
Assessing the quality of compressed images using EEG

The way images are perceived by a human observer is becoming increasingly important in visual media, e.g. for (photo-) realistic image synthesis or memory-efficient encoding of large volumes of image/video data. Traditionally, perceived image quality is assessed by either using specially developed image quality metrics or by conducting user studies. In this paper, we investigate the use of electroencephalography as a tool for evaluating image quality. We demonstrate that the presence of artifacts reliably elicits a measurable response in the brain. We furthermore show that the reaction varies depending on the severity of artifacts, so that it may be used in order to objectively quantify image quality.
Model-aided coding of multi-viewpoint image data

The paper presents a new coding technique for images taken from arbitrary recording positions around a static scene, based on the reconstructed object geometry. Such data structures occur in image-based rendering applications where many hundreds to thousands of images need to be stored and transmitted. Approximate scene geometry enables disparity compensation as well as occlusion detection, leading to improved image prediction. Images are coded in hierarchical order to ensure efficient exploitation of inter-image similarities. The 3-D geometry model allows rendering new views by warping recorded images. The presented algorithm is validated using real-world image data sets, achieving better than 1000:1 compression at acceptable reconstruction quality.
High Resolution Image Correspondences for Video Post-Production

We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression can cope with high-resolution data. The incorporation of SIFT features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically in paint these regions.
Sensitivity of image-based and texture-based multi-view coding to model accuracy

Multi-view image coding benefits from knowledge of the depicted scene's 3D geometry. To exploit geometry information for compression, two different approaches can be distinguished. In texture-based coding, images are converted to texture maps prior to compression. In image-based predictive coding, geometry is used for disparity compensation and occlusion detection between images. Coding performance of both approaches depends on the accuracy of the available geometry model. Texture-based and image-based coding are compared with regard to the influence of geometry accuracy on coding efficiency. The results are theoretically explained. Experiments with natural as well as synthetic image sets show that texture-based coding is more sensitive to small geometry inaccuracies than image-based coding. For approximate geometry models, image-based coding performs best, while texture-based coding yields superior coding results if scene geometry is exactly known.
Multi-video compression in texture space using 4D SPIHT

We present a model-based approach to encode multiple synchronized video streams which show a dynamic scene from different viewpoints. By utilizing 3D scene geometry, we compensate for motion and disparity by transforming all video images to object textures prior to compression. A 4D SPIHT wavelet compression algorithm exploits interframe coherence in both temporal and spatial dimension. Unused texels increase the compression, and a shape mask can be omitted at the cost of higher decoder complexity. The proposed coding scheme is intended for use in conjunction with free-viewpoint video and 3D-TV applications.
Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes

Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator-combined with specialized responsive visualizations-allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example.
Joint 3D-reconstruction and background separation in multiple views using graph cuts

This paper deals with simultaneous depth map estimation and background separation in a multi-view setting with several fixed calibrated cameras, two problems which have previously been addressed separately. We demonstrate that their strong interdependency can be exploited elegantly by minimizing a discrete energy functional, which evaluates both properties at the same time. Our algorithm is derived from the powerful "multi-camera scene reconstruction via graph cuts" algorithm presented by Kolmogorov and Zabih (2002). Experiments with both real-world as well as synthetic scenes demonstrate that the presented combined approach yields even more correct depth estimates. In particular, the additional information gained by taking background into account increases considerably the algorithm's robustness against noise.
Motion field and occlusion time estimation via alternate exposure flow

This paper presents an extension to optical flow-based motion estimation using alternating short- and long-exposed images. While traditional optical flow algorithms rely on consecutive short-exposed images only, long-exposed images capture motion directly in the form of motion blur. This additional information can be used to achieve more robust and accurate motion field estimation as well as to extract the moment of occlusion. We introduce an image formation model that relates the long-exposed image to its preceding and succeeding short-exposed images in terms of dense 2D motion and per-pixel occlusion/disocclusion timings. Based on this image formation model, we describe a practical algorithm to estimate the motion field not only for completely visible image regions but also for pixels becoming occluded. For these pixels the Alternate Exposure Flow (AEF) also determines the moment of occlusion. We describe the application of AEF in frame interpolation to demonstrate the advantage of the additional long exposure information
Connectionist model binarization

