nils-herrmann/clean_scirepeval huggingface dataset:
A landmark-based nonlinear elasticity model for mouse atlas registration

This paper is devoted to the registration of gene expression data to a neuroanatomical mouse atlas in two dimensions. We use a nonlinear elasticity regularization allowing large deformations, guided by an intensity-based data fidelity term and by landmarks. We overcome the difficulty of minimizing the nonlinear elasticity functional by introducing an additional variable v sime nablau, where u is the displacement. Thus, in the obtained Euler-Lagrange equation, the nonlinearity is no longer in the derivatives of the unknown, u. Experimental results show gene expression data mapped to a mouse atlas for a standard L2 data fidelity term in the presence of landmarks. We also present comparisons with biharmonic regularization. An advantage of the proposed nonlinear elasticity model is that usually no regridding is necessary, while keeping the data term, regularization term and landmark term in a unified minimization approach.
A new approach for spot detection in total internal reflection fluorescence microscopy

Biological images acquired from fluorescence microscopy-based imaging techniques, such as total internal reflection fluorescence microscopy, generally contain hundreds of subcellular components, appearing in the images as bright spots. Therefore, the first step of analysis of these images usually involves the detection of these bright spots. In this paper, we propose a new approach for spot detection using the h-dome transform along with an adaptive mask obtained from regional information. Moreover, local gradient information is used in order to distinguish the spots from other structures. To evaluate the performance of our algorithm, we test it on synthetic images and also real TIRFM images and compare our results with those of two recent methods.
Projective Reconstruction and Invariants from Multiple Images

This correspondence investigates projective reconstruction of geometric configurations seen in two or more perspective views, and the computation of projective invariants of these configurations from their images. A basic tool in this investigation is the fundamental matrix that describes the epipolar correspondence between image pairs. It is proven that once the epipolar geometry is known, the configurations of many geometric structures (for instance sets of points or lines) are determined up to a collineation of projective 3-space /spl Pscrsup 3/ by their projection in two independent images. This theorem is the key to a method for the computation of invariants of the geometry. Invariants of six points in /spl Pscrsup 3/ and of four lines in /spl Pscrsup 3/ are defined and discussed. An example with real images shows that they are effective in distinguishing different geometrical configurations. Since the fundamental matrix is a basic tool in the computation of these invariants, new methods of computing the fundamental matrix from seven-point correspondences in two images or six-point correspondences in three images are given.
Mirror Surface Reconstruction from a Single Image

This paper tackles the problem of reconstructing the shape of a smooth mirror surface from a single image. In particular, we consider the case where the camera is observing the reflection of a static reference target in the unknown mirror. We first study the reconstruction problem given dense correspondences between 3D points on the reference target and image locations. In such conditions, our differential geometry analysis provides a theoretical proof that the shape of the mirror surface can be recovered if the pose of the reference target is known. We then relax our assumptions by considering the case where only sparse correspondences are available. In this scenario, we formulate reconstruction as an optimization problem, which can be solved using a nonlinear least-squares method. We demonstrate the effectiveness of our method on both synthetic and real images. We then provide a theoretical analysis of the potential degenerate cases with and without prior knowledge of the pose of the reference target. Finally we show that our theory can be similarly applied to the reconstruction of the surface of transparent object.
Linear self-calibration of a rotating and zooming camera

A linear self-calibration method is given for computing the calibration of a stationary but rotating camera. The internal parameters of the camera are allowed to vary from image to image, allowing for zooming (change of focal length) and possible variation of the principal point of the camera. In order for calibration to be possible some constraints must be placed on the calibration of each image. The method works under the minimal assumption of zero-skew (rectangular pixels), or the more restrictive but reasonable conditions of square pixels, known pixel aspect ratio, and known principal point. Being linear the algorithm is extremely rapid, and avoids the convergence problems characteristic of iterative algorithms.
Solving Multilabel Graph Cut Problems with Multilabel Swap

