nils-herrmann/clean_scirepeval huggingface dataset:
Rapid prototyping of electronic systems

Describes a system for the rapid prototyping, testing and debugging of DSP designs. The DSP algorithm is first coded in a high level algorithmic language. This description is translated by the synthesis software into a structural specification suitable for HDI fabrication. The synthesis process includes the insertion of test capabilities into the hardware to allow for debugging the design. The rapid turnaround of HDI fabrication means that the user can have a prototype HDI module (DSP accelerator) in his hands ready for testing within at most a day or two, and at moderate cost. The DSP accelerator is then placed in a socket on a specially designed board (called the mother-board), connected to a Sun or other workstation, where it may be thoroughly tested and debugged. The debugging capabilities include structural verification, debugging and in-system test. Structural verification is testing the DSP accelerator to see that it is connected together correctly and that all the parts are working. Debugging involves sending test vectors through the chip, stepping, observing internal node values and limited reconfigurability with the purpose of debugging the algorithm. In-system test is done by connecting the DSP accelerator via a cable into the target system. This allows the system to be run at full speed (up to 20 MHz) with the DSP accelerator in place, but still retaining full observability of internal nodes.<<ETX>>
Superpixels, Occlusion and Stereo

Graph-based energy minimization is now the state of the art in stereo matching methods. In spite of its outstanding performance, few efforts have been made to enhance its capability of occlusion handling. We propose an occlusion constraint, an iterative optimization strategy and a mechanism that proceeds on both the digital pixel level and the super pixel level. Our method explicitly handles occlusion in the framework of graph-based energy minimization. It is fast and outperforms previous methods especially in the matching accuracy of boundary areas.
Critical configurations for n-view projective reconstruction

In this paper we give a characterization of critical configurations for projective reconstruction with any number of points and views. A set of cameras and points is said to be critical if the projected image points are insufficient to determine the placement of the points and the cameras uniquely, up to a projective transformation. For two views, the critical configurations are well-known. In this paper it is shown that a configuration of n 3 cameras and in points all lying on the intersection of two distinct ruled quadrics is critical. In distinction to the two-view case, which in general allows two alternative solutions, there is a family of ambiguous reconstructions for the n-view case. As a partial converse, it Is shown that for any critical configuration, all the points lie on the intersection of two ruled quadrics.
A new and compact algorithm for simultaneously matching and estimation

Feature matching and transformation estimation are two fundamental problems in computer vision research. These two problems are often related and even interlocked; solving one is solving the other's precondition. This makes them hard to solve. In order to overcome this difficulty, the paper presents a new compact algorithm requiring less than 10 lines of Matlab code. We show that the solutions of correspondence and transformation are merely two factors of two grammian matrices, and can be worked out by a factorization method. A Newton-Schulz numerical iteration algorithm is used for the factorization. The two interlocked problems are solved in an alternate (flip-flop) way. The effectiveness and efficiency are illustrated by experiments on both synthetic and real images. Global and fast convergence is attained, even starting from randomly chosen initial guesses.
Gradient Intensity-Based Registration of Multi-Modal Images of the Brain

We present a fast and accurate framework for registration of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial information in the form of 'gradient intensity'. We introduce gradient intensity as a measure of spatial strength of an image in a given direction and show that it can be used to determine the rotational misalignment independent of translation between the images. The rotation parameters are obtained by maximizing the mutual information of 2D gradient intensity matrices obtained from 3D images, hence reducing the dimensionality of the problem and improving efficiency. The rotation parameters along with estimations of translation are then used to initialize an optimization step over a conventional pixel intensity-based method to achieve sub-voxel accuracy. Our optimization algorithm converges quickly and is less subject to the common problem of misregistration due to local extrema. Experiments show that our method significantly improves the robustness, performance and efficiency of registration compared to conventional pixel intensity-based methods.
Optimizing over Radial Kernels on Compact Manifolds

We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit n-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.
Minimal Solutions for Panoramic Stitching

This paper presents minimal solutions for the geometric parameters of a camera rotating about its optical centre. In particular we present new 2 and 3 point solutions for the homography induced by a rotation with 1 and 2 unknown focal length parameters. Using tests on real data, we show that these algorithms outperform the standard 4 point linear homography solution in terms of accuracy of focal length estimation and image based projection errors.
Automatic Detection of Pre-ocular Tear Film Break-Up Sequence in Dry Eyes

Dry Eye Syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. Nevertheless, there is still no objective test that provides reliable results. We extend our method for the automated detection of dryness signs to include the breakup time (BUT), analysis of the degree of thinning of the tear film and detection of meniscus induced dryness, the last two have not been previously addressed. Our motivation is to help the clinician to automatically detect and analyze various signs related to dry eyes. The method has been tested on over 100 videos taken from 30 different patients. When compared to an analysis done by a specialized optometrist, our method is demonstrated to provide an accurate estimation for the BUT and the extent of the disease.
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 uniquely 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.
Optimizing pipelined networks of associative and commutative operators