The application of a connectionist model to an image binarization method called connectionist model binarization (CMB) is discussed. CMB employs a multilayer network of a connectionist model whose input and output are a histogram and a desirable threshold for binarization, respectively. This network is trained with a back-propagation algorithm to output a threshold which gives a visually suitable binarised image against any histogram. The details of CMB are described, and its learning strategy and binarization performance are discussed.<<ETX>>
People counting across spatially disjoint cameras by flow estimation between foreground regions

Our goal is to develop a method for counting the number of people traveling over a wide area monitored by spatially disjoint multiple cameras with non-overlapping fields of view. The proposed method counts the number of people traversing across each pair of cameras' fields of view by estimating the flows between the foreground regions which have disappeared from and appeared in the camera views within a short time interval. The approach aims at resolving two problems: people in a foreground region can split and merge outside the cameras' fields of view, and the appearance variance of the same person and persons with similar appearance can lead to errors in person re-identification across cameras. The average errors of 0.184 persons in counting the number of people traveling over an area in a university campus monitored by four virtual cameras for 100 minutes have demonstrated the effectiveness of the proposed method.
Video Summarization for Large Sports Video Archives

Video summarization is defined as creating a shorter video clip or a video poster which includes only the important scenes in the original video streams. In this paper, we propose two methods of generating a summary of arbitrary length for large sports video archives. One is to create a concise video clip by temporally compressing the amount of the video data. The other is to provide a video poster by spatially presenting the image keyframes which together represent the whole video content. Our methods deal with the metadata which has semantic descriptions of video content. Summaries are created according to the significance of each video segment which is normalized in order to handle large sports video archives. We experimentally verified the effectiveness of our methods by comparing the results with man-made video summaries
Camera oscillation pattern for VSLAM: Translational versus rotational

Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of the environment landmarks. SLAM algorithm has two interrelated processes localization and mapping. For accurate localization, we need the features location estimates to converge quickly. On the other hand, to build an accurate map, we need accurate localization. Recently, a biologically inspired approach exploits deliberate camera oscillation has been used to improve the convergence speed of depth estimate. In this paper, we explore the effect of camera oscillation pattern on the accuracy of VSLAM. Two main oscillation patterns are used for distance estimation: translational and rotational. Experiments, using static and moving robot, are made to explore the effect of these oscillation patterns on the VSLAM performance.
Collaborative multimedia analysis for detecting semantical events from broadcasted sports video

In this paper we present an approach towards detecting semantical events from broadcasted sports video through collaborative multimedia analysis, called intermodal collaboration. Broadcasted video can be viewed as a set of multimodal streams such as visual, auditory, and textual (closed caption: CC) streams. By considering temporal dependency between their streams, we aim to improve the reliability and efficiency for event detection. This method consists of three procedural stages: CC stream analysis, auditory stream analysis, and visual stream analysis. In this method, we learn both frequently appearing keywords related to the event from the CC stream and feature parameters characterizing cheering and shouting from the auditory stream. The experimental results for broadcasted sports video of American football games indicate that our approach is effective for event detection.
Automatic generation of privacy-protected videos using background estimation

Recently, video sharing services such as YouTube and Daily-motion have become popular and many videos taken with mobile video cameras are uploaded to such a video sharing service. However, such videos can infringe on the privacy right of people in the videos because they may contain privacy sensitive information (PSI) of the people, i.e., their appearances. This strongly motivates us to develop a technique to generate privacy-protected videos. In this paper, we propose a novel system for automatic generation of privacy-protected videos based on background estimation. In most conventional techniques, objects that contain PSI are detected and obscured by, e.g., blurring. Conversely, in our system, background pixels are estimated and then substituted with intended human objects that are essential for the camera person's capture intention. We quantitatively evaluate our system to demonstrate its potential applicability.
Run length based steganalysis for LSB matching steganography

