nils-herrmann/clean_scirepeval huggingface dataset:
Drive Strategies For Switched Reluctance Motor - A Review

A typical switched reluctance motor (SRM) drive system is made up of three basic components: power electronic converter, control logic circuit and the switched reluctance motor. The principle of operation of a SRM motor is quite simple: as current passes through one of its stator windings, torque is produced by the tendency of its rotor to align with the excited stator pole. The direction of the torque generated is a function of rotor position with respect to the phase that is energized, and is independent of direction of flow of current in that specific winding. The torque production can be made continuous by synchronizing the excitation of each phase winding with rotor position. The amount of current passing through the SRM winding is controlled by suitably making the power electronic switches on and off, which can connect each SRM phase to the DC bus. The drive strategy of a SRM motor predominantly consists of the power electronic drive topology because it largely dictates how the motor can be used. This paper presents different drive techniques for a SRM motor collected from researches of a number of technologists.
Compressed sensing for aperture synthesis imaging

The theory of compressed sensing has a natural application in interferometric aperture synthesis. As in many real-world applications, however, the assumption of random sampling, which is elementary to many propositions of this theory, is not met. Instead, the induced sampling patterns exhibit a large degree of regularity. In this paper, we statistically quantify the effects of this kind of regularity for the problem of radio interferometry where astronomical images are sparsely sampled in the frequency domain. Based on the favorable results of our statistical evaluation, we present a practical method for interferometric image reconstruction that is evaluated on observational data from the Very Large Array (VLA) telescope.
Consistent optical flow for stereo video

Video editing plays an important role in today's cinematic post-production: editing operations are typically applied on a keyframe basis and propagated automatically to the rest of the sequence. Thereby, small inconsistencies in correspondences used for the propagation of editing operations accumulate and need to be corrected manually. The amount of required manual interaction increases further when editing stereoscopic video sequences. In this paper, we propose an algorithm for looped correspondence estimation yielding consistent correspondences that can be used for reliable propagation of editing operations along a stereoscopic video sequence, avoiding drift and error accumulation common to standard approaches. Taking an additional spatially adjacent image into account, we extend standard two-image optical flow algorithms and exploit data redundancy within stereoscopic video sequences. Our proposed algorithm not only yields robust and consistent correspondences for stereo footage, but also improves the accuracy of standard two-image motion estimation as demonstrated for several test scenes with known ground truth.
Keyframe Animation from Video

This paper proposes a method for analyzing and synthesizing video sequences, specifically suited for image sequences of natural phenomena. We combine a low-dimensional representation of arbitrary image sequences and an image morphing technique to create realistic in-between images. The visualization based on the Isomap algorithm allows users to easily select parts of the video that have periodic character. From these segments, new sequences can be synthesized in realtime. To smooth transition artifacts between the reordered subsequences a Monge-Kantorovich based image morphing method is applied to interpolate in-between images. Our approach is useful for e.g. video key-frame animation, automatic looping or creating slow-motion sequences.
Multi-view coding for image-based rendering using 3-D scene geometry

To store and transmit the large amount of image data necessary for Image-based Rendering (IBR), efficient coding schemes are required. This paper presents two different approaches which exploit three-dimensional scene geometry for multi-view compression. In texture-based coding, images are converted to view-dependent texture maps for compression. In model-aided predictive coding, scene geometry is used for disparity compensation and occlusion detection between images. While both coding strategies are able to attain compression ratios exceeding 2000:1, individual coding performance is found to depend on the accuracy of the available geometry model. Experiments with real-world as well as synthetic image sets show that texture-based coding is more sensitive to geometry inaccuracies than predictive coding. A rate-distortion theoretical analysis of both schemes supports these findings. For reconstructed approximate geometry models, model-aided predictive coding performs best, while texture-based coding yields superior coding results if scene geometry is exactly known.
Spacetime-continuous geometry meshes from multi-view video sequences