Approximate solutions to labelling problems can be found using binary graph cuts and either the alpha-expansion or alpha-beta swap algorithms. In some specific cases, an exact solution can be computed by constructing a multilabel graph. However, in many practical applications the multilabel graph construction is infeasible due to its excessively large memory requirements. In this work, we expand the concept of alpha-beta swap to consider larger sets of labels at each iteration, and demonstrate how this approach is able to produce good approximate solutions to problems which can be solved using multilabel graph cuts. Furthermore, we show how alpha-expansion is a special case of multilabel swap, and from this new formulation, illustrate how alpha-expansion is now able to handle binary energy functions which do not satisfy the triangle inequality. Compared to alpha-beta swap, multilabel swap is able to produce an approximate solution in a shorter amount of time. We demonstrate the merits of our approach by considering the denoising and stereo problems. We illustrate how multilabel swap can be used in a recursive fashion to produce a good solution quickly and without requiring excessive amounts of memory.
X-ray metrology for quality assurance

There is considerable current interest in deriving accurate dimensional measurements of the internal geometry of complex manufactured parts, particularly castings. This paper describes an approach to the reconstruction of 3D part geometry from multiple digital X-ray images. A novel method for radiographic stereo is described which takes into account the special imaging geometry of the digital X-ray sensor modeled by a linear moving array, or pushbroom, camera. The 3D reconstruction algorithm employs a nominal geometric model which is perturbed by X-ray image constraints. Manufacturing applications are discussed and illustrated by experimental results on synthetic phantoms and actual casting images.<<ETX>>
Optimised KD-trees for fast image descriptor matching

In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-treepsilas search performance significantly.We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-treepsilas search performance.
Superpixels via pseudo-Boolean optimization

We propose an algorithm for creating superpixels. The major step in our algorithm is simply minimizing two pseudo-Boolean functions. The processing time of our algorithm on images of moderate size is only half a second. Experiments on a benchmark dataset show that our method produces superpixels of comparable quality with existing algorithms. Last but not least, the speed of our algorithm is independent of the number of superpixels, which is usually the bottle-neck for the traditional algorithms of superpixel creation.
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices

Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD matrices has proven key to the success of many algorithms. However, most existing methods only approximate the true shape of the manifold locally by its tangent plane. In this paper, inspired by kernel methods, we propose to map SPD matrices to a high dimensional Hilbert space where Euclidean geometry applies. To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices. These kernels are derived from the Gaussian kernel, but exploit different metrics on the manifold. This lets us extend kernel-based algorithms developed for Euclidean spaces, such as SVM and kernel PCA, to the Riemannian manifold of SPD matrices. We demonstrate the benefits of our approach on the problems of pedestrian detection, object categorization, texture analysis, 2D motion segmentation and Diffusion Tensor Imaging (DTI) segmentation.
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation

We propose a method of simultaneously calibrating the radial distortion function of a camera and the other internal calibration parameters. The method relies on the use of a planar (or, alternatively, nonplanar) calibration grid which is captured in several images. In this way, the determination of the radial distortion is an easy add-on to the popular calibration method proposed by Zhang [24]. The method is entirely noniterative and, hence, is extremely rapid and immune to the problem of local minima. Our method determines the radial distortion in a parameter-free way, not relying on any particular radial distortion model. This makes it applicable to a large range of cameras from narrow-angle to fish-eye lenses. The method also computes the center of radial distortion, which, we argue, is important in obtaining optimal results. Experiments show that this point may be significantly displaced from the center of the image or the principal point of the camera.
Use of digit-serial computation in systolic arrays

A method is described whereby digit-serial computation can be applied in a fairly general way to a class of systolic arrays so as to cut down substantially the amount of hardware used. Rather than increasing the utilization of the standard processor hardware, the technique proposed allows throughput to be maintained while reducing the hardware by a factor approaching alpha . The technique is to divide the data words into alpha sections, or digits, and to process these digits serially. Digit-serial data inherently have a data rate that is an integer submultiple of the parallel data rate, and the digit-size can be chosen so as to meet the data rates required by the systolic algorithm. The advantages of this approach are twofold: the number of wires required for the transmission of data is decreased and the computational elements can be smaller (digit-serial computational elements replacing the standard parallel operators). Thus, the computational elements are fully utilized.<<ETX>>
Stereo from uncalibrated cameras

