nils-herrmann/clean_scirepeval huggingface dataset:
A multi-channel digital demodulator for LVDT and RVDT position sensors

The authors present the architecture, VLSI implementation and test results of a mixed analog-digital multichannel digital demodulator for a linear-variable-differential-transformer (LVDT) position sensor. The monolithic multichannel demodulator has four signal channels for demodulation and a built-in-test (BIT) channel. Each channel has a 12-bit Δ-Σ oversampled analog-to-digital converter (ADC) implemented with switched-capacitor circuits, and a dedicated digital demodulator with a 32-bit wordlength. The outputs from the ADCs are processed by digital processors. The digital demodulator uses a novel adaptive AM demodulation algorithm based on prediction techniques. A bit-serial silicon compiler is used to complete the design. The design was fabricated in a 1.2-μm bulk CMOS process and contains 85000 transistors in a die size of 325×330 mils2. Test results show that absolute accuracies of ±0.2% were achieved without the need for calibration
In Defense of the Eight-Point Algorithm

The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.
Minimizing algebraic error in geometric estimation problems

This paper gives a widely applicable technique for solving many of the parameter estimation problems encountered in geometric computer vision. A commonly used approach is to minimize an algebraic error function instead of a possibly preferable geometric error function. It is claimed in this paper that minimizing algebraic error will usually give excellent results, and in fact the main problem with most algorithms minimizing algebraic distance is that they do not take account of mathematical constraints that should be imposed on the quantity being estimated. This paper gives an efficient method of minimizing algebraic distance while taking account of the constraints. This provides new algorithms for the problems of resectioning a pinhole camera, computing the fundamental matrix, and computing the tri-focal tensor. Evaluation results are given for the resectioning and tri-focal tensor estimation algorithms.
A Survey of Medical Image Registration on Multicore and the GPU

In this article, we look at early, recent, and state-of-the-art methods for registration of medical images using a range of high-performance computing (HPC) architectures including symmetric multiprocessing (SMP), massively multiprocessing (MMP), and architectures with distributed memory (DM), and nonuniform memory access (NUMA). The article is designed to be self-sufficient. We will take the time to define and describe concepts of interest, albeit briefly, in the context of image registration and HPC. We provide an overview of the registration problem and its main components in the section "Registration." Our main focus will be HPC-related aspects, and we will highlight relevant issues as we explore the problem domain. This approach presents a fresh angle on the subject than previously investigated by the more general and classic reviews in the literature [1]-[3]. The sections "Multi-CPU Implementations" and "Accelerator Implementations" are organized from the perspective of high-performance and parallel- computing with the registration problem embodied. This is meant to equip the reader with the knowledge to map a registration problem to a given computing architecture.
Five-Point Motion Estimation Made Easy

Estimating relative camera motion from two calibrated views is a classical problem in computer vision. The minimal case for such problem is the so-called five-point problem, for which the state-of-the-art solution is Nister's algorithm (2003, 2004). However, due to the heuristic nature of the procedures it applies, to implement it needs much effort for non-expert user. This paper provides a simpler algorithm based on the hidden variable resultant technique. Instead of eliminating the unknown variables one by one (i.e, sequentially) using the Gauss elimination, our algorithm eliminates many unknowns at once. Moreover, in the equation solving stage, instead of back-substituting and solve all the unknowns sequentially, we compute the minimal singular vector of the coefficient matrix, by which all the unknown parameters can be estimated simultaneously. Experiments on both simulation and real images have validated the new algorithm
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices –especially of high-dimensional ones– comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold. To this end, we propose to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection. This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one. We show that learning can be expressed as an optimization problem on a Grassmann manifold and discuss fast solutions for special cases. Our evaluation on several classification tasks evidences that our approach leads to a significant accuracy gain over state-of-the-art methods.
High precision X-ray stereo for automated 3D CAD-based inspection

