nils-herrmann/clean_scirepeval huggingface dataset:
Shift-orthogonal wavelet bases using splines

We present examples of a new type of wavelet basis functions that are orthogonal across shifts but not across scales. The analysis functions are piecewise linear while the synthesis functions are polynomial splines of degree n (odd). The approximation power of these representations is essentially as good as that of the corresponding Battle-Lemarie orthogonal wavelet transform, with the difference that the present wavelet synthesis filters have a much faster decay. This last property, together with the fact that these transformations are almost orthogonal, may be useful for image coding applications.
Registration and statistical analysis of PET images using the wavelet transform

We have described a general procedure for the processing and analysis of PET data. We have used the multiresolution framework of the wavelet transform to derive new solutions for the two main processing steps. The first task was to align the various brain images using a general affine deformation model. Our registration procedure uses a continuous polynomial spline image model and takes advantage of the multiresolution structure of the underlying function spaces. This method implements a nonlinear least squares optimization technique with a coarse-to-fine iteration strategy that substantially improves the overall performance of the algorithm. The second task was to analyze the series of registered images and to detect the between group differences in metabolic brain activity. We chose to take advantage of the orthogonality and localization properties of the wavelet transform. Our approach was to apply this transform to the group-difference image and identify the wavelet channels that are globally significantly different from noise.
How a simple shift can significantly improve the performance of linear interpolation

We present a simple, original method to improve piecewise linear interpolation with uniform knots. We shift the sampling knots by a fixed amount, while enforcing the interpolation property. Thanks to a theoretical analysis, we determine the optimal shift that maximizes the quality of our shifted linear interpolation. Surprisingly enough, this optimal value is nonzero and it is close to 1/5. We confirm our theoretical findings by performing a cumulative rotation experiment, which shows a significant increase of the quality of the shifted method with respect to the standard one. Most interesting is the fact that we get a quality similar to that of high-quality cubic convolution at the computational cost of linear interpolation.
The Ovuscule

We propose an active contour (a.k.a. snake) that takes the shape of an ellipse. Its evolution is driven by surface terms made of two contributions: the integral of the data over an inner ellipse, counterbalanced by the integral of the data over an outer elliptical shell. We iteratively adapt the active contour to maximize the contrast between the two domains, which results in a snake that seeks elliptical bright blobs. We provide analytic expressions for the gradient of the snake with respect to its defining parameters, which allows for the use of efficient optimizers. An important contribution here is the parameterization of the ellipse which we define in such a way that all parameters have equal importance; this creates a favorable landscape for the proceedings of the optimizer. We validate our construct with synthetic data and illustrate its use on real data as well.
Iterative multi-scale registration without landmarks

We present an automatic sub-pixel registration algorithm that minimizes the mean square difference of intensities between a reference and a test data set (volumes or images). It uses spline processing, is based on a coarse-to-fine pyramid strategy, and performs minimization according to a variation of the iterative Marquardt-Levenberg (1963) scheme. The geometric deformation model is a general affine transformation that one may optionally restrict to a rigid-body (isometric scale, rotation and translation), procrustean (rotation and translation) or translational case; it also includes an optional parameter for the linear adaptation of intensity. We present several PET and fMRI experiments and show that this algorithm provides excellent results. We conclude that the multi-resolution refinement strategy is faster and more robust than a comparable single-scale one.
Shift-orthogonal wavelet bases

Shift-orthogonal wavelets are a new type of multiresolution wavelet bases that are orthogonal with respect to translation (or shifts) within one level but not with respect to dilations across scales. We characterize these wavelets and investigate their main properties by considering two general construction methods. In the first approach, we start by specifying the analysis and synthesis function spaces and obtain the corresponding shift-orthogonal basis functions by suitable orthogonalization. In the second approach, we take the complementary view and start from the digital filterbank. We present several illustrative examples, including a hybrid version of the Battle-Lemarie (1987, 1988) spline wavelets. We also provide filterbank formulas for the fast wavelet algorithm. A shift-orthogonal wavelet transform is closely related to an orthogonal transform that uses the same primary scaling function; both transforms have essentially the same approximation properties. One experimentally confirmed benefit of relaxing the interscale orthogonality requirement is that we can design wavelets that decay faster than their orthogonal counterpart.
Convolution-based interpolation for fast, high-quality rotation of images

