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1.	Aanæs, Henrik, et al. (författare) Camera Resectioning from a Box 2009 Ingår i: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349. Konferensbidrag (refereegranskat)abstract In this paper we describe how we can do camera resectioning from a box with unknown dimensions, i.e. determine the camera model, assuming that image pixels are square. This assumption is equivalent to assuming that the camera as an aspect ratio of one and zero skew, and holds for most - if not all - digital cameras. Our proposed method works by first deriving 9 linear constraints on the projective camera matrix from the box, leaving a 3 dimensional subspace in which the projective camera matrix can lye. A single solution in this 3D subspace is then found via a method by Triggs in 1999, which uses the squared pixel assumption to set up a 4th degree polynomial to which the solution is the desired model. This approach is, however, numerically challenging, and we use several means to combat this issue. Lastly the solution is refined in an iterative manner, i.e. using bundle adjustment.
2.	Aanaes, Henrik, et al. (författare) Estimation of Deformable Structure and Motion 2002 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
3.	Agarwal, S, et al. (författare) Practical global optimization for multiview geometry 2006 Ingår i: Lecture Notes in Computer Science. - 1611-3349. ; 3951:Pt 1: Proceedings, s. 592-605 Tidskriftsartikel (refereegranskat)abstract This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and hemography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L-2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L-1-norm which is less sensitive to outliers. The efficacy of our algorithm is empirically demonstrated by good performance on experiments for both synthetic and real data. An open source MATLAB toolbox that implements the algorithm is also made available to facilitate further research.
4.	Alipoor, Mohammad, 1983, et al. (författare) Optimal Experiment Design for Mono-Exponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging 2015 Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2015 Tidskriftsartikel (refereegranskat)abstract The mono-exponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics.The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for mono-exponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (?-optimal design). In contrast to previous methods, ?-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, range of ?-values). Using Monte Carlo simulations we show that the ?-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.
5.	Alipoor, Mohammad, 1983, et al. (författare) Optimal Gradient Encoding Schemes for Diffusion Tensor and Kurtosis Imaging 2016 Ingår i: IEEE transactions on Computational Imaging. - 2333-9403. ; 2:3, s. 375-391 Tidskriftsartikel (refereegranskat)abstract Diffusion-derived parameters find application in characterizing pathological and developmental changes in living tissues. Robust estimation of these parameters is important because they are used for medical diagnosis. An optimal gradient encoding scheme (GES) is one that minimizes the variance of the estimated diffusion parameters. This paper proposes a method for optimal GES design for two diffusion models: high-order diffusion tensor (HODT) imaging and diffusion kurtosis imaging (DKI). In both cases, the optimal GES design problem is formulated as a D-optimal (minimum determinant) experiment design problem. Then, using convex relaxation, it is reformulated as a semidefinite programming problem. Solving these problems we show that: 1) there exists a D-optimal solution for DKI that is simultaneously D-optimal for second- and fourth-order diffusion tensor imaging (DTI); 2) the traditionally used icosahedral scheme is approximately D-optimal for DTI and DKI; 3) the proposed D-optimal design is rotation invariant; 4) the proposed method can be used to compute the optimal design ($b$ -values and directions) for an arbitrary number of measurements and shells; and 5) using the proposed method one can obtain uniform distribution of gradient encoding directions for a typical number of measurements. Importantly, these theoretical findings provide the first mathematical proof of the optimality of uniformly distributed GESs for DKI and HODT imaging. The utility of the proposed method is further supported by the evaluation results and comparisons with with existing methods.
6.	Alvén, Jennifer, 1989, et al. (författare) A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images 2019 Ingår i: Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030322458 - 9783030322441 ; 11765 LNCS, s. 355-363 Konferensbidrag (refereegranskat)abstract The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results.
7.	Alvén, Jennifer, 1989, et al. (författare) Shape-aware label fusion for multi-atlas frameworks 2019 Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 124, s. 109-117 Tidskriftsartikel (refereegranskat)abstract Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures.
8.	Alvén, Jennifer, 1989, et al. (författare) Shape-aware multi-atlas segmentation 2016 Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. ; 0, s. 1101-1106 Konferensbidrag (refereegranskat)abstract Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.
9.	Alvén, Jennifer, 1989, et al. (författare) Überatlas: Fast and robust registration for multi-atlas segmentation 2016 Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 80, s. 249-255 Tidskriftsartikel (refereegranskat)abstract Multi-atlas segmentation has become a frequently used tool for medical image segmentation due to its outstanding performance. A computational bottleneck is that all atlas images need to be registered to a new target image. In this paper, we propose an intermediate representation of the whole atlas set – an überatlas – that can be used to speed up the registration process. The representation consists of feature points that are similar and detected consistently throughout the atlas set. A novel feature-based registration method is presented which uses the überatlas to simultaneously and robustly find correspondences and affine transformations to all atlas images. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding ground truth. Our approach succeeds in producing better and more robust segmentation results compared to three baseline methods, two intensity-based and one feature-based, and significantly reduces the running times.