The problem of computing placement of points in 3-D space, given two uncalibrated perspective views, is considered. The main theorem shows that the placement of the points is determined only up to an arbitrary projective transformation of 3-space. Given additional ground control points, however, the location of the points and the camera parameters may be determined. The method is linear and noniterative, whereas previously known methods for solving the camera calibration and placement problem to take proper account of both ground-control points and image correspondences are unsatisfactory in requiring either iterative methods or model restrictions. As a result of the main theorem, it is possible to determine projective invariants of 3-D geometric configurations from two perspective views.<<ETX>>
Lq Averaging for Symmetric Positive-Definite Matrices

We propose a method to find the Lq mean of a set of symmetric positive-definite (SPD) matrices, for 1 ≤ q ≤ 2. Given a set of points, the Lq mean is defined as a point for which the sum of q-th power of distances to all the given points is minimum. The Lq mean, for some value of q, has an advantage of being more robust to outliers than the standard L2 mean. The proposed method uses a Weiszfeld inspired gradient descent approach to compute the update in the descent direction. Thus, the method is very simple to understand and easy to code because it does not required line search or other complex strategy to compute the update direction. We endow a Riemannian structure on the space of SPD matrices, in particular we are interested in the Riemannian structure induced by the Log-Euclidean metric. We give a proof of convergence of the proposed algorithm to the Lq mean, under the Log-Euclidean metric. Although no such proof exists for the affine invariant metric but our experimental results show that the proposed algorithm under the affine invariant metric converges to the Lq mean. Furthermore, our experimental results on synthetic data confirms the fact that the L1 mean is more robust to outliers than the standard L2 mean.
CAD-based inspection using X-ray stereo

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 an X-ray stereo system which aims to achieve fast 3D geometry measurement from a few X-ray views of a part. We describe the key algorithms in our system and a novel application of it to CAD-based verification of drilled hole positioning. Experimental results are given to illustrate the accuracy of the current system and inspection on a real part.
A high speed FIR filter designed by compiler

Digital-serial computation is a hybrid between bit-serial computation and parallel computation. In digit-serial computation, data is divided into digits of N bits, and computations take place one digit of data at a time. The PARSIFAL silicon compiler is a software environment for designing chips using this basic computational architecture. The authors report on a four-tap FIR (finite-impulse response) filter built using this compiler. In order to avoid the throughput loss inherent in serial computation, two parallel computations are interleaved to achieve a 35-MHz-per-sample throughput rate
Multi-view Human Motion Capture with an Improved Deformation Skin Model

Markerless human motion capture has received much attention in computer vision and computer graphics communities. A hierarchical skeleton template is frequently used to model the human body in literature, because it simplifies markerless human motion capture as a problem of estimating the human body shape and joint angle parameters. The proposed work establishes a skeleton based markerless human motion capture framework, comprising of (1) an improved deformation skin model suitable for markerless motion capture while it is compliant with the computer animation standard, (2) image segmentation by using Gaussian mixture static background subtraction and (3) nonlinear dynamic temporal tracking with annealed particle filter. This framework is able to efficiently represent markerless human motion capture as an optimisation problem in the temporal domain and solve it by the classic optimisation scheme. Several experiments are used to illustrate its robustness and accuracy comparing with the existing approach.
A fast method to minimize L∞ error norm for geometric vision problems