An important challenge in industrial metrology is to provide rapid measurement of critical 3D internal object geometry for either inspecting high volume parts or controlling a machining process. Existing metrological techniques are typically too slow to meet this need or can not measure small features with high precision. In this paper, we present a new method that achieves fast, accurate, internal 3D geometry measurement based on 3D reconstruction from a few X-ray views of a part. Our approach utilizes an accurate camera model for the X-ray sensor, calibration using in situ ground truth and geometry-guided X-ray feature extraction to achieve this goal and has been fully implemented in a prototype 3D measurement system. We describe a novel application of the system to CAD-based verification of drilled hole positioning. Experimental results are given to illustrate the precision of the system and 3D measurement on real industrial parts.
Multiple-View Geometry Under the {$L_\infty$}-Norm

This paper presents a new framework for solving geometric structure and motion problems based on the Linfin-norm. Instead of using the common sum-of-squares cost function, that is, the L2-norm, the model-fitting errors are measured using the Linfin-norm. Unlike traditional methods based on L2, our framework allows for the efficient computation of global estimates. We show that a variety of structure and motion problems, for example, triangulation, camera resectioning, and homography estimation, can be recast as quasi-convex optimization problems within this framework. These problems can be efficiently solved using second-order cone programming (SOCP), which is a standard technique in convex optimization. The methods have been implemented in Matlab and the resulting toolbox has been made publicly available. The algorithms have been validated on real data in different settings on problems with small and large dimensions and with excellent performance.
Removing Outliers Using The L\infty Norm

Recently, there has been interest in solving geometric vision problems such as triangulation and camera resectioning using L\infty minimization. One key advantage of using the L\infty norm rather than the L2 norm is that the L\infty cost function has a single minimum unlike the commonly used L2 cost function which typically has multiple local minima. However, one drawback of using L\infty minimization is that it is not robust to outliers. By minimizing the L\infty norm instead of the L2 norm, we are, in essence, fitting the outliers and not the good data. Therefore, before one can perform L\infty optimization on a problem, it is first necessary to remove outliers. A popular (but generally unsound) method of removing outliers is to minimize the cost function using standard optimization techniques; then if the residual error is too great, remove the offending measurements and continue. Although this method can fail for simple L2 optimization problems, we show in this paper that for a wide class of L\infty problems it is a valid technique. It is proved that the set of measurements with greatest residual must contain at least one outlier. Thus, if we keep throwing out the measurements with greatest residual, we will eventually remove all outliers in the data. We test this hypothesis on the multiview reconstruction problem and show that even simple strategies for throwing out these maximum residual measurements are effective in removing outliers.
A new simultaneous partitioning and chip placement approach based on simulated annealing

A major issue in VLSI design is that of determining how circuit components are to be distributed among a set of chips (partitioning problem) and where these chips are to be located on a board (placement problem) in order that densely connected components are placed close together and the wiring between the chips is minimized. A new partitioning and placement approach is proposed in which these problems are solved simultaneously rather than independently. A framework for annealing has been developed and cost models that allow the desired results to be achieved have been derived. It is shown experimentally that the approach results in easily routable designs.<<ETX>>
Minimizing energy functions on 4-connected lattices using elimination

We describe an energy minimization algorithm for functions defined on 4-connected lattices, of the type usually encountered in problems involving images. Such functions are often minimized using graph-cuts/max-flow, but this method is only applicable to submodular problems. In this paper, we describe an algorithm that will solve any binary problem, irrespective of whether it is submodular or not, and for multilabel problems we use alpha-expansion. The method is based on the elimination algorithm, which eliminates nodes from the graph until the remaining function is submodular. It can then be solved using max-flow. Values of eliminated variables are recovered using back-substitution. We compare the algorithm's performance against alternative methods for solving non-submodular problems, with favourable results.
Inverse tensor transfer for novel view synthesis

This paper provides a new transfer based novel view synthesis method. This method does not need a pre-computed dense depth map, therefore overcomes most common problems associated with conventional dense correspondence algorithms, yet still produce very photo-realistic novel images. The power of the method comes from the introducing and using of a novel inverse tensor transfer technique, which offers a simple mechanism to exploit both photometric constraints and geometric constraints across multiple input images. Our method works equally well for both calibrated images and un-calibrated images. Experiments on real sequences show promising results.
Convergence of Iteratively Re-weighted Least Squares to Robust M-Estimators

