nils-herrmann/clean_scirepeval huggingface dataset:
Multiscale methods for the segmentation of images

This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for finding the minima of such variational problems are prone to two shortfalls: they are often computationally intensive and almost always incapable of computing error statistics associated with the segmentation. Using a particular variational formulation as a starting point, this paper presents a derivation of an associated statistical formulation using multiscale models. The result is an algorithm which is fast and capable of computing error statistics.
Sensing-aware classification with high-dimensional data

In many applications decisions must be made about the state of an object based on indirect noisy observation of high-dimensional data. An example is the determination of the presence or absence of stroke from tomographic projections. Conventionally, the sensing process is inverted and a classifier is built in the reconstructed domain, which requires complete knowledge of the sensing mechanism. Alternatively, a direct data domain classifier might be constructed, but the constraints imposed by the sensing process are then lost. In this work we study the behavior of a third path we term “sensing-aware classification.” Our aim is to contribute to the development of a rigorous theory for such challenging problems. To this end, we consider an abstracted binary classification problem with very high dimensional observations, a restricting sensing configuration, and unknown statistical models of noise and object which must be learned from constrained training data. We analyze the impact of different levels of prior knowledge concerning the sensing mechanism for various classification strategies. In particular we prove that the strategies based on the naive estimation of all model elements results in a classification performance asymptotically no better than guessing whereas sensing-aware, projection-based classification rules attain Bayes-optimal risk. Simulation results are also provided.
Level set methods for dynamic tomography

In this paper, we propose a novel variational framework for the reconstruction of dynamic objects from sparse and noisy tomographic data. Using an object-based scene model, we developed a general object dynamic model based on a one to one and differentiable mapping. We then propose a novel distance between curves to incorporate the object dynamics into the variational framework. For the minimization of the energy function, we developed a coordinate descent algorithm based on the level set methods. Experimental results for reconstructing a sequence of multiple dynamic objects are presented.
A multiphase level set method for tomographic reconstruction of dynamic objects

In this paper we present a novel variational method for tomographic reconstruction of dynamic objects with a multiphase level set method. Our method can reconstruct an image sequence composed of multiple regions from an extremely sparse and noisy projection data set. The number of regions in a scene can also change over time in our model. Temporal dynamics for region boundary and intensities are modeled and incorporated into an energy function with a new distance measure between curves. Preliminary results on reconstructing a simulated dynamic cardiac sequence from noisy projection data of only three angles at each time instant is presented.
Control of vibrational systems

Vibrational systems, characterized by their second-order or "spring-mass" nature, occur throughout engineering. Examples range from lightly damped structures, such as the proposed space station, to regional power system models. While the study of such vibrational systems has a long and rich history, concern has traditionally focused on the properties of passive, uncontrolled systems. The growing need to actively control large, complex systems of this type leads us to the study of controlled vibrational systems. We provide stability assuring constraints for compensator design. These constraints directly use the initial system model, thus avoiding the need for order reduction techniques with the associated problems of "spill-over" from the full to the reduced system model.
Sampling below the Nyquist rate in interferometric fluorescence microscopy with multi-wavelength measurements to remove aliasing

A multi-wavelength 3D fluorescence microscope, with transfer functions varying significantly with wavelength, is proposed. This microscope measures multiple wavelengths concurrently and scans through the object at a rate significantly below the Nyquist criterion, which gives a reduced image acquisition time. The sub-Nyquist sampling produces a set of images contaminated by aliasing. Due to the differing transfer functions, the aliasing effects are different in each image. This allows the aliasing operator to be inverted and a single unaliased image to be constructed. This is an application of the generalized sampling expansion first introduced by Papoulis. The instrument is demonstrated through simulation and shown to produce images of a similar quality to those that would be expected from a Nyquist-rate instrument.
Tomographic reconstruction and estimation based on multiscale natural-pixel bases

We use a natural pixel-type representation of an object, originally developed for incomplete data tomography problems, to construct nearly orthonormal multiscale basis functions. The nearly orthonormal behavior of the multiscale basis functions results in a system matrix, relating the input (the object coefficients) and the output (the projection data), which is extremely sparse. In addition, the coarsest scale elements of this matrix capture any ill conditioning in the system matrix arising from the geometry of the imaging system. We exploit this feature to partition the system matrix by scales and obtain a reconstruction procedure that requires inversion of only a well-conditioned and sparse matrix. This enables us to formulate a tomographic reconstruction technique from incomplete data wherein the object is reconstructed at multiple scales or resolutions. In case of noisy projection data we extend our multiscale reconstruction technique to explicitly account for noise by calculating maximum a posteriori probability (MAP) multiscale reconstruction estimates based on a certain self-similar prior on the multiscale object coefficients. The framework for multiscale reconstruction presented can find application in regularization of imaging problems where the projection data are incomplete, irregular, and noisy, and in object feature recognition directly from projection data.
Dynamic tomography with curve evolution methods