A method of tree-height minimization of networks of commutative and associative operators is described. The algorithm aims at minimizing latency and shimming delays in a synchronous data flow architecture such as that used in bit/digit-serial computation. The algorithm rearranges adder/subtractor trees to meet the joint goals, often allowing otherwise impossible scheduling constraints to be met. The algorithmic methods are found to apply also to trees of adders and shifters, such as those found in shift/add multipliers.
Video Local Pattern based Image Matching for Visual Mapping

Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
A linear method for reconstruction from lines and points

Discusses the basic role of the trifocal tensor in scene reconstruction. This 3/spl times/3/spl times/3 tensor plays a role in the analysis of scenes from three views analogous to the role played by the fundamental matrix in the two-view case. In particular, the trifocal tensor maybe computed by a linear algorithm from a set of 13 line correspondences in three views. It is further shown in this paper to be essentially identical to a set of coefficients introduced by Shashua (1994) to effect point transfer in the three-view case. This observation means that the 13-line algorithm may be extended to allow for the computation of the trifocal tensor given any mixture of sufficiently many line and point correspondences. From the trifocal tensor, the camera image matrices may be computed, and the scene may be reconstructed. For unrelated uncalibrated cameras, this reconstruction is unique up to projectivity. Thus, projective reconstruction of a set of lines and points may be reconstructed linearly from three views.<<ETX>>
Illumination invariant sequential filtering human tracking

Many tracking problems can be efficiently solved by the Altering technique. Linear filter methods (e.g. Kaiman Filter) have shown their success and optimally in many linear settings with Gaussian noises. However, they expose inefficiency and weakness in the general nonlinear and high dimensional setting (e.g. human tracking). While, the advancement of Sequential Importance Re-sampling with Simulated Annealing has shown it is capable of handling nonlinearity and high dimensionality of human tracking. However, its performance is often affected by lighting variations and noises from silhouette segmentation. The proposed approach incorporates a textured human body template to annealed sequential filtering, and uses the illumination invariant CIELab formula to evaluate the observation likelihood so that influences of lighting changes and noises are minimised. Experiments with the benchmark HumanEval dataset demonstrate encouraging improvements over traditional Sequential Importance Re-sampling and the silhouette based method.
Iteratively reweighted graph cut for multi-label MRFs with non-convex priors

While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize. To tackle this, we introduce an algorithm that iteratively approximates the original energy with an appropriately weighted surrogate energy that is easier to minimize. Our algorithm guarantees that the original energy decreases at each iteration. In particular, we consider the scenario where the global minimizer of the weighted surrogate energy can be obtained by a multi-label graph cut algorithm, and show that our algorithm then lets us handle of large variety of non-convex priors. We demonstrate the benefits of our method over state-of-the-art MRF energy minimization techniques on stereo and inpainting problems.
Optimization of canonic signed digit multipliers for filter design

Constant multiplication can be carried out efficiently using a canonical signed-drift (CSD) representation of the multiplier. With this method, the multiplier can be implemented using a series of shifts and additions or subtractions. The author considers the optimization of such CSD multipliers by finding the correct sequence for shifting and adding. A computer program has been Written that will synthesize CSD multipliers in such a way as to minimize hardware and latency, producing optimal CSD multipliers. Applying these methods to finite-impulse-response (FIR) expressions, it is possible to reduce greatly the number of arithmetic operations needed.<<ETX>>
An object-oriented approach to scene reconstruction

A program is described for carrying out least-squares camera modelling and scene reconstruction from a set of image and scene measurements of geometric features. Because of the object-oriented nature of the program, it is easily extendible to include very general types of camera, image feature or measurement. A Levenberg-Marquardt parameter estimation algorithm is used to optimize the choice of camera and feature parameters to fit the measurements. A structured sparse technique is used to obtain speediest performance on large problems.
Outlier correction in image sequences for the affine camera

It is widely known that, for the affine camera model, both shape and motion can be factorized directly from the so-called image measurement matrix constructed from image point coordinates. The ability to extract both shape and motion from this matrix by a single SVD operation makes this shape-from-motion approach attractive; however, it can not deal with missing feature points and, in the presence of outliers, a direct SVD to the matrix would yield highly unreliable shape and motion components. Here, we present an outlier correction scheme that iteratively updates the elements of the image measurement matrix. The magnitude and sign of the update to each element is dependent upon the residual robustly estimated in each iteration. The result is that outliers are corrected and retained, giving improved reconstruction and smaller reprojection errors. Our iterative outlier correction scheme has been applied to both synthesized and real video sequences. The results obtained are remarkably good.
In defence of the 8-point algorithm

The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the 8 point algorithm is a frequently cited method for computing the fundamental matrix from a set of 8 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. The 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.<<ETX>>
The multibody trifocal tensor: motion segmentation from 3 perspective views