This paper presents a way of using the Iteratively Reweighted Least Squares (IRLS) method to minimize several robust cost functions such as the Huber function, the Cauchy function and others. It is known that IRLS (otherwise known as Weiszfeld) techniques are generally more robust to outliers than the corresponding least squares methods, but the full range of robust M-estimators that are amenable to IRLS has not been investigated. In this paper we address this question and show that IRLS methods can be used to minimize most common robust M-estimators. An exact condition is given and proved for decrease of the cost, from which convergence follows. In addition to the advantage of increased robustness, the proposed algorithm is far simpler than the standard L1 Weiszfeld algorithm. We show the applicability of the proposed algorithm to the rotation averaging, triangulation and point cloud alignment problems.
Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity

We propose a new approach for template-based extensible surface reconstruction from a single view. We extend the method of isometric surface reconstruction and more recent work on conformal surface reconstruction. Our approach relies on the minimization of a proposed stretching energy formalized with respect to the Poisson ratio parameter of the surface. We derive a patch-based formulation of this stretching energy by assuming local linear elasticity. This formulation unifies geometrical and mechanical constraints in a single energy term. We prevent local scale ambiguities by imposing a set of fixed boundary 3D points. We experimentally prove the sufficiency of this set of boundary points and demonstrate the effectiveness of our approach on different developable and non-developable surfaces with a wide range of extensibility.
Recovering Camera Motion Using L\infty Minimization

Recently, there has been interest in formulating various geometric problems in Computer Vision as L\infty optimization problems. The advantage of this approach is that under L\infty norm, such problems typically have a single minimum, and may be efficiently solved using Second-Order Cone Programming (SOCP). This paper shows that such techniques may be used effectively on the problem of determining the track of a camera given observations of features in the environment. The approach to this problem involves two steps: determination of the orientation of the camera by estimation of relative orientation between pairs of views, followed by determination of the translation of the camera. This paper focusses on the second step, that of determining the motion of the camera. It is shown that it may be solved effectively by using SOCP to reconcile translation estimates obtained for pairs or triples of views. In addition, it is observed that the individual translation estimates are not known with equal certainty in all directions. To account for this anisotropy in uncertainty, we introduce the use of covariances into the L\infty optimization framework.
Outlier removal using duality

In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step to RANSAC, a method that works on the entire (or a larger) part of the sequence. In this paper we consider two algorithms for doing this. The first one is related to a method by Sim & Hartley where a quasiconvex problem is solved repeatedly and the error residuals with the largest error is removed. Instead of solving a quasiconvex problem in each step we show that it is enough to solve a single LP or SOCP which yields a significant speedup. Using duality we show that the same theoretical result holds for our method. The second algorithm is a faster version of the first, and it is related to the popular method of L1-optimization. While it is faster and works very well in practice, there is no theoretical guarantee of success. We show that these two methods are related through duality, and evaluate the methods on a number of data sets with promising
Motion segmentation with missing data using PowerFactorization and GPCA

We consider the problem of segmenting multiple rigid motions from point correspondences in multiple affine views. We cast this problem as a subspace clustering problem in which the motion of each object lives in a subspace of dimension two, three or four. Unlike previous work, we do not restrict the motion subspaces to be four-dimensional or linearly independent. Instead, our approach deals gracefully with all the spectrum of possible affine motions: from two-dimensional and partially dependent to four-dimensional and fully independent. In addition, our method handles the case of missing data, meaning that point tracks do not have to be visible in all images. Our approach involves projecting the point trajectories of all the points into a 5-dimensional space, using the PowerFactorization method to fill in missing data. Then multiple linear subspaces representing independent motions is fitted to the points in R5 using GPCA. We test our algorithm on various real sequences with degenerate and nondegenerate motions, missing data, perspective effects, transparent motions, etc. Our algorithm achieves a misclassification error of less than 5% for sequences with up to 30% of missing data points.
An algorithm for efficient computation of spatial impulse response on the GPU with application in ultrasound simulation

Computation of the spatial impulse response (SIR) is a time-consuming but fundamental step in the computation of the linear ultrasonic fields in homogeneous media and the scattering fields in the presence of non-homogeneity. In this paper, we present a new algorithm for the computation of the SIR which is suitable for parallelization on massively multiprocessing platforms. The algorithm is numerically stable and can be used with double and single precision arithmetic. We present an implementation of the algorithm for CUDA-enabled GPUs. The results demonstrate a significant speedup (85-fold) in comparison with the state-of-the-art (implemented in Field II) and take us closer to the goal of accurate simulation of the ultrasound fields in real-time.
On the Optimality of Sequential Forward Feature Selection Using Class Separability Measure

