nils-herrmann/clean_scirepeval huggingface dataset:
Computationally efficient fixed-length entropy codec for robust video compression

Fixed length entropy codes (FLC) have been previously introduced as an alternative to variable length entropy codes (VLC) in video compression applications. Video codecs employing FLC entropy codes have been shown to outperform the ones using VLC codes in noisy environments. Unfortunately, no computationally efficient implementation has been proposed for FLC codes since they had not received as much attention from the source encoding community as VLC codes had. We introduce data structures and algorithms to implement FLC coders and decoders that make feasible the use of FLC codes in a multitude of applications including real time video codecs. The proposed implementation makes the complexity of FLC and VLC codes comparable.
DCT quantization noise in compressed images

In lossy image compression schemes utilizing the discrete cosine transform (DCT), quantization of the DCT coefficients introduces error in the image representation and a loss of signal information. At high compression ratios, this introduced error produces visually undesirable compression artifacts that can dramatically lower the perceived quality of a particular image. This paper provides a spatial domain model of the quantization error based on a statistical noise model of the error introduced when quantizing the DCT coefficients. The resulting theoretically derived spatial domain quantization noise model shows that, in general, the compression noise in the spatial domain is both correlated and spatially varying. This provides some justification for many of the ad hoc artifact removal filters that have been proposed. An accurate description of quantization noise is essential if one hopes to remove, or at least alleviate, the visibility of compression artifacts.
Ocean science concept mapping: what do people need to know about ocean sciences

While attempting to infuse more ocean science research into K-16 classrooms, museums and aquaria, the New England Regional Center for Ocean Sciences Education Excellence (NER-COSEE) is focused on identifying excellent examples of ocean science education. However, without a "map" of ocean sciences, it is difficult or impossible to meet the needs of both the research and education communities. Therefore, we are using a combination of surveys, small working groups, concept mapping strategies, and collaboration across the ocean science community to produce a simplified web of age appropriate ocean science concepts along with excellent examples of how to teach these concepts.
Viewpoint Invariant Recovery of Visual Surfaces from Sparse Data

An algorithm for the reconstruction of visual surfaces from sparse data is proposed. An important aspect of this algorithm is that the surface estimated from the sparse data is approximately invariant with respect to rigid transformation of the surface in 3D space. The algorithm is based on casting the problem as an ill-posed inverse problem that must be stabilized using a priori information related to the image and constraint formation. To form a surface estimate that is approximately invariant with respect to viewpoint, the stabilizing information is based on invariant surface characteristics. With appropriate approximations, this results in a convex functional to minimize, which is then solved using finite element analysis. The relationship of this algorithm to several previously proposed reconstruction algorithms is discussed, and several examples that demonstrate its effectiveness in reconstructing viewpoint-invariant surface estimates are given.
Maximum likelihood parameter estimation for non-Gaussian prior signal models

For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. These models are generally dependent upon two parameters, one controlling the size of reconstructed discontinuities, and the other controlling data smoothing. The authors propose a maximum likelihood technique for automatically estimating these parameters, resulting in the optimization of an expression dependent upon the prior model partition function. An exact expression is derived for the 1D signal model partition function, while an approximation is proposed for the 2D image model partition function. Parameters estimated from degraded signals result in high quality restorations.<<ETX>>
Optical flow estimation with p-harmonic regularization

In this paper, an alternative efficient energy functional is proposed for estimating optical flow. The problem of optical flow estimation is posed as one of solving for the minimum configurations of its two components in an L1 total variation framework augmented with a p-harmonic energy functional. A novel smooth term based on a p-harmonic map energy is added as a regularization constraint in the optical flow computation. The p-harmonic smooth term deals with the regularization of the angle of directional data, while preserving the magnitude. The proposed algorithm is based on separating the optical flow vector into its magnitude and angle, and then processing each one of these components iteratively, in a closed-loop manner. Experiments show that the proposed algorithm outperforms a recently published, top performing L1 total variation method, which shows the capability of the new regularization term in improving the optical flow estimation performance.
Improved definition image expansion