10.	Alvén, Jennifer, 1989, et al. (författare) Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation 2015 Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319196640 ; 9127, s. 92-102 Konferensbidrag (refereegranskat)abstract Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensitybased registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based registration method for affine registration is presented. The algorithm constructs an abstract representation of the entire atlas set, an uberatlas, through clustering of features that are similar and detected consistently through the atlas set. This is done offline. At runtime only the feature clusters are matched to the target image, simultaneously yielding robust correspondences to all atlases in the atlas set from which the affine transformations can be estimated efficiently. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding gold standards. Our approach succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature-based, and significantly reduces the running times.
11.	Arnab, Anurag, et al. (författare) Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction 2018 Ingår i: IEEE Signal Processing Magazine. - 1558-0792 .- 1053-5888. ; 35:1, s. 37-52 Tidskriftsartikel (refereegranskat)abstract Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions.
12.	Arvidsson, Jonathan, et al. (författare) Image Fusion of Reconstructed Digital Tomosynthesis Volumes From a Frontal and a Lateral Acquisition 2016 Ingår i: Radiation protection dosimetry. - : Oxford University Press (OUP). - 1742-3406 .- 0144-8420. ; 169:1-4, s. 410-415 Tidskriftsartikel (refereegranskat)abstract Digital tomosynthesis (DTS) has been used in chest imaging as a low radiation dose alternative to computed tomography (CT). Traditional DTS shows limitations in the spatial resolution in the out-of-plane dimension. As a first indication of whether a dual-plane dual-view (DPDV) DTS data acquisition can yield a fair resolution in all three spatial dimensions, a manual registration between a frontal and a lateral image volume was performed. An anthropomorphic chest phantom was scanned frontally and laterally using a linear DTS acquisition, at 120 kVp. The reconstructed image volumes were resampled and manually co-registered. Expert radiologist delineations of the mediastinal soft tissues enabled calculation of similarity metrics in regard to delineations in a reference CT volume. The fused volume produced the highest total overlap, implying that the fused volume was a more isotropic 3D representation of the examined object than the traditional chest DTS volumes.
13.	Arvidsson, Jonathan, et al. (författare) Regularized k-means clustering for segmentation of brain tissues using hemodynamic features in DSC-MRI 2018 Ingår i: International Society for Magnetic Resonance in Medicine, Paris 16 - 21 2018. Konferensbidrag (övrigt vetenskapligt/konstnärligt)
14.	Ask, Erik, et al. (författare) Optimal Geometric Fitting Under the Truncated L-2-Norm 2013 Ingår i: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - 1063-6919. ; , s. 1722-1729 Konferensbidrag (refereegranskat)abstract This paper is concerned with model fitting in the presence of noise and outliers. Previously it has been shown that the number of outliers can be minimized with polynomial complexity in the number of measurements. This paper improves on these results in two ways. First, it is shown that for a large class of problems, the statistically more desirable truncated L-2-norm can be optimized with the same complexity. Then, with the same methodology, it is shown how to transform multi-model fitting into a purely combinatorial problem-with worst-case complexity that is polynomial in the number of measurements, though exponential in the number of models. We apply our framework to a series of hard registration and stitching problems demonstrating that the approach is not only of theoretical interest. It gives a practical method for simultaneously dealing with measurement noise and large amounts of outliers for fitting problems with low-dimensional models.
15.	Ask, Erik, et al. (författare) Tractable and Reliable Registration of 2D Point Sets 2014 Ingår i: Lecture Notes in Computer Science (Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I). - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783319105895 - 9783319105901 ; 8689, s. 393-406 Konferensbidrag (refereegranskat)abstract This paper introduces two new methods of registering 2D point sets over rigid transformations when the registration error is based on a robust loss function. In contrast to previous work, our methods are guaranteed to compute the optimal transformation, and at the same time, the worst-case running times are bounded by a low-degree polynomial in the number of correspondences. In practical terms, this means that there is no need to resort to ad-hoc procedures such as random sampling or local descent methods that cannot guarantee the quality of their solutions. We have tested the methods in several different settings, in particular, a thorough evaluation on two benchmarks of microscopic images used for histologic analysis of prostate cancer has been performed. Compared to the state-of-the-art, our results show that the methods are both tractable and reliable despite the presence of a significant amount of outliers.