This paper studies sequential forward feature selection that uses the scatter-matrix-based class separability measure. We find that by adding a scale factor to each iteration of the conventional sequential selection, a sequential selection that guarantees the global optimum can be attained. We give a thorough theoretical proof of its optimality via a novel geometric interpretation, and this leads to a unified framework including the optimal sequential selection, the conventional sequential selection and the best-individual-N selection. In addition, we show that with our formulation, feature selection can be treated as a linear fractional maximization problem, and it can be efficiently solved by algorithms well developed in the literature. This gives a non-sequential globally optimal feature selection algorithm. Both theoretical and experimental study demonstrate their efficiency.
Projective least-squares: Global solutions with local optimization

Work in multiple view geometry has focused on obtaining globally optimal solutions at the price of computational time efficiency. On the other hand, traditional bundle adjustment algorithms have been found to provide good solutions even though there may be multiple local minima. In this paper we justify this observation by giving a simple sufficient condition for global optimality that can be used to verify that a solution obtained from any local method is indeed global. The method is tested on numerous problem instances of both synthetic and real data sets. In the vast majority of cases we are able to verify that the solutions are optimal, in particular for small-scale problems. We also develop a branch and bound procedure that goes beyond verification. In cases where the sufficient condition does not hold, the algorithm returns either of the following two results: (i) a certificate of global optimality for the local solution or (ii) the global solution.
Reconstruction from six-point sequences

An algorithm is given for computing projective structure from a set of six points seen in a sequence of many images. The method is based on the notion of duality between cameras and points first pointed out by Carlsson and Weinshall. The current implementation avoids the weakness inherent in previous implementations of this method in which numerical accuracy is compromised by the distortion of image point error distributions under projective transformation. It is shown in this paper that one may compute the dual fundamental matrix by minimizing a cost function giving a first-order approximation to geometric distance error in the original untransformed image measurements. This is done by a modification of a standard near-optimal method for computing the fundamental matrix. Subsequently, the error measurements are adjusted optimally to conform with exact imaging geometry by application of the triangulation method of Hartley-Sturm.
Combining Multiple Manifold-Valued Descriptors for Improved Object Recognition

We present a learning method for classification using multiple manifold-valued features. Manifold techniques are becoming increasingly popular in computer vision since Riemannian geometry often comes up as a natural model for many descriptors encountered in different branches of computer vision. We propose a feature combination and selection method that optimally combines descriptors lying on different manifolds while respecting the Riemannian geometry of each underlying manifold. We use our method to improve object recognition by combining HOG~\cite{Dalal05Hog} and Region Covariance~\cite{Tuzel06} descriptors that reside on two different manifolds. To this end, we propose a kernel on the $n$-dimensional unit sphere and prove its positive definiteness. Our experimental evaluation shows that combining these two powerful descriptors using our method results in significant improvements in recognition accuracy.
Designing systolic arrays using digit-serial arithmetic

A method of transforming systolic arrays using bit-parallel arithmetic into arrays using digit-serial arithmetic is described. Digit-serial computation is an area-time efficient method of doing high-speed arithmetic calculations, having the advantage through appropriate choice of digit and word size of allowing throughput capacity to be matched to design needs. For a certain class of systolic arrays, digit-serial arithmetic allows a further very significant benefit by transforming arrays in which processors are under-utilized into arrays late with 100% processor utilization.
A critical configuration for reconstruction from rectilinear motion

This paper investigates critical configurations for projective reconstruction from multiple images taken by a camera moving in a straight line. Projective reconstruction refers to a determination of the 3D (three-dimensional) geometrical configuration of a set of 3D points and cameras, given only correspondences between points in the images. A configuration of points and cameras is critical if it cannot be determined uniquely (up to a projective transform) from the image coordinates of the points. It is shown that a configuration consisting of any number of cameras lying on a straight line, and any number of points lying on a twisted cubic constitutes a critical configuration. An alternative configuration consisting of a set of points and cameras all lying on a rational quartic curve exists.