A method for nonlinear image expansion is introduced. The method preserves the discontinuities of the original image, therefore producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation technique that is proposed results in the constrained optimization of a convex functional. The expanded image produced from this method will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion.<<ETX>>
A Bayesian approach to image expansion for improved definitio

Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion.
Mechanisms for Data Quality and Validation in Citizen Science

Data quality is a primary concern for researchers employing a public participation in scientific research (PPSR) or ``citizen science'' approach. This mode of scientific collaboration relies on contributions from a large, often unknown population of volunteers with variable expertise. In a survey of PPSR projects, we found that most projects employ multiple mechanisms to ensure data quality and appropriate levels of validation. We created a framework of 18 mechanisms commonly employed by PPSR projects for ensuring data quality, based on direct experience of the authors and a review of the survey data, noting two categories of sources of error (protocols, participants) and three potential intervention points (before, during and after participation), which can be used to guide project design.
Motion deblurring and depth estimation from multiple images

Scene depth variation is an important factor that leads to spatially-varying camera motion blur. Most of the previous methods require auxiliary cameras or user interaction to make depth-aware deblurring tractable. In this work, we propose to use a noisy/blurred/noisy image sequence and simultaneously recorded inertial measurements to jointly estimate scene depth and remove spatially-varying blur caused by depth variation and camera in-plane motion. The inertial data could provide initialization of camera motion parameters, while the noisy image pair preserve large-scale sharp edges from which a coarse disparity map can be generated. However, this initial estimate is not accurate enough to produce a high-quality clean image. Therefore, we develop an optimization scheme to refine depth, motion parameters and latent image alternately. A Markov Random Field (MRF) framework is formulated to solve for the depth map by exploiting both stereo cues and motion blur cues and the residual Richardson-Lucy algorithm is used to effectively suppress deconvolution ringing artifacts. Experimental results demonstrate that our approach can address both depth estimation as well as image deblurring.
Efficient error recovery for multiple description video coding

Multiple description coding (MDC) and multihypothesis motion-compensated prediction (MHMCP) are two promising error resilient video coding techniques. We propose an enhanced error resilient video coding scheme based on multiple state video coding (MSVC). It uses multihypothesis motion prediction in addition to normal motion-compensated video compression steps. Yielded additional motion vectors between frames of two descriptions can help efficiently and quickly estimate lost content in one description when the other one is received correctly. Negligible redundancy is added to originally coded MSVC bitstreams while complicated motion search is avoided at decoder when there is a need to estimate lost content. Simulation results show that the proposed scheme has better error recovery performance than previously used approaches in most situations, especially when multiple and complex motions appear.
Studentized Dynamical System for Robust Object Tracking

This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model.
An EM algorithm for robust Bayesian PCA with student’s t-distribution

Principal component analysis (PCA) is a technique that is widely used for applications such as dimensionality reduction, image compression, feature extraction and data visualization. One of the key issues in the use of PCA for modelling is that it is very sensitive to outliers since its formulation is based on Gaussian density model. Lately, more heavy-tailed distribution (i.e., Student’s t-distribution) is introduced to increase the robustness of traditional PCA. But the robust version of PCA is expressed as the maximum likelihood solution of a probabilistic latent variable model. This reformulation raises the question of how to determine the optimal number of principal components to be retained. In this paper, we develop a Bayesian model selection approach to estimate the true dimensionality of the data. The proposed algorithm is based on a new Bayesian treatment of robust Student’s t-distribution PCA. A simple Expectation-Maximization (EM) solver is introduced to find approximate solutions for the model. Experiments show that the proposed model achieves simultaneous optimal dimensionality selection and accurate principal components recovery.
Bayesian motion estimation without spatial and temporal gradients