16.	Boykov, Yuri, et al. (författare) Guest Editorial: Energy Optimization Methods 2013 Ingår i: International Journal of Computer Vision. - : Springer Science and Business Media LLC. - 1573-1405 .- 0920-5691. ; 104:3, s. 221-222 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
17.	Brandon, Daniel, et al. (författare) Fire Safe implementation of visible mass timber in tall buildings – compartment fire testing 2021 Rapport (övrigt vetenskapligt/konstnärligt)abstract Five real scale fire tests of compartments constructed of cross-laminated timber (CLT) and glued laminated timber, compliant with product standards specified in current US model building code, were performed. Four of the tested compartments were designed to result in a representative and severe fire scenario in a residential fire compartment, using a probabilistic approach. The other tested compartment had additional openings and a greater opening factor, which was aimed to be representative of buildings designed for business occupancy. The interior of the compartments had surface areas of exposed mass timber that varied from approximately the area of the floor plan to approximately two times the area of the floor plan. The tests included measurements to study the internal compartment exposure, the temperature development at gypsum protected surfaces, the temperature development in the structural timber, oxygen concentrations at locations of interest and exposure to exterior surfaces of the wall and façade above the openings. The fire in the compartment with a greater opening factor had two layers of fire-rated gypsum board protection on the back wall and all other surfaces of CLT and glued laminated timber exposed. Despite having the highest peak combustion rate, this compartment fire had the least severe internal and external fire exposure. The fire decayed relatively quickly after flashover and continued to decay until the test was stopped at 4 hours after ignition. This fire resulted in less structural damage than the fires in compartments with fewer and smaller openings. The compartments with fewer and smaller openings had similar temperatures for approximately the first 10 minutes after flashover. The compartment with only the ceiling (including the glued laminated timber beam) exposed started to decay after 22 minutes of post-flashover fire and continued to decay until the end of the test at 4 hours after ignition. The other three tests had, in addition to the ceiling, significant areas of exposed wall and column surfaces. To accommodate for the extended fire duration that was expected in these configurations an extra layer of gypsum board protection was applied to the protected surfaces. The additional exposed surface areas of walls led to an increase of the fully developed fire duration by 6 - 9 minutes. One of the compartments included corners where two exposed walls intersect. Significantly increased damage was observed in the lower part of these wall corners, and an overall higher radiative exposure in the test with such corners. After more than three hours of decay, surface flaming developed on the walls in that test. The fires in the tests without such corners exhibited continual decay for the full 4-hour test duration. Post-test analysis showed that the structural damage was lower in exposed ceilings than at the bottom of the exposed walls for all tests. After the tests, remaining smoldering and hot spots were reduced using relatively small amounts of water mist. Overnight measurements to study the thermal wave going through the loadbearing structure indicated no post-test reduction of structural capacity.
18.	Brandon, Daniel, et al. (författare) Fire Safety of CLT Buildings with Ex-posed Wooden Surfaces : Summary Report 2020 Rapport (övrigt vetenskapligt/konstnärligt)abstract Five real scale compartment fire tests, constructed of CLT slabs and glulam beam and column in accordance with current US product standards, were performed. The compartments had surface areas of exposed mass timber equal to up to two times the area of the floor plan. The 4 hours long tests showed that compartments with such quantities of exposed wood can exhibit continuous decay to hot-spots and embers after flashover. The tests indicate that the presence of two exposed wall surfaces in one corner should be avoided to ensure this.
19.	Brandon, Daniel, et al. (författare) Post-Fire Rehabilitation of CLT 2021 Rapport (övrigt vetenskapligt/konstnärligt)abstract Engineered mass timber materials such as CLT have been increasingly implemented as a structural material for tall or larger buildings in recent years. Most studies have been conducted on the structural performance of timber exposed to fire, but the number of studies focusing on post-fire rehabilitation of mass timber have been limited. As increasingly large timber buildings are being realized, for insurance purposes it becomes increasingly important to ensure that a building can be repaired after a fire. This report presents a case study of the repair of a section of a CLT ceiling after a significant fire. The specimen is obtained from a recent compartment fire test and is positioned and oriented in a way that is representative for on site rehabilitation. The repair was done in six steps: 1. Mapping the thickness of the charred or damaged layer 2. Design and planning 3. Removal of the char layer 4. Planing of the surface including corners 5. Gluing procedure of replacing lamella 6. Finish the surface to meet architectural requirement A new method for determining the grade of damage, the method for planing the specimen, the adhesive type, the glue pressing methods were designed for the rehabilitation exercise. In addition, the layup of the CLT is changed to prioritise flexural stiffness and bending capacity over shear capacity, as they generally govern the structural capacity of CLT floors. After the six-step repair was done, the specimen was cut in half to perform two similar structural bending tests. The results indicate that the flexural stiffness which is generally governing the load bearing capacity of floors, is fully restored by the rehabilitation work. The results also indicate that bending capacity, which can be governing for relatively short floor spans, is restored and possibly increased by the rehabilitation work. The shear capacity which is only critical for short floor spans in combination with very high loads is, however, reduced, as the experimental shear capacity is 18% lower than the characteristic shear capacity.
