nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of 3d motion from stereo images - differential and discrete formulations

In this paper we analyze the problem of motion estimation from a sequence of stereo images. We formulate both the differential and discrete approaches of two methods. The differential approach uses differential optical flow whereas the discrete approaches uses feature correspondences. We use both methods to compute, first, the 3D velocity in the Z direction and, second, the complete motion parameters. The methods were extensively tested using synthetic images as well as real images and several conclusions are drawn from the results. We point out the critical factors for the methods. The real images are used without any illumination control of the scene in order to analyze the behavior of the methods in strongly noisy environments and low resolution depth maps.
Rigid motion estimation from non-central catadioptric images

This work addresses the problem of rigid motion estimation and 3D reconstruction in vision systems where it is possible to recover the incident direction from image points. Such systems include pinhole cameras and catadioptric cameras. Given two images of the same scene acquired from two positions, the transformation is estimated by means of an iterative process. The estimation process aims at having corresponding incident rays intersecting at the same 3D point. Geometrical relationships are derived to support the estimation method. Experiments with real images are presented.
Control issues to improve visual control of motion: applications in active tracking of moving targets

This paper deals with active tracking of 3D moving targets. Tracking is based on different visual behaviors, namely smooth pursuit and vergence control. The performance and robustness in visual control of motion depends both on the vision algorithms and the control structure. In this work we evaluate these two aspects, characterize the delays, and discuss ways to cope with latency while improving system performance. Kalman filtering is used to achieve smooth behaviors and increase visual processing robustness. A specific Kalman filter structure is proposed and its tuning and initialization are discussed. Delays and system latencies substantially affect the performance of visually guided systems. Interpolation is used to cope with visual processing delays. Model predictive control strategies are proposed to compensate for the mechanical latency in visual control of motion.
Active Stereo Tracking of $N\le 3$ Targets Using Line Scan Cameras

This paper presents a general approach for the simultaneous tracking of multiple moving targets using a generic active stereo setup. The problem is formulated on the plane, where cameras are modeled as “line scan cameras,” and targets are described as points with unconstrained motion. We propose to control the active system parameters in such a manner that the images of the targets in the two views are related by a homography. This homography is specified during the design stage and, thus, can be used to implicitly encode the desired tracking behavior. Such formulation leads to an elegant geometric framework that enables a systematic and thorough analysis of the problem at hand. The benefits of the approach are illustrated by applying the framework to two distinct stereo configurations. In the first case, we assume two pan-tilt-zoom cameras, with rotation and zoom control, which are arbitrarily placed in the working environment. It is proved that such a stereo setup can track up to N = 3 free-moving targets, while assuring that the image location of each target is the same for both views. The second example considers a robot head with neck pan motion and independent eye rotation. For this case, it is shown that it is not possible to track more than N = 2 targets because of the lack of zoom. The theoretical framework is used to derive the control equations, and the implementation of the tracking behavior is described in detail. The correctness of the results is confirmed through simulations and real tracking experiments.
Depth perception using focus with hand-eye robotic system

The authors explore focus to obtain depth or structure perception of the world. They propose to vary the degree of focusing by moving the camera with respect to the object position. In this case the camera is attached to the tool of a manipulator in a hand-eye configuration with the position of the camera always known. This approach ensures that the focused areas of the images are always subjected to the same magnification. To measure the focus quality, operators are used to quantify the high-frequency content of the image. Different types of operators were tested and the results compared.<<ETX>>
Pose estimation for non-central cameras using planes

In this paper we study pose estimation for non-central cameras, using planes. The method proposed uses non-minimal data. Using the homography matrix to represent the transformation between the world and camera coordinate systems, we describe a non-iterative algorithm for pose estimation. In addition, we propose a parameter optimization to refine the pose estimate. We evaluate the proposed solutions against the state-of-the-art method in terms of both robustness to noise and computation time. From the experiments, we conclude that the proposed method is more accurate against noise. We also conclude that the numerical results obtained with this method improve with increasing number of data points. In terms of processing speed both versions of the algorithm presented are faster than the state-of-the-art algorithm.
SDP-based approach to monocular reconstruction of inextensible surfaces