A Bayesian motion estimation technique is proposed which models the motion vector field with a discontinuity-preserving prior and the observation noise corrupting the video frames with a Gaussian density. The method is related to various optical flow techniques, except that it is not dependent on spatial and temporal gradients which are notoriously difficult to estimate from real image sequences. The objective function to be minimized contains a block matching likelihood term and an optical flow prior term, making the technique a hybrid of two popular motion estimation schemes. Simulations show that the proposed technique results in more accurate motion vector fields than those obtained through conventional block matching and Horn-Schunck optical flow estimation.
Robust Bayesian PCA with Student’s t-distribution: The variational inference approach

Principal component analysis (PCA) is a technique that is widely used for applications such as dimensionality reduction, image compression, feature extraction and data visualization. One of the key issues in the use of PCA for modelling is that it is very sensitive to outliers since its formulation is based on Gaussian density model. Lately, more heavy-tailed distribution (i.e., Student's t-distribution) is introduced to increase the robustness of traditional PCA. But the robust version of PCA is expressed as the maximum likelihood solution of a probabilistic latent variable model. This reformulation raises the question of how to determine the optimal number of principal components to be retained. In this paper, we develop a Bayesian model selection approach to estimate the true dimensionality of the data. The proposed algorithm is based on a new Bayesian treatment of robust Student's t-distribution PCA. A simple Expectation-Maximization (EM) solver is introduced to find approximate solutions for the model. Experiments show that the proposed model achieves simultaneous optimal dimensionality selection an.d accurate principal components recovery.
Codeword assignment for fixed-length entropy coded video streams

Fixed length entropy codes (FLC) have been successfully employed as an alternative to the popular variable length codes (VLC) in video codecs. This paper presents a codeword assignment for FLC coded interframe coefficients and motion vectors that minimizes the effects of bit errors on the decoded video sequence. Intensive testing has shown that with the proposed solution, the distortion introduced by transmission errors is coarsely half of that obtained with VLC codes, while maintaining the compression efficiency.
Dual-receiver blind identification for image blurs

Degradation of images by blurs and noise frequently occurs in data transmission. Identification of the blur parameters is crucial to accurate restoration of the degraded image. This paper proposes a deterministic blind blur identification technique that uses two different blurred versions of a single image in the identification process. Through row-ordering, the blurred images are put into a matrix form and then combined using a portion of the matrix having the same size as the blur. It can be shown that through singular value decomposition, the blur coefficients may be uniquely determined, given a matrix null space of unity dimension. Experiments show that identification is also possible with noisy data that has no null space. Application of the process in image restoration confirms the usefulness of the method in actual practice.
Mapping measurable quantities of point-spread function observations to Seidel aberration coefficients

The Seidel aberration model has proven an invaluable tool in the design of optical systems [1, 2], by providing a model that bridges the gap between first-order optics models and actual system performance. However, it has been largely neglected in the modeling of blur kernels, despite the ability of the model to accurately predict the point-spread function (PSF) of an optical system due to aberration and defocus. As a step towards developing a parameterized, spatially-varying PSF model, we propose a novel mapping to Seidel aberration coefficients from observable spread measures of discretely-defined PSFs. We demonstrate through simulation that this mapping, and the associated estimation algorithm, allow a noisy dataset of 120 PSF observations (consisting of thousands of degrees of freedom) to be unified under a single 4-parameter model.
PlayGround 2.0: Simulating behavior decisions with trust and control computations

Trust fosters cooperation in social interactions. It is widely studied in applied and academic disciplines. However, trust is a hard concept to define. Network analysis of the Web of Science citation databases shows it has multiple meanings and applications even within Computer Science. To date general models of trust are essentially conceptual, whereas workable proposals focus on special situations. In an effort to make trust simulations more generalist and applicable, we implemented the PlayGround model (Marsh, 1994) and modified it to incorporate a broader concept of trust and notions of control. Results with new model, that consider competence as a trustworthiness component, and use control to mitigate the risks, were similar to original ones from Marsh's, although the conceptual differences are significant to encompasses the many meanings of trust.
Bidirectional block placement in corrupted fixed-length entropy coded video streams

