nils-herrmann/clean_scirepeval huggingface dataset:
Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security

In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.
Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging

We present an evaluation of the impact of a recently proposed synthetic aperture radar (SAR) imaging technique on feature enhancement and automatic target recognition (ATR) performance. This image formation technique is based on nonquadratic optimization, and the images it produces appear to exhibit enhanced features. We quantify such feature enhancement through a number of criteria. The findings of our analysis indicate that the new feature-enhanced SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared with conventional SAR images. We also provide an ATR-based evaluation. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, with three different classifiers. The first classifier is template based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that the new feature-enhanced SAR imaging method can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.
Line detection in images through regularized hough transform

The problem of determining the location and orientation of straight lines in images is of great importance in the fields of computer vision and image processing. Traditionally the Hough transform, (a special case of the Radon transform) has been widely used to solve this problem for binary images. In this paper, we pose the problem of detecting straight lines in gray-scale images as an inverse problem. Our formulation is based on use of the inverse Radon operator, which relates the parameters determining the location and orientation of the lines in the image to the noisy input image. The advantage of this formulation is that we can then approach the problem of line detection within a regularization framework and enhance the performance of the Hough-based line detector through the incorporation of prior information in the form of regularization. We discuss the type of regularizers that are useful for this problem and derive efficient computational schemes to solve the resulting optimization problems enabling their use in large applications. Finally, we show how our new approach can be alternatively viewed as one of finding an optimal representation of the noisy image in terms of elements chosen from a dictionary of lines. This interpretation relates the problem of Hough-based line finding to the body of work on adaptive signal representation.
A sufficient condition for the stability of interval matrix polynomials

The root location of sets of scalar polynomials whose coefficients are confined to intervals and the associated extension to eigenvalues of sets of constant matrices whose coefficients are contained in intervals are reviewed. A central result for complex scalar interval polynomials is a theorem developed by V.L. Kharatonov (1978), which states that each member of a set of such polynomials is stable if and only if eight special polynomials from the set are stable. The case of interval matrix polynomials is examined, and a Kharitonov-like result for their strong stability is provided. This in turn yields a sufficient condition for stability of a set of interval matrix polynomials.
Sensing structure in learning-based binary classification of high-dimensional data

In many problems of engineering interest, one encounters the situation where the ambient dimension of the sensed data is very large relative to the number of samples, but there exists a latent low dimensional sensing structure that can potentially be leveraged for inferencing tasks. This work investigates the impact of latent sensing structure on supervised classification performance when the data dimension scales to infinity faster than the number of samples. In contrast to related studies, here the classification difficulty is held fixed as the data dimension scales. For a binary supervised classification problem with Gaussian likelihood functions, it is shown that the asymptotic error probability converges to half if the sensing structure is totally ignored, whereas it converges to the Bayes risk if the sensing structure is sufficiently regular and it is properly utilized. It is also shown, however, that without suitable regularity in the latent low-dimensional sensing structure, it is impossible to attain an asymptotic error probability which is better than half. These findings are validated through various simulations. Additional numerical results for support vector machines and sensitivity to mismatch between true and assumed structure are also provided.
Dictionary based image enhancement for integrated circuit imaging

The rapid decrease in the dimensions of integrated circuits has necessitated corresponding higher-resolution methods for defect imaging. Current state of the art, defect imaging systems are reaching the limits of their resolution. In this work, we are proposing a new overcomplete dictionary based sparse signal imaging framework to improve the resolution and localization in confocal microscopy systems for backside optical integrated circuit defect imaging. The domain of integrated circuit imaging is particularly suitable for the application of overcomplete dictionaries in an image reconstruction framework because the images are highly structured, containing predictable building blocks derivable from the corresponding computer aided design layouts. This structure provides a strong and natural a-priori dictionary for scene reconstruction. This dictionary prior is coupled with a physically-based observation model to create enhanced scene reconstructions. The approach is described and results on simulated data are provided.
Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.
A feasibility study of joint respiratory and cardiac motion correction for coronary PET/CT imaging

Coronary artery disease (CAD) or atherosclerosis is a leading cause of death in industrialized nations. Such diseases are marked by development of chronic vascular inflammation in coronary arteries. Accurate assessment, characterization and localization of this inflammation through non-invasive methods is an important step towards the treatment of CAD. It has been shown that positron emission tomography (PET) is capable of detecting large vessel inflammation via activated macrophage uptake of FDG. However, respiratory and cardiac motion during image acquisition leads to severe blurring of the resulting images thereby rendering the spatial resolution inadequate for detection of inflammation in coronary arteries. The objective of this paper is to demonstrate the potential of producing high resolution PET images to enable imaging of coronary artery inflammation. In this paper, we propose a novel method for joint cardiac and respiratory motion correction in PET/CT called Cardiac Shape Tracking with Adjustment for Respiration (CSTAR). It uses a sequential cardiac and respiratory motion correction scheme by decoupling the two, and also features the use of all acquired data for SNR preservation. CT images are primarily used for cardiac shape tracking through the estimation of cardiac motion. Cardiac motion correction is incorporated in a super-resolution framework, followed by adjustment for the residual respiratory motion blur using blind deconvolution. We investigated the feasibility of this technique on simulated cardiac PET/CT data using XCAT and the preliminary results show a marked qualitative and quantitative improvement when compared to conventional PET reconstruction.
Shared-boundary fusion for estimation of noisy multi-modality atherosclerotic plaque imagery