We reconstruct geometry for a time-varying scene given by a number of video sequences. The dynamic geometry is represented by a 3D hypersurface embedded in space-time. The intersection of the hypersurface with planes of constant time then yields the geometry at a single time instant. In this paper, we model the hypersurface with a collection of triangle meshes, one for each time frame. The photo-consistency error is measured by an error functional defined as an integral over the hypersurface. It can be minimized using a PDE driven surface evolution, which simultaneously optimizes space-time continuity as well. Compared to our previous implementation based on level sets, triangle meshes yield more accurate results, while requiring less memory and computation time. Meshes are also directly compatible with triangle-based rendering algorithms, so no additional post-processing is required.
Two approaches to incorporate approximate geometry into multi-view image coding

Two compression schemes for coding multiple images of a static scene are compared. Both codecs apply approximate 3-D scene geometry: in model-aided coding, images are predicted by model-driven disparity compensation, while the model-based coder uses the geometry to convert images into texture maps for compression. Images of real-world objects are used to evaluate both codecs. The coding performance is examined subject to varying geometry accuracy. Compression factors of up to 1000:1 at an acceptable reconstruction quality verify the usefulness of the 3-D geometry for coding multi-viewpoint imagery.
Gaze-Contingent Computational Displays: Boosting perceptual fidelity

Contemporary digital displays feature multimillion pixels at ever-increasing refresh rates. Reality, on the other hand, provides us with a view of the world that is continuous in space and in time. The discrepancy between viewing the physical world and its sampled depiction on digital displays gives rise to perceptual quality degradation. By measuring or estimating where we look, a new breed of gaze-contingent algorithms aims to exploit the way we visually perceive digital images and videos to remedy visible artifacts. In this article, we provide an overview of recent developments in computational display algorithms that enhance perceived visual quality of conventional video footage when viewed on commodity monitors, projectors, or headmounted displays (HMDs).
Weighted Minimal Hypersurface Reconstruction

Many problems in computer vision can be formulated as a minimization problem for an energy functional. If this functional is given as an integral of a scalar-valued weight function over an unknown hypersurface, then the sought-after minimal surface can be determined as a solution of the functional's Euler-Lagrange equation. This paper deals with a general class of weight functions that may depend on surface point coordinates as well as surface orientation. We derive the Euler-Lagrange equation in arbitrary dimensional space without the need for any surface parameterization, generalizing existing proofs. Our work opens up the possibility of solving problems involving minimal hypersurfaces in a dimension higher than three, which were previously impossible to solve in practice. We also introduce two applications of our new framework: We show how to reconstruct temporally coherent geometry from multiple video streams, and we use the same framework for the volumetric reconstruction of refractive and transparent natural phenomena, bodies of flowing water.
Making of Who Cares? HD Stereoscopic Free Viewpoint Video

We present a detailed blueprint of our stereoscopic freeviewpoint video system. Using unsynchronized footage as input, we can render virtual camera paths in the post-production stage. The movement of the virtual camera also extends to the temporal domain, so that slow-motion and freeze-and-rotate shots are possible. As a proof-of-concept, a full length stereoscopic HD music video has been produced using our approach.
Data compression for light-field rendering

Two light-field compression schemes are presented. The codecs are compared with regard to compression efficiency and rendering performance. The first proposed coder is based on video-compression techniques that have been modified to code the four-dimensional light-field data structure efficiently. The second coder relies entirely on disparity-compensated image prediction, establishing a hierarchical structure among the light-field images. The coding performance of both schemes is evaluated using publicly available light fields of synthetic, as well as real-world, scenes. Compression ratios vary between 100:1 and 2000:1, depending on the reconstruction quality and light-field scene characteristics.
Constrained example-based audio synthesis

Music and sound play an important role for the emotional involvement of the spectator in visual media such as movies, video games or artistic shows. However, the generation of suitable background music and atmospheric sound matching the visual context is a tedious and typically completely manual task. For content creators, it would be desirable to be able to specify a sound example and a few key points at which certain parts are to be played, and to then automatically generate a soundtrack subject to these constraints. For interactive media such as video games, on the other hand, example-based background sound could automatically adapt to the game events. We propose a new example-based audio synthesis method for both applications which works for musical and texture-like sound examples alike. Our algorithm is evaluated on a variety of musical styles, from classical music to punk rock, and on several texture-like atmospheric sounds.
Constrained inverse volume rendering for planetary nebulae