This study addresses the problem of monocular reconstruction of surfaces that deform isometrically, using points tracked in a single image. To tackle this problem, a flat three-dimensional (3D) shape of the surface and its image are used as template. Such deformations are characterised by certain geometric constraints and to reconstruct the surfaces, these constraints have to be properly exploited. Therefore, the authors propose an algebraic formula that aims at the joint expression of the geometric constraints, namely those based on the differential properties and also those based on the upper-bound model. This expression is, in fact, a unique formulation that results from integrating these two types of constraints, and leads to the intended reconstructions, even when the surface is not strictly isometric. The template shape is used to set the parameters of the expression, which is then optimised (along with the projection equations) by means of a semi-definite programming (SDP) problem. This optimisation enables the estimation of 3D positions of the points on the surface. However, and for implementation purposes, this optimisation is applied separately to patches which, together, make up the whole surface. The experimental results show that the proposed approach improves the results from other methods in terms of accuracy.
Geometric properties of central catadioptric line images and their application in calibration

In central catadioptric systems lines in a scene are projected to conic curves in the image. This work studies the geometry of the central catadioptric projection of lines and its use in calibration. It is shown that the conic curves where the lines are mapped possess several projective invariant properties. From these properties, it follows that any central catadioptric system can be fully calibrated from an image of three or more lines. The image of the absolute conic, the relative pose between the camera and the mirror, and the shape of the reflective surface can be recovered using a geometric construction based on the conic loci where the lines are projected. This result is valid for any central catadioptric system and generalizes previous results for paracatadioptric sensors. Moreover, it is proven that systems with a hyperbolic/elliptical mirror can be calibrated from the image of two lines. If both the shape and the pose of the mirror are known, then two line images are enough to determine the image of the absolute conic encoding the camera's intrinsic parameters. The sensitivity to errors is evaluated and the approach is used to calibrate a real camera.
Improving camera calibration by using multiple frames in hand-eye robotic systems

Addresses the geometric modelling aspects related with the use of a pair of video cameras mounted on a six degrees-of-freedom manipulator in an hand-eye configuration. The information obtained by these sensors shall be used to generate the depth map of the environment where the manipulator works. The procedure to determining the image formation parameters is known by camera calibration and a method to optimize these calibration parameters is presented. After presenting the calibration technique used, the authors develop a calibration matrix invariant to movement. The optimization process is applied to this matrix to obtain a better performance in the numerical data.<<ETX>>
Real-time active visual surveillance by integrating peripheral motion detection with foveated tracking

In this paper we describe an active binocular tracking system integrating peripheral motion detection. The system is made up of a binocular active system used to track the objects and a fixed camera providing wide angle images of the environment. The system can cope with changes in lighting conditions by adjusting aperture and focus. Binocular flow enables tracking of nonrigid objects even when partial occlusion occurs.
Active stereo tracking of multiple free-moving targets

This article presents a general approach for the active stereo tracking of multiple moving targets. The problem is formulated on the plane, where cameras are modeled as “line scan cameras” and targets are described as points with unconstrained motion. We propose to control the active system parameters in such a manner that the images of the targets in the two views are related by an homography. This homography is specified during the design stage and implicitly encodes the tracking behavior. It is shown that this formulation leads to an elegant geometric framework that enables to decide about the feasibility of a particular active tracking task. We apply it to prove that two cameras with rotation and zoom control, can track up to three moving targets, while assuring that the image location of each target is the same for both views. In addition, the framework is also useful for devising tracking strategies and deriving the required control equations. This feature is illustrated through a real experiment on tracking two independent targets using a binocular stereo head.
Statistical emission image reconstruction for randoms-precorrected PET scans using negative sinogram values

Many conventional PET emission scans are corrected for accidental coincidence (AC) events, or randoms, by real-time subtraction of delayed-window coincidences, leaving only the randoms-precorrected data available for image reconstruction. The real-time precorrection compensates in mean for AC events but destroys Poisson statistics. Since the exact log-likelihood for randoms-precorrected data is inconvenient to maximize, practical approximations are desirable for statistical image reconstruction. Conventional approximations involve setting negative sinogram values to zero, which can induce positive systematic biases, particularly for scans with low counts per ray. We propose new likelihood approximations that allow negative sinogram values without requiring zero-thresholding. We also develop monotonic algorithms for the new models by using "optimization transfer" principles. Simulation results show that our new model, SP/sup -/, is free of systematic bias yet keeps low variance. Despite its simpler implementation, the new model performs comparably to the saddle-point (SD) model which has previously shown the best performance (as to systematic bias and variance) in randoms-precorrected PET emission reconstruction.
Image Reconstruction is a New Frontier of Machine Learning