20.	Brynte, Lucas, 1990, et al. (författare) On the Tightness of Semidefinite Relaxations for Rotation Estimation 2022 Ingår i: Journal of Mathematical Imaging and Vision. - : Springer Science and Business Media LLC. - 1573-7683 .- 0924-9907. ; 64:1, s. 57-67 Tidskriftsartikel (refereegranskat)abstract Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note that there are few failure cases reported in the literature, in particular for estimation problems with a single rotation, motivating us to gain further theoretical understanding. A general framework based on tools from algebraic geometry is introduced for analyzing the power of semidefinite relaxations of problems with quadratic objective functions and rotational constraints. Applications include registration, hand–eye calibration, and rotation averaging. We characterize the extreme points and show that there exist failure cases for which the relaxation is not tight, even in the case of a single rotation. We also show that some problem classes are always tight given an appropriate parametrization. Our theoretical findings are accompanied with numerical simulations, providing further evidence and understanding of the results.
21.	Brynte, Lucas, 1990, et al. (författare) Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors 2020 Ingår i: 31st British Machine Vision Conference, BMVC 2020. Konferensbidrag (refereegranskat)abstract In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
22.	Brynte, Lucas, 1990, et al. (författare) Rigidity preserving image transformations and equivariance in perspective 2023 Ingår i: Image analysis. - Cham : Springer Nature. - 9783031314384 - 9783031314377 ; 13886 LNCS, s. 59-76 Konferensbidrag (refereegranskat)abstract We characterize the class of image plane transformations which realize rigid camera motions and call these transformations ‘rigidity preserving’. It turns out that the only rigidity preserving image transformations are homographies corresponding to rotating the camera. In particular, 2D translations of pinhole images are not rigidity preserving. Hence, when using CNNs for 3D inference tasks, it can be beneficial to modify the inductive bias from equivariance w.r.t. translations to equivariance w.r.t. rotational homographies. We investigate how equivariance with respect to rotational homographies can be approximated in CNNs, and test our ideas on 6D object pose estimation. Experimentally, we improve on a competitive baseline.
23.	Bylow, Erik, et al. (författare) Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models 2019 Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 261-274 Konferensbidrag (refereegranskat)abstract In recent years, great progress has been made on the problem of 3D scene reconstruction using depth sensors. On a large scale, these reconstructions look impressive, but often many fine details are lacking due to limitations in the sensor resolution. In this paper we combine two well-known principles for recovery of 3D models, namely fusion of depth images with photometric stereo to enhance the details of the reconstructions. We derive a simple and transparent objective functional that takes both the observed intensity images and depth information into account. The experimental results show that many details are captured that are not present in the input depth images. Moreover, we provide a quantitative evaluation that confirms the improvement of the resulting 3D reconstruction using a 3D printed model.
24.	Bylow, Erik, et al. (författare) Direct Camera Pose Tracking and Mapping With Signed Distance Functions 2013 Konferensbidrag (refereegranskat)abstract In many areas, the ability to create accurate 3D models is of great interest, for example, in computer vision, robotics, architecture, and augmented reality. In this paper we show how a textured indoor environment can be reconstructed in 3D using an RGB-D camera. Real-time performance can be achieved using a GPU. We show how the camera pose can be estimated directly using the geometry that we represent as a signed distance function (SDF). Since the SDF contains information about the distance to the surface, it defines an error-metric which is minimized to estimate the pose of the camera. By iteratively estimating the camera pose and integrating the new depth images into the model, the 3D reconstruction is computed on the fly. We present several examples of 3D reconstructions made from a handheld and robot-mounted depth sensor, including detailed reconstructions from medium-sized rooms with almost drift-free pose estimation. Furthermore, we demonstrate that our algorithm is robust enough for 3D reconstruction using data recorded from a quadrocopter, making it potentially useful for navigation applications.