Our work focuses on applying boundary-preserving smoothing techniques to the fusion of multiple image modalities in an effort to improve image-based classification of atherosclerotic lesions. No single imaging modality has yet demonstrated the ability to reliably detect "vulnerable" lesions. We present an approach for estimating multi-modality biomedical imagery when tissue classes are sharply delimited by boundaries. Our approach is based on the Mumford-Shah framework for edge-preserving smoothing. We exploit this framework to fuse heterogeneous sensing modalities that image unrelated physicochemical parameters of a piecewise-homogeneous tissue field. We demonstrate this approach by fusing boundary field estimates from MR and CT atherosclerotic lesion imagery into a single estimated underlying tissue boundary field, while simultaneously estimating the original imagery to better estimate tissue characteristics and structure.
High-resolution biosensor spectral peak shift estimation

In this paper, we present a maximum likelihood (ML) approach to high-resolution estimation of the shifts of a spectral signal. This spectral signal arises in application of optically based resonant biosensors, where high resolution in the estimation of signal shift is synonymous with high sensitivity to biological interactions. For the particular sensor of interest, the underlying signal is nonuniformly sampled and exhibits Poisson amplitude statistics. Shift estimation accuracies orders of magnitude finer than the sample spacing are sought. The new ML-based formulation leads to a solution approach different from typical resonance shift estimation methods based on polynomial fitting and peak (or null) estimation and tracking.
Probabilistic and sequential computation of optical flow using temporal coherence

In the computation of dense optical flow fields, spatial coherence constraints are commonly used to regularize otherwise ill-posed problem formulations, providing spatial integration of data. We present a temporal, multiframe extension of the dense optical flow estimation formulation proposed by Horn and Schunck (1981) in which we use a temporal coherence constraint to yield the optimal fusing of data from multiple frames of measurements. Conceptually, standard Kalman filtering algorithms are applicable to the resulting multiframe optical flow estimation problem, providing a solution that is sequential and recursive in time. Experiments are presented to demonstrate that the resulting multiframe estimates are more robust to noise than those provided by the original, single-frame formulation. In addition, we demonstrate cases where the aperture problem of motion vision cannot be resolved satisfactorily without the temporal integration of data enabled by the proposed formulation. Practically, the large matrix dimensions involved in the problem prohibit exact implementation of the optimal Kalman filter. To overcome this limitation, we present a computationally efficient, yet near-optimal approximation of the exact filtering algorithm. This approximation has a precise interpretation as the sequential estimation of a reduced-order spatial model for the optical flow estimation error process at each time step and arises from an estimation-theoretic treatment of the filtering problem. Experiments also demonstrate the efficacy of this near-optimal filter.
Using multi-element detectors to create optimal apertures in confocal microscopy

A detection pinhole is used in confocal microscopy to reduce contributions from image planes outside of the focal plane. The size of this pinhole may be varied but the idea of a fixed circular aperture is ubiquitous. Here it is shown that an ideal detection aperture varies as a function of the spatial-frequency being imaged. A method for calculating such detection apertures is given, an example calculation is shown and a detector array is suggested as a means to approximate these varying detection apertures
Quality enhancement of arterial measurements based on a more accurate stochastic model of X-ray cine-angiography

The problem of estimating the diameter of narrow coronary arteries with low measurement error and variability remains unresolved. Current methods for improving measurements increase accuracy at the expense of reproducibility and vice-versa. To address this problem, we have constructed a parametric model for X-ray cine-angiography that accurately captures the non-linearity of the process. In addition, we have derived a practical method for the estimation of the image model parameters directly from the angiographic data. These developments make it possible to use absolute image intensity values to improve the diameter estimation quality. We have characterized the performance and sensitivity of this technique by using Monte Carlo simulation and by using actual X-ray image data. Our results show that this estimator is robust to the expected range of encountered imaging conditions and produces lower estimation error and variability than existing methods. This quality enhancement is greatest for narrow vessel widths associated with severe coronary disease.
A graph cut method for linear inverse problems

In this paper we propose a graph cut method for solving a multi-label linear inverse problem with an arbitrary system matrix. Graph cuts are efficient methods for solving pixel-labeling and early vision problems. Energy function minimization problems that occur in image denoising are easily solved by graph cut techniques. However, applying graph cuts to inverse problems which have a non-diagonal system matrix becomes challenging, as a data cost of one pixel depends on intensities of other pixels. Such cost functions are not graph representable. In this paper, we propose an iterative method for minimization of energy functions occurring in inverse problems, where a graph-representable Taylor approximation of the original cost function is rapidly solved via a graph cut method at each iteration.
Advances in the quantification, analysis and display of thallium myocardial images