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme “Deep learning in medical imaging” [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.
Regularization Designs for Uniform Spatial Resolution and Noise Properties in Statistical Image Reconstruction for 3-D X-ray CT

Statistical image reconstruction methods for X-ray computed tomography (CT) provide improved spatial resolution and noise properties over conventional filtered back-projection (FBP) reconstruction, along with other potential advantages such as reduced patient dose and artifacts. Conventional regularized image reconstruction leads to spatially variant spatial resolution and noise characteristics because of interactions between the system models and the regularization. Previous regularization design methods aiming to solve such issues mostly rely on circulant approximations of the Fisher information matrix that are very inaccurate for undersampled geometries like short-scan cone-beam CT. This paper extends the regularization method proposed in  to 3-D cone-beam CT by introducing a hypothetical scanning geometry that helps address the sampling properties. The proposed regularization designs were compared with the original method in  with both phantom simulation and clinical reconstruction in 3-D axial X-ray CT. The proposed regularization methods yield improved spatial resolution or noise uniformity in statistical image reconstruction for short-scan axial cone-beam CT.
Penalized-likelihood estimators and noise analysis for randoms-precorrected PET transmission scans

This paper analyzes and compares image reconstruction methods based on practical approximations to the exact log likelihood of randoms precorrected positron emission tomography (PET) measurements. The methods apply to both emission and transmission tomography, however, in this paper the authors focus on transmission tomography. The results of experimental PET transmission scans and variance approximations demonstrate that the shifted Poisson (SP) method avoids the systematic bias of the conventional data-weighted least squares (WLS) method and leads to significantly lower variance than conventional statistical methods based on the log likelihood of the ordinary Poisson (OF) model. The authors develop covariance approximations to analyze the propagation of noise from attenuation maps into emission images via the attenuation correction factors (ACF's). Empirical pixel and region variances from real transmission data agree closely with the analytical predictions. Both the approximations and the empirical results show that the performance differences between the OP model and SP model are even larger, when considering noise propagation from the transmission images into the final emission images, than the differences in the attenuation maps themselves.
Minimax Emission Computed Tomography using High-Resolution Anatomical Side Information and B-Spline Models

In this paper a minimax methodology is presented for combining information from two imaging modalities having different intrinsic spatial resolutions. The focus application is emission computed tomography (ECT), a low-resolution modality for reconstruction of radionuclide tracer density, when supplemented by high-resolution anatomical boundary information extracted from a magnetic resonance image (MRI) of the same imaging volume. The MRI boundary within the two-dimensional (2-D) slice of interest is parameterized by a closed planar curve. The Cramer-Rao (CR) lower bound is used to analyze estimation errors for different boundary shapes. Under a spatially inhomogeneous Gibbs field model for the tracer density a representation for the minimax MRI-enhanced tracer density estimator is obtained. It is shown that the estimator is asymptotically equivalent to a penalized maximum likelihood (PML) estimator with resolution-selective Gibbs penalty. Quantitative comparisons are presented using the iterative space alternating generalized expectation maximization (SAGE-FM) algorithm to implement the PML estimator with and without minimax weight averaging.
Pragmatic image reconstruction for the MiCES Fully-3D mouse imaging PET scanner

We present a pragmatic approach to image reconstruction for data from the MiCES fully-3D mouse imaging PET scanner under construction at the University of Washington. Our approach is modeled on fully-3D image reconstruction used in clinical PET scanners, which is based on Fourier rebinning (FORE) followed by 2D iterative image reconstruction. The use of iterative methods allows modeling the effects of statistical noise and attenuation etc., while FORE accelerates the reconstruction process by reducing the fully-3D data to a stacked set of independent 2D sinograms. Preliminary investigations have indicated that nonstationary detector point-spread response effects, which are ignored for clinical imaging, significantly impact image quality for the MiCES scanner geometry. To model the effect of nonstationary detector point spread response, we have added a factorized system matrix to the ASPIRE reconstruction library. The current implementation uses FORE+AWOSEM followed by postreconstruction 3D Gaussian smoothing. The results indicate that the proposed approach produces a dramatic improvement in resolution without undue increases in noise.
Regularized fieldmap estimation in MRI