25.	Bylow, Erik, et al. (författare) Minimizing the maximal rank 2016 Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 1063-6919. - 9781467388511 ; 2016-January, s. 5887-5895 Konferensbidrag (refereegranskat)abstract In computer vision, many problems can be formulated as finding a low rank approximation of a given matrix. Ideally, if all elements of the measurement matrix are available, this is easily solved in the L2-norm using factorization. However, in practice this is rarely the case. Lately, this problem has been addressed using different approaches, one is to replace the rank term by the convex nuclear norm, another is to derive the convex envelope of the rank term plus a data term. In the latter case, matrices are divided into sub-matrices and the envelope is computed for each subblock individually. In this paper a new convex envelope is derived which takes all sub-matrices into account simultaneously. This leads to a simpler formulation, using only one parameter to control the trade-of between rank and data fit, for applications where one seeks low rank approximations of multiple matrices with the same rank. We show in this paper how our general framework can be used for manifold denoising of several images at once, as well as just denoising one image. Experimental comparisons show that our method achieves results similar to state-of-the-art approaches while being applicable for other problems such as linear shape model estimation.
26.	Bylow, Erik, et al. (författare) Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions 2013 Ingår i: Robotics: Science and Systems (RSS), Online Proceedings. - 2330-765X. - 9789810739379 ; 9 Konferensbidrag (refereegranskat)abstract The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed distance function (SDF), the camera pose can be efficiently estimated by directly minimizing the error of the depth images on the SDF. As the SDF contains the distances to the surface for each voxel, the pose optimization can be carried out extremely fast. By iteratively estimating the camera poses and integrating the RGB-D data in the voxel grid, a detailed reconstruction of an indoor environment can be achieved. We present reconstructions of several rooms using a hand-held sensor and from onboard an autonomous quadrocopter. Our extensive evaluation on publicly available benchmark data shows that our approach is more accurate and robust than the iterated closest point algorithm (ICP) used by KinectFusion, and yields often a comparable accuracy at much higher speed to feature-based bundle adjustment methods such as RGB-D SLAM for up to medium-sized scenes.
27.	Bylow, Erik, et al. (författare) Robust Camera Tracking by Combining Color and Depth Measurements 2014 Ingår i: International Conference on Pattern Recognition. - 1051-4651. Konferensbidrag (refereegranskat)abstract One of the major research areas in computer vision is scene reconstruction from image streams. The advent of RGB-D cameras, such as the Microsoft Kinect, has lead to new possibilities for performing accurate and dense 3D reconstruction. There are already well-working algorithms to acquire 3D models from depth sensors, both for large and small scale scenes. However, these methods often break down when the scene geometry is not so informative, for example, in the case of planar surfaces. Similarly, standard image-based methods fail for texture-less scenes. We combine both color and depth measurements from an RGB-D sensor to simultaneously reconstruct both the camera motion and the scene geometry in a robust manner. Experiments on real data show that we can accurately reconstruct large-scale 3D scenes despite many planar surfaces.
28.	Bylow, Erik, et al. (författare) Robust online 3D reconstruction combining a depth sensor and sparse feature points 2016 Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. ; 0, s. 3709-3714, s. 3709-3714 Konferensbidrag (refereegranskat)abstract Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications like when the camera position and the 3D model must be created online in real-time.
29.	Bökman, Georg, 1994, et al. (författare) A case for using rotation invariant features in state of the art feature matchers 2022 Ingår i: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. - 2160-7516 .- 2160-7508. ; 2022-June, s. 5106-5115 Konferensbidrag (refereegranskat)abstract The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.
30.	Bökman, Georg, et al. (författare) In search of projectively equivariant networks 2023 Ingår i: Transactions on Machine Learning Research. - 2835-8856. Tidskriftsartikel (refereegranskat)abstract Equivariance of linear neural network layers is well studied. In this work, we relax the equivariance condition to only be true in a projective sense. Hereby, we introduce the topic of projective equivariance to the machine learning audience. We theoretically study the relation of projectively and linearly equivariant linear layers. We find that in some important cases, surprisingly, the two types of layers coincide. We also propose a way to construct a projectively equivariant neural network, which boils down to building a standard equivariant network where the linear group representations acting on each intermediate feature space are lifts of projective group representations. Projective equivariance is showcased in two simple experiments. Code for the experiments is provided in the supplementary material.