A system has been developed for aiding the analysis of Thallium-201 myocardial images. The system automatically localizes the outer boundary of the myocardium using novel ellipse fitting algorithms, allows convolution and morphological filtering of the /sup 201/Th images, and provides an extensible platform for the investigation of automated protocols for quantitative analysis. The system has the capability to perform analyses on the localized regions and generate a three-dimensional display from the cardiac projections.<<ETX>>
Functional MRI activity characterization using response time shift estimates from curve evolution

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brain's response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.
A moment-based variational approach to tomographic reconstruction

We describe a variational framework for the tomographic reconstruction of an image from the maximum likelihood (ML) estimates of its orthogonal moments. We show how these estimated moments and their (correlated) error statistics can be computed directly, and in a linear fashion from given noisy and possibly sparse projection data. Moreover, thanks to the consistency properties of the Radon transform, this two-step approach (moment estimation followed by image reconstruction) can be viewed as a statistically optimal procedure. Furthermore, by focusing on the important role played by the moments of projection data, we immediately see the close connection between tomographic reconstruction of nonnegative valued images and the problem of nonparametric estimation of probability densities given estimates of their moments. Taking advantage of this connection, our proposed variational algorithm is based on the minimization of a cost functional composed of a term measuring the divergence between a given prior estimate of the image and the current estimate of the image and a second quadratic term based on the error incurred in the estimation of the moments of the underlying image from the noisy projection data. We show that an iterative refinement of this algorithm leads to a practical algorithm for the solution of the highly complex equality constrained divergence minimization problem. We show that this iterative refinement results in superior reconstructions of images from very noisy data as compared with the classical filtered back-projection (FBP) algorithm.
A nested recursive approach to MAP estimation based on Gauss-Markov random fields

Many large multidimensional space-time signal processing and data inversion applications (e.g. deconvolution) require some form of regularization to extract meaningful information. A popular approach to regularizing such problems in a statistical context is via a Gauss-Markov random field (GMRF) prior model in a maximum a posteriori (MAP) estimation framework. While providing good reconstructions, the high dimensionality of these problems can lead to prohibitive computational constraints which limit their practical applicability, particularly in real or near-real time applications. It has been shown that GMRF models possess a particular recursive structure. Conversely, complementary work in suboptimal filtering has been based on reduced order GMRF modeling. In, this work, we combine these two results to present a suboptimal filter design which repeatedly takes advantage of this recursive GMRF structure to subdivide a large problem into a series of smaller, more tractable problems. In this way we present a method for approximate, model-based, recursive solution to such high dimensional problems based on their inherent recursive structure.
Geometric Aspects Of Tomographic Signal Processing

Tomographic signal processing encompasses a wide variety of applications in such areas as medical imaging, geophysical and oceanographic signal processing, machine vision, and astrophysics. All such applications, however, share a common underlying structure. An object is to be reconstructed from its noisy, and possibly sparse!, line-integral projections. Mathematically, the problem is that of inversion of the Radon-transform of a function from noisy data. If one assumes that the object in question is binary, i.e. has constant intensity, then the line-integral projection data take on a very rich geometxic structure. Specifically, the projection data can be interpreted as chord-length measurements across the interior of the object. In this case, the problem can be regarded as one of reconstructing the boundary of a binary object given noisy, and possibly sparse, chord-length measurements across the object. In this paper we describe those geometric constraints that arise from the above interpretation. These constraints include: 1. Constraints arising from prior assumptions on the shape of the object such as convexity, smoothness, etc; 2. Constraints on the number of projections needed so that a unique reconstruction is obtained. These constraints may differ according to the type of projection being applied (fan-beam, or parallel-beam), and are critical in identifying redundancy in the data and to the design of efficient processing algorithms; 3. A geometric interpretation of the Radon-Transform Constraints. We will show that the incorporation of these constraints is critical to the reconstruction when the data is sparse and/or the signal-to-noise ration is low. Furthermore, we demonstrate that the inclusion of these constraints in the reconstruction algorithms will aid the reconstruction process even if the data is not sparse or excessively noisy. lThis work was supported by the National Science Foundation under Grant 9015281-MIP, the Office of Naval Research under Grant N00014-91-J-1004, and the US Army Research Office under Contract DAAL0386-K-0171.
Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization

We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional SAR techniques, the method we propose produces images with increased resolution, reduced sidelobes, reduced speckle and easier-to-segment regions. Our technique effectively deals with the complex-valued, random-phase nature of the underlying SAR reflectivities. An efficient and robust numerical solution is achieved through extensions of half-quadratic regularization methods to the complex-valued SAR problem. We demonstrate the performance of the method on synthetic and real SAR scenes.