Determining the three-dimensional structure of distant astronomical objects is a challenging task, given that terrestrial observations provide only one viewpoint. For this task, bipolar planetary nebulae are interesting objects of study because of their pronounced axial symmetry due to fundamental physical processes. Making use of this symmetry constraint, we present a technique to automatically recover the axisymmetric structure of bipolar planetary nebulae from two-dimensional images. With GPU-based volume rendering driving a nonlinear optimization, we estimate the nebula's local emission density as a function of its radial and axial coordinates, and we recover the orientation of the nebula relative to Earth. The optimization refines the nebula model and its orientation by minimizing the differences between the rendered image and the original astronomical image. The resulting model enables realistic 3D visualizations of planetary nebulae, e.g. for educational purposes in planetarium shows. In addition, the recovered spatial distribution of the emissive gas allows validating computer simulation results of the astrophysical formation processes of planetary nebulae.
Sensitivity of reflected radiance to surface normal orientation

Given the illumination and the surface normal at a point on a convex surface, the effect of small changes in surface normal direction on observed reflected radiance is examined. A signal-processing framework suitable for the hemispherical geometry of the problem is applied to discuss surface reflection in terms of convolving incident illumination with the reflectance function. Depending on the surface material's reflection properties as well as on the incident illumination distribution, normal direction deviation may or may not be noticeable. Expressions are derived to quantitatively evaluate the amount of change in reflected radiance. Different scenarios exemplify the results. The presented theoretical results have implications for shape-from-shading techniques as well as for scene-based compression methods.
Hierarchical coding of light fields with disparity maps

A coder for light fields is presented. Due to the large amount of data needed to represent a complete light field, a hierarchical decomposition is employed. The full light field is built up by recursively predicting intermediate images from a small subset of light-field images. Intermediate images are predicted by disparity-compensating multiple surrounding images. The predicted images are refined using DCT coding. Rate-distortion measurements for two standard light fields verify the efficiency of the proposed scheme. Compression ratios of 1000:1 are achieved at acceptable quality of the rendered views.
Combining stereo and visual hull information for on-line reconstruction and rendering of dynamic scenes

In this paper, we present a novel system which, combines depth-from-stereo and visual hull reconstruction for acquiring dynamic real-world scenes at interactive rates. First, we use the silhouettes from multiple views to construct a polyhedral visual hull is then used to limit the disparity range during depth-from-stereo computation. The restricted search range improves both speed and quality of the stereo reconstruction. In return, stereo information can compensate for some of the visual hull method, such as inability to reconstruct surface details and concave regions. Our system achieves a reconstruction frame rate of 4fps.
An Approach Toward Fast Gradient-Based Image Segmentation

In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford-Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex-concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images .
Multivideo compression in texture space

We present a model-based approach to encode multiple synchronized video streams depicting a dynamic scene from different viewpoints. With approximate 3D scene geometry available, we compensate for motion as well as disparity by transforming all video images to object textures prior to compression. A two-level hierarchical coding strategy is employed to efficiently exploit inter-texture coherence as well as to ensure quick random access during decoding. Experimental validation shows that attainable compression ratios range up to 50:1 without subsampling. The proposed coding scheme is intended for use in conjunction with free-viewpoint video and 3D-TV applications.
Color Mathematical Morphology Based on Partial Ordering of Spectra

Mathematical morphology is based on the principle of ordering. There is no natural way to order colors (being triplets of scalars). A lot of different ordering relations have been proposed in the literature, most on an ad-hoc basis. In this paper, we propose an ordering relation for colors that is based on the natural (i.e. physically plausible) ordering of spectra. Therefore, we ensure that the ordering is independent of the chosen color parameterization. We discuss that part of colorimetric theory that enables us to reconstruct the spectrum given the three color parameters. Furthermore, we present the very basics of the algebraic framework of mathematical morphology. This allows us to embed the presented ordering of colors within the morphological framework such that we can fully exploit the possibilities to define morphological image operators working on color images.
Capturing the shape of a dynamic world - fast!

