nils-herrmann/clean_scirepeval huggingface dataset:
Translation Estimation from Omnidirectional Images

We present a translation estimation method from omnidirectional images with missing data. Given omnidirectional images, it is possible to estimate the rotations of each camera using a fundamental matrix. We can thus get the translations of each camera using a plane-based projective reconstruction method. However, the plane-based translation estimation method does not give a robust result when there are measurement errors in the data. The method proposed in this paper solves this problem and shows more reliable results than the previous method.
The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration

It is well-known that the relative pose problem can be generalized to non-central cameras. We present a further generalization, denoted the generalized relative pose and scale problem. It has surprising importance for classical problems such as solving similarity transformations for view-graph concatenation in hierarchical structure from motion and loop-closure in visual SLAM, both posed as a 2D-2D registration problem. The relative pose problem and all its generalizations constitute a family of similar symmetric eigenvalue problems, which allow us to compress data and find a geometrically meaningful solution by an efficient search in the space of rotations. While the derivation of a completely general closed-form solver appears intractable, we make use of a simple heuristic global energy minimization scheme based on local minimum suppression, returning outstanding performance in practically relevant scenarios. Efficiency and reliability of our algorithm are demonstrated on both simulated and real data, supporting our claim of superior performance with respect to both generalized 2D-3D and 3D-3D registration approaches. By directly employing image information, we avoid the common noise in point clouds occurring especially along the depth direction.
Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning

Simple nearest neighbor classification fails to exploit the additional information in image sets. We propose self-regularized nonnegative coding to define between set distance for robust face recognition. Set distance is measured between the nearest set points (samples) that can be approximated from their orthogonal basis vectors as well as from the set samples under the respective constraints of self-regularization and nonnegativity. Self-regularization constrains the orthogonal basis vectors to be similar to the approximated nearest point. The nonnegativity constraint ensures that each nearest point is approximated from a positive linear combination of the set samples. Both constraints are formulated as a single convex optimization problem and the accelerated proximal gradient method with linear-time Euclidean projection is adapted to efficiently find the optimal nearest points between two image sets. Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition. The proposed algorithm works independently of the chosen features and has been tested on gray pixel values and local binary patterns. Experiments on three standard data sets show that the proposed method consistently outperforms existing state-of-the-art methods.
Image-based interactive exploration of real-world environments

Interactive scene walkthroughs have long been an important computer graphics application area. More recently, researchers have developed techniques for constructing photorealistic 3D architectural models from real-world images. We present an image-based rendering system that brings us a step closer to a compelling sense of being there. Whereas many previous systems have used still photography and 3D scene modeling, we avoid explicit 3D reconstruction because it tends to be brittle. Our system is not the first to propose interactive video-based tours. We believe, however, that our system is the first to deliver fully interactive, photorealistic image-based tours on a personal computer at or above broadcast video resolutions and frame rates. Moreover, to our knowledge, no other tour provides the same rich set of interactions or visually complex environments.
A digital-serial silicon compiler

A novel silicon compiler is described, called PARSIFAL. It constructs chips with a data-flow architecture in which data is passed in a digit-wide pipeline form one computational element to the next. The size of a digit can be specified by the user to be any value between one and the full word size of the chip. A digit size of one gives bit-serial chips, whereas a digit-size equal to the word-size gives fully parallel computation. It is shown that an intermediate value of the digit-size usually gives the most efficient chips in terms of throughput per unit area.<<ETX>>
A synthesis, test and debug environment for rapid prototyping of DSP designs

Recently, considerable progress has been made in the design of digital signal processing (DSP) integrated circuits and systems. In order to address the need for rapid and economical production and testing of hardware prototypes, a hardware and software system called DIODES is being developed for the rapid prototyping, testing and debugging of DSP designs. DIODES will allow the user to design a DSP system, have it partitioned into predefined function blocks, have it assembled using advanced packing technology and then thoroughly test and debug the design both stand-alone and in a larger electronic system environment. This paper gives an overview of the whole system focussing particularly on the debugging hardware and software support environment. The algorithmic description is translated by the DIODES synthesis software into a structural specification suitable for High-Density Interconnect (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 DIODES module in hand ready for testing within at most a day or two, and at moderate cost.<<ETX>>
Rapid prototyping using high density interconnects

This paper introduces the discretionary interconnect one-day electronic systems (DIODES) for the rapid prototyping of DSP electronic systems. DIODES merges silicon compiler and high-density interconnect technology with the goal of prototyping hardware systems as quickly as possible-within one day. Working from a high-level algorithmic description of a DSP algorithm, DIODES will determine which chips from an inventory of specially designed chips are needed to implement the design. These chips are then placed on a prepared substrate and routed together using computer controlled laser lithography to produce a hardware implementation of the design.<<ETX>>
LQ-bundle adjustment

In this paper we propose a method to solve for an Lq solution of bundle adjustment, a non-linear parameter estimation problem. Given a set of images of a scene, bundle adjustment simultaneously estimates camera parameters and 3D structure of the scene. Generally, a least squares criterion is minimized by using the Levenberg-Marquardt (LM) method, a non-linear least squares optimization method. It is known that the least squares methods are not robust to outliers, even a single outlier can deviate the solution from its true value. Therefore, we propose a method to minimize an Lq cost function, for 1 ≤ q <; 2. The Lq cost function minimizes the sum of the q-th power of errors. The proposed method has an advantage of using the Levenberg-Marquardt (LM) method to find a robust solution of the problem. Our experimental results confirm that the proposed method is more robust to outliers than the standard least squares method.
A linear approach to motion estimation using generalized camera models

A well-known theoretical result for motion estimation using the generalized camera model is that 17 corresponding image rays can be used to solve linearly for the motion of a generalized camera. However, this paper shows that for many common configurations of the generalized camera models (e.g., multi-camera rig, catadioptric camera etc.), such a simple 17-point algorithm does not exist, due to some previously overlooked ambiguities. We further discover that, despite the above ambiguities, we are still able to solve the motion estimation problem effectively by a new algorithm proposed in this paper. Our algorithm is essentially linear, easy to implement, and the computational efficiency is very high. Experiments on both real and simulated data show that the new algorithm achieves reasonably high accuracy as well.
Camera calibration and the search for infinity

This paper considers the problem of self-calibration of a camera from an image sequence in the case where the camera's internal parameters (most notably focal length) may change. The problem of camera self-calibration from a sequence of images has proven to be a difficult one in practice, due to the need ultimately to resort to non-linear methods, which have often proven to be unreliable. In a stratified approach to self-calibration, a projective reconstruction is obtained first and this is successively refined first to an affine and then to a Euclidean (or metric) reconstruction. It has been observed that the difficult step is to obtain the affine reconstruction, or equivalently to locate the plane at infinity in the projective coordinate frame. The problem is inherently non-linear and requires iterative methods that risk not finding the optimal solution. The present paper overcomes this difficulty by imposing chirality constraints to limit the search for the plane at infinity to a 3-dimensional cubic region of parameter space. It is then possible to carry out a dense search over this cube in reasonable time. For each hypothesised placement of the plane at infinity, the calibration problem is reduced to one of calibration of a nontranslating camera, for which fast non-iterative algorithms exist. A cost function based on the result of the trial calibration is used to determine the best placement of the plane at infinity. Because of the simplicity of each trial, speeds of over 10,000 trials per second are achieved on a 256 MHz processor. It is shown that this dense search allows one to avoid areas of local minima effectively and find global minima of the cost function.
Recursive computation of the Fourier transform

Efficient recursive methods and circuits for computing a continuously updated discrete Fourier transform (DFT) of an input-digital signal are considered. The Fourier transform (FT) is recomputed at each sample input time, with only O(N) operations being required to compute the transform, where N is the number of frequency bins. Various window functions are considered for windowing the input-wave form, namely a rectangular window, a triangular window, and an exponential window. The last type of window has not been widely considered in the past, partly due to its asymmetrical shape, and hence nonlinear phase response. Nevertheless, it is shown to have certain advantages in ease of computation and in flexibility. For the exponential window, a circuit that conveniently allows zooming in to particularly interesting parts of the frequency spectrum is shown. By appropriately loading a multiplier storage RAM, arbitrarily fine resolution may be achieved in any part of the spectrum, thus permitting closely adjacent peaks to be distinguished. The general approach is based on the interpretation of the FT as a set of simultaneous bandpass filters. Though these filters are generally finite-impulse-response filters, computational advantages are derived from formulating them as recursive filters.<<ETX>>
The 3D-3D Registration Problem Revisited

We describe a new framework for globally solving the 3D-3D registration problem with unknown point correspondences. This problem is significant as it is frequently encountered in many applications. Existing methods are not fully satisfactory, mainly due to the risk of local minima. Our framework is grounded on the Lipschitz global optimization theory. It achieves a guaranteed global optimality without any initialization. By exploiting the special structure of the problem itself and of the 3D rotation space SO(3), we propose a box-and-ball algorithm, which solves the problem efficiently. The main idea of the work can be applied to many other problems as well.
Improved Single Image Dehazing Using Geometry

Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique estimates the depth of each pixel independently, and assumes neighbouring pixels will have similar depths. In this work, we employ the fact that images containing fog are captured from outdoor cameras. As a result, the scene geometry is usually dominated by a ground plane. More importantly, objects which appear towards the top of the image are usually further away. We show how this preference (implemented as a soft constraint) is compatible with the alpha-expansion optimization technique and illustrate how it can be used to improve the robustness of any single image dehazing technique.
A digit-serial compiler operator library

The PARSIFAL compiler is conceived for higher-throughput applications, allowing chips to be designed with throughput sample rates of up to 30 MHz, containing 300-400 MOPS on a chip of moderate size (250-mil/sup 2/). Details are presented of the library of digit-serial cells used to implement arithmetic operations on digit-serial data. A description is given of the design of digit-serial addition/subtraction, multiplication, and shifting cells, and it is indicated how division can be carried out. The multiple precision and staggered-digit data formats are described. These formats allow greater precision and higher throughput to be achieved when needed.<<ETX>>
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, usual Euclidean computer vision and machine learning algorithms yield inferior results on such data. In this paper, we define Gaussian radial basis function (RBF)-based positive definite kernels on manifolds that permit us to embed a given manifold with a corresponding metric in a high dimensional reproducing kernel Hilbert space. These kernels make it possible to utilize algorithms developed for linear spaces on nonlinear manifold-valued data. Since the Gaussian RBF defined with any given metric is not always positive definite, we present a unified framework for analyzing the positive definiteness of the Gaussian RBF on a generic metric space. We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i.e., the Riemannian manifold of linear subspaces of a Euclidean space. We show that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels.
Image Registration in Hough Space Using Gradient of Images

We present an accurate and fast method for rigid registration of images with large non-overlapping areas using a Hough transformation of image gradients. The Hough space representation of gradients can be used to separate estimation of the rotation parameter from the translation. It also allows us to estimate transformation parameters for 2D images over a 1D space, hence reducing the computational complexity. The cost functions in the Hough domain have larger capture ranges compared to the cost functions in the intensity domain. This allows the optimization to converge better in the presence of large misalignments. We show that the combination of estimating registration parameters in the Hough domain and fine tuning the results in the intensity domain significantly improves performance of the application compared to the conventional intensitybased multi-resolution methods.
Factorization-based structure-and-motion computation for generalized camera model

Generalized camera model (GCM) has been introduced recently to unify the analysis and description of a variety of non-conventional camera designs (e.g. catadioptric and omnidirectional), as well as multi-camera systems. In this paper, we extend the well-known and powerful Tomasi-Kanade type factorization framework to generalized cameras. We first prove that even for such seemingly more complicated generalized cameras there is also a rank-4 constraint, similar to the case of using a single pinhole projective camera. This result is much simpler and more compact than a recent work suggesting a rank-13 tensor factorization. Secondly, we propose two GCM factorization algorithms to recover the structure and motion. We also provide theoretic convergence analysis for the algorithms. Experiments on synthetic data validate the theory and the proposed algorithms.
Tree-height minimization in pipelined architectures