In fast MR imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard fieldmap estimation methods yield noisy fieldmaps, particularly in image regions having low spin density. This paper describes regularized methods for fieldmap estimation. These methods exploit the fact that fieldmaps are smooth functions. Efficient convergent algorithms are given even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of fieldmap estimates relative to conventional unregularized methods
Analytical approach to regularization design for isotropic spatial resolution

In emission tomography, conventional quadratic regularization methods lead to nonuniform and anisotropic spatial resolution in penalized-likelihood (or MAP) reconstructed images, even for idealized shift-invariant imaging systems. Previous methods for designing regularizers that improve resolution uniformity have used matrix manipulations and discrete Fourier transforms. This paper describes a simpler approach for designing data-dependent, shift-variant regularizers. We replace the usual discrete system models used in statistical image reconstruction with locally shift-invariant, continuous-space approximations, and design the regularizer using analytical Fourier transforms. We discretize the final analytical solution to compute the regularizer coefficients. This new approach requires even less computation than previous approaches that used FFTs, and provides additional insight into the problem of designing regularization methods to achieve uniform, isotropic spatial resolution.
Alternating Dual Updates Algorithm for X-ray CT Reconstruction on the GPU

Model-based image reconstruction (MBIR) for X-ray computed tomography (CT) offers improved image quality and potential low-dose operation, but has yet to reach ubiquity in the clinic. MBIR methods form an image by solving a large statistically motivated optimization problem, and the long time it takes to numerically solve this problem has hampered MBIR's adoption. We present a new optimization algorithm for X-ray CT MBIR based on duality and group coordinate ascent that may converge even with approximate updates and can handle a wide range of regularizers, including total variation (TV). The algorithm iteratively updates groups of dual variables corresponding to terms in the cost function; these updates are highly parallel and map well onto the GPU. Although the algorithm stores a large number of variables, the “working size” for each of the algorithm's steps is small and can be efficiently streamed to the GPU while other calculations are being performed. The proposed algorithm converges rapidly on both real and simulated data and shows promising parallelization over multiple devices.
Model-Based Image Reconstruction for MRI

Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging modality. The inverse FFT has served the MR community very well as the conventional image reconstruction method for k-space data with full Cartesian sampling. And for well sampled non-Cartesian data, the gridding method with appropriate density compensation factors is fast and effective. But when only under-sampled data is available, or when non-Fourier physical effects like field inhomogeneity are important, then gridding/FFT methods for image reconstruction are suboptimal, and iterative algorithms based on appropriate models can improve image quality, rat the price of increased computation. This article reviews the use of iterative algorithms for model-based MR image reconstruction.
Alternating Direction Method of Multiplier for Tomography With Nonlocal Regularizers

The ordered subset expectation maximization (OSEM) algorithm approximates the gradient of a likelihood function using a subset of projections instead of using all projections so that fast image reconstruction is possible for emission and transmission tomography such as SPECT, PET, and CT. However, OSEM does not significantly accelerate reconstruction with computationally expensive regularizers such as patch-based nonlocal (NL) regularizers, because the regularizer gradient is evaluated for every subset. We propose to use variable splitting to separate the likelihood term and the regularizer term for penalized emission tomographic image reconstruction problem and to optimize it using the alternating direction method of multiplier (ADMM). We also propose a fast algorithm to optimize the ADMM parameter based on convergence rate analysis. This new scheme enables more sub-iterations related to the likelihood term. We evaluated our ADMM for 3-D SPECT image reconstruction with a patch-based NL regularizer that uses the Fair potential function. Our proposed ADMM improved the speed of convergence substantially compared to other existing methods such as gradient descent, EM, and OSEM using De Pierro's approach, and the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm.