Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse is applied to sub-elements ( i.e. patches) the signal, where such assumption invalid. Convolutional explicitly models local interactions through convolution operator, however resulting optimization problem considerably more...

Compressive Sensing has become one of the standard methods face recognition within literature. We show, however, that sparsity assumption which underpins much this work is not supported by data. This lack in data means compressive sensing approach cannot be guaranteed to recover exact signal, and therefore sparse approximations may deliver robustness or performance desired. In vein we show a simple ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Implicit shape representations, such as Level Sets, provide a very elegant formulation for performing computations involving curves and surfaces. However, including implicit representations into canonical Neural Network formulations is far from straightforward. This has consequently restricted existing approaches to inference, significantly less effective perhaps most commonly voxels occupancy maps or sparse point clouds. To overcome this limitation we propose novel that permits the use of...

The calculation of a low-rank approximation matrix is fundamental operation in many computer vision applications. workhorse this class problems has long been the Singular Value Decomposition. However, presence missing data and outliers method not applicable, unfortunately, often case practice. In paper we present for calculating factorization which minimizes L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm data. Our approach...

In this paper we explore the role of duality principles within problem rotation averaging, a fundamental task in wide range computer vision applications. its conventional form, averaging is stated as minimization over multiple constraints. As these constraints are non-convex, generally considered challenging to solve globally. We show how circumvent difficulty through use Lagrangian duality. While such an approach well-known it normally not guaranteed provide tight relaxation. Based on...

In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method task is RANSAC. However, as RANSAC only works on a subset images, mismatches in longer point tracks may go undetected. To deal with would like to have, post processing step RANSAC, that entire (or larger) part sequence. two algorithms doing this. The first one related by Sim & Hartley where quasiconvex solved repeatedly error residuals largest...

Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising criterion still customarily done by randomised sample-and-test techniques, which do not guarantee optimality result. Several globally optimal algorithms exist, but they are too slow to challenge dominance methods. Our work aims change this state affairs proposing an efficient algorithm global maximisation consensus. Under framework LP-type methods, we show...

In this paper we study large-scale optimization problems in multi-view geometry, particular the Bundle Adjustment problem. its conventional formulation, complexity of existing solvers scale poorly with problem size, hence component Structure-from-Motion pipeline can quickly become a bottle-neck. Here present novel formulation for solving bundle adjustment truly distributed manner using consensus based methods. Our algorithm is presented concise derivation on proximal splitting, along...

To assess if and by which mechanisms pharmacological estrogen treatment induces gallstone disease, we examined patients with recently diagnosed prostatic cancer randomly allocated to therapy (n = 37) or orchidectomy 35). According gallbladder ultrasonography, after 1 yr new gallstones had developed in 5 of 28 estrogen-treated patients, compared 0 26 orchidectomized (P 0.03). Estrogen for 3 mo increased the relative concentration cholesterol saturation bile approximately 30% 10). Serum LDL...

Abstract: Concentrations of dopamine (DA), its metabolites 3‐methoxytyramine and homovanillic acid (HVA), noradrenaline (NA), normetanephrine (NM) 3‐methoxy‐4‐hydroxyphenylglycol (MHPG), 5‐hydroxytryptamine (5‐HT, serotonin), metabolite 5‐hy‐droxyindoleacetic (5‐HIAA) were measured in 14 brain regions CSF from the third ventricle 27 human autopsy cases. In addition, six cases, lumbar was obtained. Monoamine concentrations determined by reversed‐phase liquid chromatography with...

The calculation of a low-rank approximation to matrix is fundamental many algorithms in computer vision and other fields. One the primary tools used for calculating such approximations Singular Value Decomposition, but this method not applicable case where there are outliers or missing elements data. Unfortunately, often practice. We present which generalization Wiberg algorithm. Our calculates rank-constrained factorization, minimizes L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Registering two 3D point clouds involves estimating the rigid transform that brings into alignment. Recently there has been a surge of interest in using branch-and-bound (BnB) optimisation for cloud registration. While BnB guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source difficulty lies search rotational parameters. In this work, first by assuming translation known, we focus on constructing fast rotation algorithm. With respect an inherently...

Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising criterion still customarily done by randomised sample-and-test techniques, which do not guarantee optimality result. Several globally optimal algorithms exist, but they are too slow to challenge dominance methods. We aim change this state affairs proposing a very efficient algorithm global maximisation consensus. Under framework LP-type methods, we show how...

Bundle adjustment plays a vital role in feature-based monocular SLAM. In many modern SLAM pipelines, bundle is performed to estimate the 6DOF camera trajectory and 3D map (3D point cloud) from input feature tracks. However, two fundamental weaknesses plague systems based on adjustment. First, need carefully initialise means that all variables, particular map, must be estimated as accurately possible maintained over time, which makes overall algorithm cumbersome. Second, since estimating...

The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the exactly computationally demanding, and effort required increases rapidly with size. Although randomized algorithms are much more efficient, optimality of solution not guaranteed. Towards goal solving exactly, we present guaranteed outlier removal as a technique reduce runtime exact algorithms. Specifically, before conducting global optimization, attempt remove data that...

Star trackers are primarily optical devices that used to estimate the attitude of a spacecraft by recognising and tracking star patterns. Currently, most use conventional sensors. In this application paper, we propose usage event sensors for tracking. There potentially two benefits using tracking: lower power consumption higher operating speeds. Our main contribution is formulate an algorithmic pipeline from data includes novel formulations rotation averaging bundle adjustment. addition,...

Maximum consensus estimation plays a critically important role in several robust fitting problems computer vision. Currently, the most prevalent algorithms for maximization draw from class of randomized hypothesize-and-verify algorithms, which are cheap but can usually deliver only rough approximate solutions. On other extreme, there exact exhaustive search nature and be costly practical-sized inputs. This paper fills gap between two extremes by proposing deterministic to approximately...

In this paper we introduce two new methods for solving binary quadratic problems. While spectral relaxation have been the workhorse subroutine a wide variety of computer vision problems - segmentation, clustering, image restoration to name few it has recently challenged by semidefinite programming (SDP) relaxations. fact, can be shown that SDP relaxations produce better lower bounds than on with objective function. On other hand, computational complexity increases rapidly as number decision...

In this paper we explore the role of duality principles within problem rotation averaging, a fundamental task in wide range applications. its conventional form, averaging is stated as minimization over multiple constraints. As these constraints are non-convex, generally considered challenging to solve globally. We show how circumvent difficulty through use Lagrangian duality. While such an approach well-known it normally not guaranteed provide tight relaxation. Based on spectral graph...

In this paper we present a novel approach termed 3D Move to See (3DMTS) which is based on the principle of finding next best view using camera array and robotic manipulator obtain multiple samples scene from different perspectives. Distinct traditional visual servoing approaches, proposed method uses simultaneously-captured views, segmentation an objective function applied each perspective estimate gradient representing direction in "single shot". The demonstrated within simulation real...