Fixed length entropy codes (FLC) have been employed as an alternative to the usual variable length codes (VLC) in video codecs. The effect of a transmission error in an FLC coded stream does not propagate beyond the corrupted codeword because of its fixed parsing structure. This paper presents a new decoding algorithm for FLC compressed video streams that fully exploits the error resilience properties of FLC codes combined with spatial synchronization points. While maintaining the compression efficiency of VLC coded video streams, the proposed solution is a novel idea to combat the consequences of transmission errors and it is totally compatible with more traditional solutions such as error concealment and forward error correcting codes.
On the application of turbo codes to the robust transmission of compressed images

Compressed images transmitted over noisy channels are extremely sensitive to bit errors. This necessitates the application of error control channel coding to the compressed representation before transmission. This paper presents an image transmission system which takes advantage of the superior performance of turbo codes, an important new class of parallel concatenated codes. Several aspects of the application of turbo codes to image transmission are studied, including comparison to a previous image transmission system using convolutional codes. Experimental results for several channel signal-to-noise ratios show that, in the same SNR range, turbo codes achieve much better performance with less decoding complexity than convolutional codes and that similar performance can be achieved at much lower channel SNRs. Studies also show that the use of feedback from an outer Reed-Solomon code to aid turbo decoding results in further improvement.
Joint channel and source decoding for vector quantized images using turbo codes

This paper presents a novel joint decoding scheme for image transmission over noisy channels. The proposed decoding scheme includes two features of interaction between a turbo channel decoder and a vector quantization source decoder. First, the source decoder makes use of the channel soft outputs for better reconstruction; second, a feedback algorithm is designed for using source information to improve the error correction capability of the turbo decoder. Significant improvement in terms of the error correction capability and the quality of reconstructed images is achieved compared to a separately operating decoding system.
Design and application of broadband ceramic feedthrough capacitor EMI filters to cardiac pacemakers and implantable defibrillators

This paper describes ceramic feedthrough capacitor electromagnetic interference (EMI) broadband filter design and installation techniques that generally make cardiac pacemakers and implantable cardioverter defibrillators (ICDs) immune to EMI from handheld wireless communication devices (including cellular phones) and other RF emitters. A typical cardiac pacemaker with four platinum lead wires which egress through a titanium housing (can) via a gold brazed alumina hermetic terminal is studied. The titanium can is seam welded to act both as an electromagnetic shield and a hermetic barrier to protect the internal components from body fluids. These lead wires, which normally carry the pacing pulses and sense cardiac activity, can also act as an antenna and conduct undesirable RF carrier signals (such as EMI induced by cellular phones) to sensitive internal electronic circuits. The EMI filter design described herein effectively de-couples and shields such signals over a broad frequency range before they can enter into the titanium can and disrupt normal pacemaker or ICD functions.
Improved decoding of compressed images received over noisy channels

This paper presents an image communication system with improved decoding of compressed image information. A convolutional code protects the compressed image information from channel noise while a Reed-Solomon outer code gives additional protection to the critical image header information. A post-processor detects uncorrected channel errors in the reconstructed image and feeds error location information to a list-based iterative trellis decoder. This list-based decoder provides significant improvement in image quality. Experimental results are given for varying channel SNR and for varying bit rate.
Inverse halftoning via MAP estimation

There has been a tremendous amount of research in the area of image halftoning, where the goal has been to find the most visually accurate representation given a limited palette of gray levels (often just two, black and white). This paper focuses on the inverse problem, that of finding efficient techniques for reconstructing high-quality continuous-tone images from their halftoned versions. The proposed algorithms are based on a maximum a posteriori (MAP) estimation criteria using a Markov random field (MRF) model for the prior image distribution. Image estimates obtained with the proposed model accurately reconstruct both the smooth regions of the image and the discontinuities along image edges. Algorithms are developed and example gray-level reconstructions are presented generated from both dithered and error-diffused halftone originals. Application of the technique to the problems of rescreening and the processing of halftone images are shown.