31.	Bökman, Georg, 1994, et al. (författare) Investigating how ReLU-networks encode symmetries 2023 Ingår i: Advances in Neural Information Processing Systems. - 1049-5258. ; 36 Konferensbidrag (refereegranskat)abstract Many data symmetries can be described in terms of group equivariance and the most common way of encoding group equivariances in neural networks is by building linear layers that are group equivariant. In this work we investigate whether equivariance of a network implies that all layers are equivariant. On the theoretical side we find cases where equivariance implies layerwise equivariance, but also demonstrate that this is not the case generally. Nevertheless, we conjecture that CNNs that are trained to be equivariant will exhibit layerwise equivariance and explain how this conjecture is a weaker version of the recent permutation conjecture by Entezari et al. [2022]. We perform quantitative experiments with VGG-nets on CIFAR10 and qualitative experiments with ResNets on ImageNet to illustrate and support our theoretical findings. These experiments are not only of interest for understanding how group equivariance is encoded in ReLU-networks, but they also give a new perspective on Entezari et al.'s permutation conjecture as we find that it is typically easier to merge a network with a group-transformed version of itself than merging two different networks.
32.	Bökman, Georg, 1994, et al. (författare) ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds 2022 Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - : IEEE Computer Society. - 1063-6919. ; 2022-June, s. 10966-10975 Konferensbidrag (refereegranskat)abstract In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision.
33.	Chandraker, Manmohan, et al. (författare) Autocalibration via Rank-Constrained Estimation of the Absolute Quadric 2007 Ingår i: [Host publication title missing]. - 1424411807 ; , s. 1-8 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
34.	Chandraker, Manmohan, et al. (författare) Reflections on the Generalized Bas-Relief Ambiguity 2005 Ingår i: ; , s. 788-795 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
35.	Chelani, Kunal, 1992, et al. (författare) How privacy-preserving are line clouds? Recovering scene details from 3D lines 2021 Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 1063-6919. ; , s. 15663-15673 Konferensbidrag (refereegranskat)abstract Visual localization is the problem of estimating the camera pose of a given image with respect to a known scene. Visual localization algorithms are a fundamental building block in advanced computer vision applications, including Mixed and Virtual Reality systems. Many algorithms used in practice represent the scene through a Structure-from-Motion (SfM) point cloud and use 2D-3D matches between a query image and the 3D points for camera pose estimation. As recently shown, image details can be accurately recovered from SfM point clouds by translating renderings of the sparse point clouds to images. To address the resulting potential privacy risks for user-generated content, it was recently proposed to lift point clouds to line clouds by replacing 3D points by randomly oriented 3D lines passing through these points. The resulting representation is unintelligible to humans and effectively prevents point cloud-to-image translation. This paper shows that a significant amount of information about the 3D scene geometry is preserved in these line clouds, allowing us to (approximately) recover the 3D point positions and thus to (approximately) recover image content. Our approach is based on the observation that the closest points between lines can yield a good approximation to the original 3D points. Code is available at https://github.com/kunalchelani/Line2Point.
36.	Chelani, Kunal, 1992, et al. (författare) Privacy-Preserving Representations are not Enough: Recovering Scene Content from Camera Poses 2023 Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 1063-6919. ; 2023-June, s. 13132-13141 Konferensbidrag (refereegranskat)abstract Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloudbased applications, privacy issues are becoming a very important aspect of the localization process. Existing work on privacy-preserving localization aims to defend against an attacker who has access to a cloud-based service. In this paper, we show that an attacker can learn about details of a scene without any access by simply querying a localization service. The attack is based on the observation that modern visual localization algorithms are robust to variations in appearance and geometry. While this is in general a desired property, it also leads to algorithms localizing objects that are similar enough to those present in a scene. An attacker can thus query a server with a large enough set of images of objects, e.g., obtained from the Internet, and some of them will be localized. The attacker can thus learn about object placements from the camera poses returned by the service (which is the minimal information returned by such a service). In this paper, we develop a proof-of-concept version of this attack and demonstrate its practical feasibility. The attack does not place any requirements on the localization algorithm used, and thus also applies to privacy-preserving representations. Current work on privacy-preserving representations alone is thus insufficient.
37.	Enqvist, Olof, et al. (författare) A Brute-Force Algorithm for Reconstructing a Scene from Two Projections 2011 Ingår i: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011. - 1063-6919. ; , s. 2961-2968 Konferensbidrag (refereegranskat)abstract Is the real problem in finding the relative orientation of two viewpoints the correspondence problem? We argue that this is only one difficulty. Even with known correspondences, popular methods like the eight point algorithm and minimal solvers may break down due to planar scenes or small relative motions. In this paper, we derive a simple, brute-force algorithm which is both robust to outliers and has no such algorithmic degeneracies. Several cost functions are explored including maximizing the consensus set and robust norms like truncated least-squares. Our method is based on parameter search in a four-dimensional space using a new epipolar parametrization. In principle, we do an exhaustive search of parameter space, but the computations are very simple and easily parallelizable, resulting in an efficient method. Further speedups can be obtained by restricting the domain of possible motions to, for example, planar motions or small rotations. Experimental results are given for a variety of scenarios including scenes with a large portion of outliers. Further, we apply our algorithm to 3D motion segmentation where we outperform state-of-the-art on the well-known Hopkins-155 benchmark database.
38.	Enqvist, Olof, et al. (författare) Global Optimization for One-Dimensional Structure and Motion Problems 2010 Ingår i: SIAM Journal of Imaging Sciences. - : Society for Industrial & Applied Mathematics (SIAM). - 1936-4954. ; 3:4, s. 1075-1095 Tidskriftsartikel (refereegranskat)abstract We study geometric reconstruction problems in one-dimensional retina vision. In such problems, the scene is modeled as a two-dimensional plane, and the camera sensor produces one-dimensional images of the scene. Our main contribution is an efficient method for computing the global optimum to the structure and motion problem with respect to the L-infinity norm of the reprojection errors. One-dimensional cameras have proven useful in several applications, most prominently for autonomous vehicles, where they are used to provide inexpensive and reliable navigational systems. Previous results on one-dimensional vision are limited to the classification and solving of minimal cases, bundle adjustment for finding local optima, and linear algorithms for algebraic cost functions. In contrast, we present an approach for finding globally optimal solutions with respect to the L-infinity norm of the angular reprojection errors. We show how to solve intersection and resection problems as well as the problem of simultaneous localization and mapping (SLAM). The algorithm is robust to use when there are missing data, which means that all points are not necessarily seen in all images. Our approach has been tested on a variety of different scenarios, both real and synthetic. The algorithm shows good performance for intersection and resection and for SLAM with up to five views. For more views the high dimension of the search space tends to give long running times. The experimental section also gives interesting examples showing that for one-dimensional cameras with limited field of view the SLAM problem is often inherently ill-conditioned.
39.	Enqvist, Olof, et al. (författare) Non-Sequential Structure from Motion 2011 Konferensbidrag (refereegranskat)abstract Prior work on multi-view structure from motion is dominated by sequential approaches starting from a single two-view reconstruction, then adding new images one by one. In contrast, we propose a non-sequential methodology based on rotational consistency and robust estimation using convex optimization. The resulting system is more robust with respect to (i) unreliable two-view estimations caused by short baselines, (ii) repetitive scenes with locally consistent structures that are not consistent with the global geometry and (iii) loop closing as errors are not propagated in a sequential manner. Both theoretical justifications and experimental comparisons are given to support these claims.
40.	Enqvist, Olof, et al. (författare) Optimal Correspondences from Pairwise Constraints 2009 Ingår i: IEEE International Conference on Computer Vision. - 1550-5499. - 9781424444199 ; , s. 1295-1302 Konferensbidrag (refereegranskat)abstract Correspondence problems are of great importance in computer vision. They appear as subtasks in many applications such as object recognition, merging partial 3D reconstructions and image alignment. Automatically matching features from appearance only is difficult and errors are frequent. Thus, it is necessary to use geometric consistency to remove incorrect correspondences. Typically heuristic methods like RANSAC or EM-like algorithms are used, but they risk getting trapped in local optima and are in no way guaranteed to ﬁnd the best solution. This paper illustrates how pairwise constraints in combination with graph methods can be used to efficiently ﬁnd optimal correspondences. These ideas are implemented on two basic geometric problems, 3D-3D registration and 2D-3D registration. The developed scheme can handle large rates of outliers and cope with multiple hypotheses. Despite the combinatorial explosion, the resulting algorithm which has been extensively evaluated on real data, yields competitive running times compared to state of the art
41.	Enqvist, Olof, et al. (författare) Robust Fitting for Multiple View Geometry 2012 Ingår i: Lecture Notes in Computer Science (Computer Vision - ECCV 2012, Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Part I ). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642337178 - 9783642337185 ; 7572, s. 738-751 Konferensbidrag (refereegranskat)abstract How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the num- ber of outliers can be found with an algorithm that has polynomial time- complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L2 -norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required condi- tions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.
42.	Enqvist, Olof, et al. (författare) Robust Optimal Pose Estimation 2008 Ingår i: Computer Vision – ECCV 2008 (Lecture Notes in Computer Science). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783540886815 ; 5302, s. 141-153 Konferensbidrag (refereegranskat)abstract We study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. A common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3D points. Existing techniques Such as RANSAC to handle outliers have no guarantee of optimality. In contrast, we work with a natural extension of the L-infinity norm to the outlier case. Using a simple result from classical geometry, we derive necessary conditions for L-infinity optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. The algorithm has been evaluated on synthetic as well as real data showing good empirical performance. In addition, for cases with no outliers, we demonstrate shorter execution times than existing optimal algorithms.