Minimizing L∞ error norm for some geometric vision problems provides global optimization using the well- developed algorithm called SOCP (second order cone programming). Because the error norm belongs to quasi- convex functions, bisection method is utilized to attain the global optimum. It tests the feasibility of the intersection of all the second order cones due to measurements, repeatedly adjusting the global error level. The computation time increases according to the size of measurement data since the number of second order cones for the feasibility test inflates correspondingly. We observe in this paper that not all the data need be included for the feasibility test because we minimize the maximum of the errors; we may use only a subset of the measurements to obtain the optimal estimate, and therefore we obtain a decreased computation time. In addition, by using L∞ image error instead of L2 Euclidean distance, we show that the problem is still a quasi-convex problem and can be solved by bisection method but with linear programming (LP). Our algorithm and experimental results are provided.
Motion Estimation for Nonoverlapping Multicamera Rigs: Linear Algebraic and L∞ Geometric Solutions

We investigate the problem of estimating the ego-motion of a multicamera rig from two positions of the rig. We describe and compare two new algorithms for finding the 6 degrees of freedom (3 for rotation and 3 for translation) of the motion. One algorithm gives a linear solution and the other is a geometric algorithm that minimizes the maximum measurement error-the optimal L∞ solution. They are described in the context of the General Camera Model (GCM), and we pay particular attention to multicamera systems in which the cameras have nonoverlapping or minimally overlapping field of view. Many nonlinear algorithms have been developed to solve the multicamera motion estimation problem. However, no linear solution or guaranteed optimal geometric solution has previously been proposed. We made two contributions: 1) a fast linear algebraic method using the GCM and 2) a guaranteed globally optimal algorithm based on the L∞ geometric error using the branch-and-bound technique. In deriving the linear method using the GCM, we give a detailed analysis of degeneracy of camera configurations. In finding the globally optimal solution, we apply a rotation space search technique recently proposed by Hartley and Kahl. Our experiments conducted on both synthetic and real data have shown excellent results.
A new causal factors analysis to support a high reliability organization

Pantex, because of its nuclear weapon mission, has no choice but to be a High Reliability Organization (HRO). In addition to using Human Performance Improvements (HPI) as a way of proactively enhancing the attributes of its HRO, BWXT Pantex is currently undertaking a plant-wide initiative to implement a new Causal Factor Analysis (CFA) process that will provide a clear glimpse of “operational reality” during event investigations. By correlating this “operational reality” with HPI indicators and BWXT Pantex HRO indicators, BWXT Pantex plans to evaluate the worker’s response as a way of evaluating the effectiveness of the Pantex HRO.
Iterative Extensions of the Sturm/Triggs Algorithm: Convergence and Nonconvergence

We give the first complete theoretical convergence analysis for the iterative extensions of the Sturm/Triggs algorithm. We show that the simplest extension, SIESTA, converges to nonsense results. Another proposed extension has similar problems, and experiments with "balanced" iterations show that they can fail to converge or become unstable. We present CIESTA, an algorithm which avoids these problems. It is identical to SIESTA except for one simple extra computation. Under weak assumptions, we prove that CIESTA iteratively decreases an error and approaches fixed points. With one more assumption, we prove it converges uniquely. Our results imply that CIESTA gives a reliable way of initializing other algorithms such as bundle adjustment. A descent method such as Gauss-Newton can be used to minimize the CIESTA error, combining quadratic convergence with the advantage of minimizing in the projective depths. Experiments show that CIESTA performs better than other iterations.
Generalized Weiszfeld Algorithms for Lq Optimization

In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L2 cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L2 optimization.
Graph connectivity in sparse subspace clustering

Sparse Subspace Clustering (SSC) is one of the recent approaches to subspace segmentation. In SSC a graph is constructed whose nodes are the data points and whose edges are inferred from the L1-sparse representation of each point by the others. It has been proved that if the points lie on a mixture of independent subspaces, the graphical structure of each subspace is disconnected from the others. However, the problem of connectivity within each subspace is still unanswered. This is important since the subspace segmentation in SSC is based on finding the connected components of the graph. Our analysis is built upon the connection between the sparse representation through L1-norm minimization and the geometry of convex poly-topes proposed by the compressed sensing community. After introduction of some assumptions to make the problem well-defined, it is proved that the connectivity within each subspace holds for 2- and 3-dimensional subspaces. The claim of connectivity for general d-dimensional case, even for generic configurations, is proved false by giving a counterexample in dimensions greater than 3.