This paper focuses on the design of fast algorithms for rotating images and preserving high quality. The basis for the approach is a decomposition of a rotation into a sequence of one-dimensional translations. As the accuracy of these operations is critical, we introduce a general theoretical framework that addresses their design and performance. We also investigate the issue of optimality and present an improved least-square formulation of the problem. This approach leads to a separable three-pass implementation of a rotation using one-dimensional convolutions only. We provide explicit filter formulas for several continuous signal models including spline and bandlimited representations. Finally, we present rotation experiments and compare the currently standard techniques with the various versions of our algorithm. Our results indicate that the present algorithm in its higher-order versions outperforms all standard high-accuracy methods of which we are aware, both in terms of speed and quality. Its computational complexity increases linearly with the order of accuracy. The best-quality results are obtained with the sine-based algorithm, which can be implemented using simple one-dimensional FFTs.
Precision isosurface rendering of 3D image data

We address the task of rendering by ray tracing the isosurface of a high-quality continuous model of volumetric discrete and regular data. Based on first principles, we identify the quadratic B-spline as the best model for our purpose. The nonnegativity of this basis function allows us to confine the potential location of the isosurface within a binary shell. We then show how to use the space-embedding property of splines to further shrink this shell to essentially a single voxel width. Not all rays traced through a given shell voxel intersect the isosurface; many may only graze it, especially when the ray-tracing vantage point is close to or within the volume to be rendered. We propose an efficient heuristic to detect those cases. We present experiments to support our claims.
Complete parameterization of piecewise-polynomial interpolation kernels

Every now and then, a new design of an interpolation kernel appears in the literature. While interesting results have emerged, the traditional design methodology proves laborious and is riddled with very large systems of linear equations that must be solved analytically. We propose to ease this burden by providing an explicit formula that can generate every possible piecewise-polynomial kernel given its degree, its support, its regularity, and its order of approximation. This formula contains a set of coefficients that can be chosen freely and do not interfere with the four main design parameters; it is thus easy to tune the design to achieve any additional constraints that the designer may care for.
Regularized Interpolation for Noisy Images

Interpolation is the means by which a continuously defined model is fit to discrete data samples. When the data samples are exempt of noise, it seems desirable to build the model by fitting them exactly. In medical imaging, where quality is of paramount importance, this ideal situation unfortunately does not occur. In this paper, we propose a scheme that improves on the quality by specifying a tradeoff between fidelity to the data and robustness to the noise. We resort to variational principles, which allow us to impose smoothness constraints on the model for tackling noisy data. Based on shift-, rotation-, and scale-invariant requirements on the model, we show that the Lp-norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose. In addition to Tikhonov-like quadratic regularization, this includes edge-preserving total-variation-like (TV) regularization. We give algorithms to recover the continuously defined model from noisy samples and also provide a data-driven scheme to determine the optimal amount of regularization. We validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of TV-like nonquadratic regularization over Tikhonov-like quadratic regularization.
Bi-Exponential Edge-Preserving Smoother

Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure—a pair of one-tap recursions. By a careful but simple local adaptation of the filter weights to the data, we are able to design an edge-preserving smoother that has a very low memory and computational footprint while requiring a trivial coding effort. We demonstrate that our filter (a bi-exponential edge-preserving smoother, or BEEPS) has formal links with the traditional bilateral filter. On a practical side, we observe that the BEEPS also produces images that are similar to those that would result from the bilateral filter, but at a much-reduced computational cost. The cost per pixel is constant and depends neither on the data nor on the filter parameters, not even on the degree of smoothing.
Texture mapping by successive refinement

We define texture mapping as an optimization problem for which the goal is to preserve the maximum amount of information in the mapped texture. We derive a solution that is optimal in the least-squares sense and that corresponds to the pseudo-inverse of the texture-mapping transformation. In practice, a first-order approximation of the least-squares solution is used as an initial estimate for the mapped texture. This initial solution is refined by successive approximation to yield the least-squares optimal result. In essence, the proposed multi-pass method acts like an adaptive antialiasing filter.
Analysis of functional magnetic resonance images by wavelet decomposition

The use of the wavelet transform to detect differences between sequentially acquired functional magnetic resonance images (fMRIs) is explored. A statistical data model is developed that makes use of the orthogonality and regularity conditions of the wavelets to achieve a signal decomposition into uncorrelated components, enabling application of standard parametric tests of significance on wavelet coefficients directly. This overcomes the problems associated with high intervoxel correlations in the spatial domain, and achieves economy in statistical testing by limiting the search for significant signal components to a subspace where the signal power is located. Thus, a smaller p-value adjustment for multiple testing is required, resulting in a lower detection threshold for a given overall level of statistical significance. For the fMRIs investigated, a 10:1 reduction in the number of statistical tests was achieved, and about 1% of the wavelet coefficients were significant (p<0.05 per volume), which then served to resynthesize the difference images by inverse wavelet transform.
Compensation of unidirectional geometric distortion in EPI using spline warping

Due to magnetic field inhomogeneities, EPI images are geometrically distorted, predominantly along the phase-encoding direction. Currently, the distortion is either ignored or compensated manually using a warping function defined through a set of landmarks. We propose an automatic method to unwarp the geometric distortion of EPI images by registering them with corresponding undistorted anatomical MRI images. We show that cubic splines are optimal interpolating functions for both landmark interpolation and approximation. We will consequently use the same warping space in our algorithm which replaces landmarks by an image difference criterion. B-splines are used as generating functions, which leads to a fast and accurate computation. Multiresolution gives robustness and additional speedup. The algorithm performance was evaluated using both real and synthetic data and was found superior to the manual method.
High-quality isosurface rendering with exact gradient

We address the task of rendering by ray tracing the isosurface of a high-quality continuous spline model of volumetric discrete and regular data. By expressing the spline model as a sum of nonnegative B-splines, we are able to confine the potential location of the isosurface within a thin binary shell. We then show how to use the space-embedding property of splines to further shrink this shell to essentially a single-voxel width. We also propose a new illumination model that highlights the outline of the rendered isosurface, which provides for a sensitive test of the perceived quality of the rendering. We present experiments to support our claims, along with an efficient algorithm to compute simultaneously an array of B-splines and of its derivatives.
A pyramid approach to sub-pixel image fusion based on mutual information

We investigate aspects of multi-modal image registration based on a new criterion named mutual information (or sometimes Shannon information). This criterion is intensity-based and requires no landmarks; hence, its application can be automated without resorting to segmentation. We present a form amenable to derivation with respect to the geometric transformation parameters (affine transformation). This form involves Parzen windows; we explore the dependence of the registration accuracy on these windows and propose that they be tuned to each resolution level in a pyramid approach. We conduct experiments and show that both the window width and the number of windows is relevant. In addition, we show that it is beneficial to use a spline-based high-order interpolation scheme for applying the geometric transformation.
Efficient geometric transformations and 3-D image registration

Presents a general framework for the fast, high quality implementation of geometric affine transformations of images (p=2) or volumes (p=3), including rotations and scaling. The method uses a factorization of the p/spl times/p transformation matrix into p+1 elementary matrices, each affecting one dimension of the data only. This yields a separable implementation through an appropriate sequence of 1-D affine transformations (scaling+translation). Each elementary transformation is implemented in an optimal least squares sense using a polynomial spline signal model. The authors consider various matrix factorizations and compare their method with the conventional nonseparable interpolation approach. The new method provides essentially the same quality results and at the same time offers significant speed improvement.
The Snakuscule

Traditional snakes, or active contours, are planar parametric curves. Their parameters are determined by optimizing the weighted sum of three energy terms: one depending on the data (typically on the integral of its gradient under the curve, or on its integral over the area enclosed by the curve), one monitoring the shape of the curve (typically promoting its smoothness, or regularizing ambiguous solutions), and one incorporating prior knowledge (typically favoring a given shape). We present in this paper a snake that we designed to be as simple as possible without losing too many of the characteristics of more complicated, fuller versions. It retains an area data term and requires regularization to avoid an ill-posed optimization problem. It is parameterized by just two points, thus further easing requirements on the optimizer. Despite its extreme simplicity, this active contour can efficiently solve a variety of problems such as cell counting and segmentation of approximately circular features.
Design of Shapes for Precise Image Registration

This correspondence deals with the problem of designing planar shapes for subpixel image registration. Basic theoretical considerations are shown to lead to a lower bound on location accuracy. Optimal registration marks achieving this bound are discussed. These optimal designs, however, require very high printing or etching resolution and are inherently very sensitive to variations in the image sampling model (like scaling of grid size and rotation). More robust, optimal and suboptimal "topology-preserving" registration marks are then introduced and analyzed.
Latency in Speech Feature Analysis for Telepresence Event Coding