In this paper, we propose a steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. We use the fusion of histogram of run length and histogram characteristic function to detect the LSB matching. Experimental results on two datasets demonstrate that this method has superior results compared with other recently proposed algorithms, and shows that the proposed method is efficient to detect the LSB matching steganography on compressed or uncompressed images.
Real-World Observation Extraction from Microblog Based on Word Associative Relations

A large number of real-world observations by social sensors all over the world can be obtained from various social networking services. Especially, observations covering miscellaneous areas of interest are posted to Twitter as short text messages. Our goal is to extract a wide range of observations related to the target of interest specified by the user from Twitter regardless of their popularity. Assuming that the related observations are likely to contain words people often associate with each other, the associative relations among words are learned from the past messages. When a user gives a keyword representing his/her current interest, recent related observations are extracted based on their word frequency distributions in terms of the associative relations to the keyword.
Event based indexing of broadcasted sports video by intermodal collaboration

In this paper, we propose event-based video indexing, which is a kind of indexing by its semantical contents. Because video data is composed of multimodal information streams such as visual, auditory, and textual [closed caption (CC)] streams, we introduce a strategy of intermodal collaboration, i.e., collaborative processing taking account of the semantical dependency between these streams. Its aim is to improve the reliability and efficiency in contents analysis of video. Focusing here on temporal correspondence between visual and CC streams, the proposed method attempts to seek for time spans in which events are likely to take place through extraction of keywords from the CC stream and then to index shots in the visual stream. The experimental results for broadcasted sports video of American football games indicate that intermodal collaboration is effective for video indexing by the events such as touchdown (TD) and field goal (FG).
Privacy protecting visual processing for secure video surveillance

Privacy protection is important in video surveillance. In this paper, we address privacy protection related issues. First, based on questionnaire-based experiments we analyze personal sense of privacy from the viewpoint of the relationship between a viewer and a subject. With the analysis results, we introduce a privacy protected video surveillance system named PriSurv, which can adaptively protect subjects' privacy according to their privacy policy against each viewer. Then, we propose two methods of protecting individuals' privacy by controlling the disclosure of subjects' visual information. One uses a set of visual abstraction operators such as silhouette and dot, which gradually control subjects' visual information. The other uses an active appearance model (AAM) based masks which encode privacy information in the original face region. The latter method can be used especially when a subject's expression can be seen in the video and is characterized by the recoverability of the encoded privacy information.
Modeling visual information by spatio-temporal patterns to analyze event tactic in sports video

Although event detection in sports videos has been gaining some remarkable results, tools for exploring and exploiting these event clips on their own demands are far from user's expectation. In this paper, a novel method using spatio-temporal patterns to model visual information tailored to analyze event tactics in sports videos without tracking objects is introduced. The major contribution of the proposed method is to represent visual information by using a sequence of symbols. Each symbol represents a distribution of players in a certain period of time. By using these sequences, event tactics are detected, explained, and visualized to help users understand event tactics without watching whole clips. Moreover, users could query by an example or by a text to find all events sharing the same tactic. Thorough testing with over 100 goal clips in soccer domain demonstrates the superiority of the proposed method in terms of precision recall ratios.
Stability analysis and H/sub /spl infin// norm computation of 2D discrete systems described by Fornasini-Marchesini second model

The paper presents a stability criterion and a method for computing the H/sub /spl infin// norm of 2D discrete systems. Both methods are based on linear matrix inequalities (LMI), and, hence, they are computationally tractable. In deriving these methods, the finite-order Fourier series approximation of the solution for frequency-dependent LMI (FDLMI), and the properties of the quadratic form representation of finite-order Fourier series play key roles. From the point of view of the proposed methods, existing LMI-based methods can be regarded as ones which are obtained by the Fourier series approximation of order zero, and thus, it is expected that the proposed methods lead to less conservative results. This is illustrated by numerical examples. The effectiveness of the proposed methods is also illustrated by numerical examples.