We propose a geometric approach to 3D motion segmentation from point correspondences in three perspective views. We demonstrate that after applying a polynomial embedding to the correspondences they become related by the so-called multibody trilinear constraint and its associated multibody trifocal tensor We show how to linearly estimate the multibody trifocal tensor from point-point-point correspondences. We then show that one can estimate the epipolar lines associated with each image point from the common root of a set of univariate polynomials and the epipoles by solving a plane clustering problem in /spl Ropf//sup 3/ using GPCA. The individual trifocal tensors are then obtained from the second order derivatives of the multibody trilinear constraint. Given epipolar lines and epipoles, or trifocal tensors, we obtain an initial clustering of the correspondences, which we use to initialize an iterative algorithm that finds an optimal estimate for the trifocal tensors and the clustering of the correspondences using Expectation Maximization. We test our algorithm on real and synthetic dynamic scenes.
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. 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.
Tracking of Blood Vessels in Retinal Images Using Kalman Filter

We present an automatic method to segment the blood vessels in retinal images. Our method is based on tracking the center of the vessels using the Kalman filter. We define a linear model to track the blood vessels, suitable for both the detection of wide and thin vessels in noisy images. The estimation of the next state is computed by using gradient information, histogram of the orientations and the expected structure of a vessel. Seed points are detected by a set of matched filters in different widths and orientations. Tracking is carried out for all detected seed points, however we retrace the segmentation for seeds with small confidence. Our algorithm also handles branching points by proceeding in the previous moving direction when no dominant gradient information is available. The method is tested on the public DRIVE database and shows good results with a low false positive rate.
Move-Based Algorithms for the Optimization of an Isotropic Gradient MRF Model

We consider an isotropic gradient model for the regularization terms in a multi-label MRF lattice. The isotropic gradient is modeled by considering 3- cliques in an 8-connected lattice. Of interest here are iterative move algorithms like alpha- expansion and alpha-beta swap, which try to minimize the energy function by solving a series of binary labeling problems. Such algorithms mainly differ in their update policy for the binary move at each iteration. Here, the aim is to study the submodularity of the binary move at each stage for a general update policy. We give the necessary and sufficient condition for the submodularity of a general update policy for the two major types of labels, namely ordered labels and unordered labels.
Using Galois Theory to Prove Structure from Motion Algorithms are Optimal

This paper presents a general method, based on Galois theory, for establishing that a problem can not be solved by a 'machine' that is capable of the standard arithmetic operations, extraction of radicals (that is, m-th roots for any m), as well as extraction of roots of polynomials of degree smaller than n, but no other numerical operations. The method is applied to two well known structure from motion problems: five point calibrated relative orientation, which can be realized by solving a tenth degree polynomial [6], and L2-optimal two-view triangulation, which can be realized by solving a sixth degree polynomial . It is shown that both these solutions are optimal in the sense that an exact solution intrinsically requires the solution of a polynomial of the given degree (10 or 6 respectively), and cannot be solved by extracting roots of polynomials of any lesser degree.
Portable Multi-megapixel Camera with Real-Time Recording and Playback

We are interested in the problem of automatically tracking football players, subject to the constraint that only one vantage point is available. Tracking algorithms benefit from seeing the entire playing field, as one does not have to worry about objects entering and leaving the field of view. However, the image of the entire field must be of sufficient resolution to allow each of the players to be identified automatically. To achieve this desired video data, several high definition video cameras are used to record a football match from a single vantage point. The cameras are oriented to cover the entire playing field, and their images combined to create a single high-resolution video feed. The user is able to pan and zoom in real-time within the unified video stream while it is playing. The system is achieved by distributing tasks across a network of computers and only processing data that will be visible to the user.
Height Measurement for Humans in Motion Using a Camera: A Comparison of Different Methods

This paper aims to answer the questions whether human height measurement from video is affected by human motion and how accurately human static height can be measured from a video or from a single image frame that captures a walking subject. In this paper, we present a new approach for measuring human height based on the cross ratio. This approach is performed in parallel with the vanishing point based approach for comparison. The accuracy of each approach is examined by comparing the estimated value to the actual static height value which is measured directly. For the height measurement from video, our analyses show that human height varies significantly during human motion and the highest point in a human stride is the most accurate measurement of the static height. We also model the height variation as a sinusoidal pattern to maximize the accuracy of the estimated height. For the height measurement from a single frame, a correction method is developed in which the length of the human torso with human head and the length of the human leg are separately estimated. We conclude that static human height can be measured accurately, even though the variation of human height is significant during human motion.
A linear least-squares solution to elastic Shape-from-Template

We cast SfT (Shape-from-Template) as the search of a vector field (X, δX), composed of the pose X and the displacement δX that produces the deformation. We propose the first fully linear least-squares SfT method modeling elastic deformations. It relies on a set of Solid Boundary Constraints (SBC) to position the template at X in the deformed frame. The displacement is mapped by the stiffness matrix to minimize the amount of force responsible for the deformation. This linear minimization is subjected to the Reprojection Boundary Constraints (RBC) of the deformed shape X + δX on the deformed image. Compared to state-of-the-art methods, this new formulation allows us to obtain accurate results at a low computation cost.