43.	Enqvist, Olof, et al. (författare) Tractable Algorithms for Robust Model Estimation 2015 Ingår i: International Journal of Computer Vision. - : Springer Science and Business Media LLC. - 1573-1405 .- 0920-5691. ; 112:1, s. 115-129 Tidskriftsartikel (refereegranskat)abstract What is the computational complexity of geometric model estimation in the presence of noise and outliers? We show that the number of outliers can be minimized in polynomial time with respect to the number of measurements, although exponential in the model dimension. Moreover, for a large class of problems, we prove that the statistically more desirable truncated L2-norm can be optimized with the same complexity. In a similar vein, it is also shown how to transform a multi-model estimation problem into a purely combinatorial one—with worst-case complexity that is polynomial in the number of measurements but exponential in the number of models. We apply our framework to a series of hard fitting problems. It gives a practical method for simultaneously dealing with measurement noise and large amounts of outliers in the estimation of low-dimensional models. Experimental results and a comparison to random sampling techniques are presented for the applications rigid registration, triangulation and stitching.
44.	Eriksson, Anders P, et al. (författare) Efficiently Solving the Fractional Trust Region Problem 2007 Ingår i: ; , s. 796-805 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
45.	Eriksson, Anders P, et al. (författare) Image segmentation with context 2007 Ingår i: Proceedings 15th Scandinavian Image Analysis Conference. - Berlin, Heidelberg : Springer Berlin Heidelberg. ; 4522, s. 283-292 Konferensbidrag (refereegranskat)abstract We present a technique for simultaneous segmentation and classification of image partitions using combinatorial optimization techniques. By combining existing image segmentation approaches with simple learning techniques we show how prior knowledge can be incorporated into the visual grouping process through the formulation of a quadratic binary optimization problem. We further show how such to efficiently solve such problems through relaxation techniques and trust, region methods. This has resulted in an method that partitions images into a number of disjoint regions based on previously learned example segmentations. Preliminary experimental results are also presented in support of our suggested approach.
46.	Eriksson, Anders P, et al. (författare) Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints 2011 Ingår i: Journal of Mathematical Imaging and Vision. - : Springer Science and Business Media LLC. - 0924-9907 .- 1573-7683. ; 39:1, s. 45-61 Tidskriftsartikel (refereegranskat)abstract Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these methods is that they are noticeably limited in the type of constraints that can be incorporated and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle linear equality constraints for an arbitrary number of classes. This is done by restating the problem and showing how linear constraints can be enforced exactly in the optimization scheme through duality. This allows us to add group priors, for example, that certain pixels should belong to a given class. In addition, it provides a principled way to perform multi-class segmentation for tasks like interactive segmentation. The method has been tested on real data showing good performance and improvements compared to standard normalized cuts.
47.	Eriksson, Anders P, et al. (författare) Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints 2007 Ingår i: ; , s. 1024-1031 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
48.	Eriksson, Anders, 1972, et al. (författare) Rotation Averaging and Strong Duality 2018 Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 1063-6919. - 9781538664209 ; , s. 127-135 Konferensbidrag (refereegranskat)abstract In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that out-performs general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data.
49.	Eriksson, Anders, et al. (författare) Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality 2021 Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 43:1, s. 256-268 Tidskriftsartikel (refereegranskat)abstract In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that outperforms general purpose numerical solvers by a large margin and compares favourably to current state-of-the-art. Further, our approach is able to handle the large problem instances commonly occurring in structure from motion settings and it is trivially parallelizable. Experiments are presented for a number of different instances of both synthetic and real-world data.
50.	Fejne, Frida, 1986, et al. (författare) Multiatlas Segmentation Using Robust Feature-Based Registration 2017 Ingår i: , Cloud-Based Benchmarking of Medical Image Analysis. - Cham : Springer International Publishing. - 9783319496429 ; , s. 203-218 Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract This paper presents a pipeline which uses a multiatlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted feature-based method which uses organ-specific features. These features are learnt during an offline preprocessing step, and thus, the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC followed by a thin-plate spline refinement or NiftyReg. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally, the segmentation is obtained using graph cuts with a Potts model as interaction term. Our pipeline was evaluated on 20 organs in 10 whole-body CT images at the VISCERAL Anatomy Challenge, in conjunction with the International Symposium on Biomedical Imaging, Brooklyn, New York, in April 2015. It performed best on majority of the organs, with respect to the Dice index.

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