Acquiring online the evolving shape of a dynamic scene from a handful of video streams may be considered one of the most challenging, but at the same time also most auspicious tasks in contemporary computer graphics and computer vision research. The anticipation of revolutionary new applications such as interactive 3D television broadcasts motivates the ongoing work on free-viewpoint video rendering. The paper aims at giving a state-of-progress report on this lively research endeavor. Different acquisition setups and online reconstruction approaches are exemplified. Yielding interactive frame rates, depth map-based techniques, polyhedral as well as volumetric visual hull reconstruction approaches, and combined methods employing visual hull-guided depth map estimation are presented. The experience gained with these approaches allows us to identify future research directions towards real-time analysis and high-quality synthesis of dynamic, real-world scenes.
Flowlab - An Interactive Tool for Editing Dense Image Correspondences

Finding dense correspondences between two images is a well-researched but still unsolved problem. For various tasks in computer graphics, e.g. image interpolation, obtaining plausible correspondences is a vital component. We present an interactive tool that allows the user to modify and correct dense correspondence maps between two given images. Incorporating state-of-the art algorithms in image segmentation, correspondence estimation and optical flow, our tool assists the user in selecting and correcting mismatched correspondences.
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data

Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.
Perception-based visual quality measures

In recent years diverse quality measures to support the exploration of high-dimensional data sets have been proposed. Such measures can be very useful to rank and select information-bearing projections of very high dimensional data, when the visual exploration of all possible projections becomes unfeasible. But even though a ranking of the low dimensional projections may support the user in the visual exploration task, different measures deliver different distances between the views that do not necessarily match the expectations of human perception. As an alternative solution, we propose a perception-based approach that, similar to the existing measures, can be used to select information bearing projections of the data. Specifically, we construct a perceptual embedding for the different projections based on the data from a psychophysics study and multi-dimensional scaling. This embedding together with a ranking function is then used to estimate the value of the projections for a specific user task in a perceptual sense.
Optimizing Apparent Display Resolution Enhancement for Arbitrary Videos

Display resolution is frequently exceeded by available image resolution. Recently, apparent display resolution enhancement (ADRE) techniques show how characteristics of the human visual system can be exploited to provide super-resolution on high refresh rate displays. In this paper, we address the problem of generalizing the ADRE technique to conventional videos of arbitrary content. We propose an optimization-based approach to continuously translate the video frames in such a way that the added motion enables apparent resolution enhancement for the salient image region. The optimization considers the optimal velocity, smoothness, and similarity to compute an appropriate trajectory. In addition, we provide an intuitive user interface that allows to guide the algorithm interactively and preserves important compositions within the video. We present a user study evaluating apparent rendering quality and show versatility of our method on a variety of general test scenes.
Reconstruction and visualization of planetary nebulae

From our terrestrially confined viewpoint, the actual three-dimensional shape of distant astronomical objects is, in general, very challenging to determine. For one class of astronomical objects, however, spatial structure can be recovered from conventional 2D images alone. So-called planetary nebulae (PNe) exhibit pronounced symmetry characteristics that come about due to fundamental physical processes. Making use of this symmetry constraint, we present a technique to automatically recover the axisymmetric structure of many planetary nebulae from photographs. With GPU-based volume rendering driving a nonlinear optimization, we estimate the nebula's local emission density as a function of its radial and axial coordinates and we recover the orientation of the nebula relative to Earth. The optimization refines the nebula model and its orientation by minimizing the differences between the rendered image and the original astronomical image. The resulting model allows creating realistic 3D visualizations of these nebulae, for example, for planetarium shows and other educational purposes. In addition, the recovered spatial distribution of the emissive gas can help astrophysicists gain deeper insight into the formation processes of planetary nebulae.
A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing

An algorithmic framework to compute sparse or minimal-TV solutions of linear systems is proposed. The framework includes both the Kaczmarz method and the linearized Bregman method as special cases and also several new methods such as a sparse Kaczmarz solver. The algorithmic framework has a variety of applications and is especially useful for problems in which the linear measurements are slow and expensive to obtain. We present examples for online compressed sensing, TV tomographic reconstruction and radio interferometry.