For video conferencing, there are network bandwidth and screen real-estate constraints that limit the number of user channels. We propose an intermediate transmission mode that transmits only at “events”, where these are detected by both audio and video changes from the short-term signal average. Our objective in this paper is to determine latency until the audio portion of a single telepresence channel stabilizes. It is this stable signal from which we detect events. We describe a recursive filter approach for feature determination and experiments on the Switchboard telephone call database. Results show latency to stable signal of up to 10 seconds. Although events can be detected much more quickly (
The converging squares algorithm: An efficient multidimensional peak picking method

The converging squares algorithm is a method for locating peaks in sampled data of 2 dimensions or higher. There are two primary advantages of this algorithm over other conventional methods. First, it is robust with respect to noise and data type. There are no empirical parameters to allow adjustment of the process, so results are completely objective. Secondly, the method is computationally efficient. The inherent structure of the algorithm is that of a resolution pyramid. This enhances computational efficiency as well as contributing to the quality of noise immunity of the method. The algorithm is detailed for 2-dimensional data. Quantitative comparisons of computation are made with two conventional peak picking methods.
A System for Automated Liver Tissue Image Analysis: Methods and Results

Image analysis techniques are described for the automatic detection, segmentation, feature description, and classification of tissue components, in liver tissue images. The result of the analysis is a quantitative description of the tissue, based on statistics of the components. A software package for analysis of these images has been developed by combining some newly developed algorithms with other techniques. The newly developed algorithms include the converging squares algorithm for location of cell nuclei, and the wedge filter technique for nuclei segmentation.
Protecting ownership rights through digital watermarking

The Internet revolution is now in full swing, and commercial interests abound. As with other maturing media technologies, the focus is moving from technology to content, as commercial vendors and developers try to use network technology to deliver media products for profit. This shift inevitably raises questions about how to protect ownership rights. Digital watermarking has been proposed as a way to identify the source, creator, owner, distributor, or authorized consumer of a document or image. Its objective is to permanently and unalterably mark the image so that the credit or assignment is beyond dispute. In the event of illicit use, the watermark would facilitate the claim of ownership, the receipt of copyright revenues, or successful prosecution. Watermarking has also been proposed for tracing images that have been illicitly redistributed. In the past, the infeasibility of large-scale photocopying and distribution often limited copyright infringement, but modern digital networks make large-scale dissemination simple and inexpensive. Digital watermarking allows each image to be uniquely marked for every buyer. If that buyer makes an illicit copy, the copy itself identifies the buyer as the source.
Using Information Extraction for Quality Analysis in Human Authentication

During the past decade there have been significant advances in the field of Natural Language Processing (NLP) and, in particular, Information Extraction (IE) which have fueled the development and deployment of a wide variety of systems in diverse application areas. However, one application area that has only recently been explored by the NLP community is the area of Information Assurance and Security. In this paper we describe a system for human authentication called QDP, or Query-Directed Passwords, that uses information extraction techniques with the World Wide Web as a database to provide feedback, from the security viewpoint, on the "quality" of a hint and answer pair input by a user.
Authenticating Lossy Surveillance Video

Public camera feeds are increasingly being opened to use by multiple authorities (e.g., police, fire, traffic) as well as to the public. Because of the difficulty and insecurity of sharing cryptographic keys, these data are available in the clear. However, authorities must have a mechanism to assure trust in the video, that is, to authenticate it. While lossless video is straightforward to authenticate by cryptographic means, lossy video as may result from UDP, wireless, or transcoded transmissions, is more difficult to authenticate. We describe a method that combines a concise and efficiently computed video fingerprint with public key cryptography. Essential components of our approach are: the procedure to combine inexact video fingerprint with exact digital signature to enable lossy authentication, and matching for misaligned video via a major incline approach. Experimental results relate video fingerprint length to authentication accuracy and latency (time to authentication).
Comparing passwords, tokens, and biometrics for user authentication

For decades, the password has been the standard means for user authentication on computers. However, as users are required to remember more, longer, and changing passwords, it is evident that a more convenient and secure solution to user authentication is necessary. This paper examines passwords, security tokens, and biometrics-which we collectively call authenticators-and compares these authenticators and their combinations. We examine their effectiveness against several attacks and suitability for particular security specifications such as compromise detection and nonrepudiation. Examples of authenticator combinations and protocols are described to show tradeoffs and solutions that meet chosen, practical requirements. The paper endeavors to offer a comprehensive picture of user authentication solutions for the purposes of evaluating options for use and identifying deficiencies requiring further research.