A method of tree-height minimization for networks of commutative and associative operators is proposed. An algorithm is described for minimizing latency and shimming delays in a synchronous data-flow architecture such as that used in pipelined or bit- or digit-serial computation. The algorithm rearranges operator trees to meet the joint goals, often allowing otherwise impossible scheduling constraints to be met. It may also be applied to word-parallel pipelined architectures to optimize operator trees within pipelined stages. The method is evaluated by testing it on several filter examples for which it finds optimal network topologies and schedules.<<ETX>>
On Projective Reconstruction in Arbitrary Dimensions

We study the theory of projective reconstruction for multiple projections from an arbitrary dimensional projective space into lower-dimensional spaces. This problem is important due to its applications in the analysis of dynamical scenes. The current theory, due to Hartley and Schaffalitzky, is based on the Grassmann tensor, generalizing the ideas of fundamental matrix, trifocal tensor and quadrifocal tensor used in the well-studied case of 3D to 2D projections. We present a theory whose point of departure is the projective equations rather than the Grassmann tensor. This is a better fit for the analysis of approaches such as bundle adjustment and projective factorization which seek to directly solve the projective equations. In a first step, we prove that there is a unique Grassmann tensor corresponding to each set of image points, a question that remained open in the work of Hartley and Schaffalitzky. Then, we prove that projective equivalence follows from the set of projective equations given certain conditions on the estimated camera-point setup or the estimated projective depths. Finally, we demonstrate how wrong solutions to the projective factorization problem can happen, and classify such degenerate solutions based on the zero patterns in the estimated depth matrix.
Detection of the tear meniscus shape using asymmetric graph-cuts

We present a new fully automatic algorithm to evaluate the shape and regularity of the tear meniscus in eye images taken using a slit-lamp after instilling fluorescein. Our method analyzes the meniscus in the corneal and conjunctival areas and detects abnormalities such as conjunctival folds. We use graph-cuts to minimize a cost function to simultaneously produce a segmentation of the meniscus and the best shape prior for the eyelids. The pairwise term is asymmetric in order to capture the global properties of the meniscus and add a sense of direction. We tested our method on 43 images and provide a grading of the quality of the meniscus.
Auto-Calibration of a Compound-Type Omnidirectional Camera

We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
Memory Efficient Max Flow for Multi-Label Submodular MRFs

Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable <inline-formula><tex-math notation="LaTeX">$X_i$</tex-math><alternatives><inline-graphic xlink:href="ajanthan-ieq1-2819675.gif"/></alternatives></inline-formula> is represented by <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math><alternatives><inline-graphic xlink:href="ajanthan-ieq2-2819675.gif"/></alternatives></inline-formula> nodes (where <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math><alternatives><inline-graphic xlink:href="ajanthan-ieq3-2819675.gif"/></alternatives></inline-formula> is the number of labels) arranged in a column. However, this method in general requires <inline-formula><tex-math notation="LaTeX">$2\;\ell ^2$</tex-math><alternatives><inline-graphic xlink:href="ajanthan-ieq4-2819675.gif"/></alternatives></inline-formula> edges for each pair of neighbouring variables. This makes it inapplicable to realistic problems with many variables and labels, due to excessive memory requirement. In this paper, we introduce a variant of the max-flow algorithm that requires much less storage. Consequently, our algorithm makes it possible to optimally solve multi-label submodular problems involving large numbers of variables and labels on a standard computer.
Digit-serial processing techniques

An architecture is presented for hard-wired data-flow algorithms which is based on the transmission of arithmetic data one digit at a time serially, and performance of operations digit-serially on that data. It is shown that digit-serial computation gives rise to particularly efficient chip designs, and that choice of digit-size allows the user to match throughput requirements to specifications. Details of the implementation of the individual operators as a cell-library of silicon CMOS circuits are given and mention is made of the software environment (silicon compiler) which allows the rapid translation of algorithms to integrated circuits.