In this paper, we propose a curve evolution method for dynamic tomography. We jointly estimate both object boundaries and intensity dynamics using all the data. The image intensity is assumed piecewise constant and the activity in each region is modeled as a parameterized dynamic process. The boundaries of objects are modeled as a series of curves. The boundaries and dynamics are estimated jointly through a curve evolution process. This curve evolution process is implemented with level set methods, which can handle topology changes easily.
A curve evolution approach to object-based tomographic reconstruction

In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background. By designing a new "tomographic flow", the resulting problem is recast into a curve evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy limited-view Radon transformed data and noisy ground-penetrating radar data. The reconstruction results and computational cost are compared with those of conventional, pixel-based regularization methods. The results indicate that the curve evolution methods achieve improved shape reconstruction and have potential computation and memory advantages over conventional regularized inversion methods.
A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution

In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term. We achieve curve evolution corresponding to such evolution speeds by separating the evolution process into two different cycles: one cycle for the data-dependent term and a second cycle for the smoothness regularization. The smoothing term is derived from a Gaussian filtering process. In both cycles, the evolution is realized through a simple element switching mechanism between two linked lists, that implicitly represents the curve using an integer valued level-set function. By careful construction, all the key evolution steps require only integer operations. A consequence is that we obtain significant computation speedups compared to exact PDE-based approaches while obtaining excellent agreement with these methods for problems of practical engineering interest. In particular, the resulting algorithm is fast enough for use in real-time video processing applications, which we demonstrate through several image segmentation and video tracking experiments.
Sensing aware dimensionality reduction for nearest neighbor classification of high dimensional signals

A successful approach to high-dimensional classification problems has been to couple nearest-neighbor classification with distance-preserving data dimensionality reduction via independent random projections. In many problems, however, the observed data is related to a natural set of latent variables through a non-trivial sensing process. A common approach in such cases is to either ignore the sensing process or to invert it and then proceed with dimensionality reduction and classifier design. Inversion can be costly and may be unnecessary if the end goal is classification. Rather than either ignoring sensing structure or using it for explicit inversion, we propose using “structured random projections” whose correlation explicitly accounts for the sensing structure. We show examples comparing the performance of independent random projections of observed data, independent random projections of reconstructed data, and our new structured random projections of observed data which demonstrate the benefits of our approach.
A variational energy approach for estimating vascular structure and deformation from B-mode ultrasound imagery

The estimation of local arterial structure and vascular deformation is hampered by regions of poor contrast and by the presence of ultrasound imaging artifacts such as speckle noise and specular reflections. Conventional methods for boundary extraction and velocity estimation can exhibit poor performance under these conditions. We demonstrate a new multi-stage processing scheme which uses a directionally sensitive variational energy segmentation to suppress noise and preserve image boundaries for robust extraction of the vessel contour. This contour information is used to generate a motion region-of-interest (mROI) for a modified optical flow scheme which estimates local boundary motion based only on image data lying within the mROI.
Robust knot detection and spline approximation using wavelet transform extrema and multi-target tracking

Mallat and Hwang (1992) and Mallat and Zhong (1992) describe a novel algorithm both for signal compression and noise removal based on the extraction of the extrema of continuous wavelet transforms (CWTs) at a dyadic sequence of scales, the chaining of the extrema across scale that come from the same feature in the original signal, and the separation of noise from signal through the estimation of the Lipschitz exponents of the singularities corresponding to each such chain of extrema. The results in Mallat and Hwang, and Mallat Zhong and in other efforts based on this technique show the apparent promise of the approach and lead to the conjecture that it is robust, but a number of interesting and important questions and potential applications remain. Mallat and Hwang, and Mallat and Zhong use a simple extrema matching algorithm based on the comparison of the values and the positions of extrema at a pair of consecutive scales. This algorithm cannot work equally well for all input signals, because the trajectories of CWT extrema depend on the type of the corresponding singularity, as well as on the wavelet. The present authors restrict the approximation class of signals to splines. In this case, a more sophisticated and reliable extrema matching algorithm is possible. They arrive at such an algorithm by treating the extrema matching problem as a multi-target tracking problem. The final result is a spline approximation algorithm whose robustness is shown using Cauchy-contaminated Gaussian noise.<<ETX>>
Real-time tracking using level sets

In this paper we propose a novel implementation of the level set method that achieves real-time level-set-based video tracking. In our fast algorithm, the evolution of the curve is realized by simple operations such as switching elements between two linked lists and there is no need to solve any partial differential equations. Furthermore, a novel procedure based on Gaussian filtering is introduced to incorporate boundary smoothness regularization. By replacing the standard curve length penalty with this new smoothing procedure, further speedups are obtained. Another advantage of our fast algorithm is that the topology of the curves can be controlled easily. For the tracking of multiple objects, we extend our fast algorithm to maintain the desired topology for multiple object boundaries based on ideas from discrete topology. With our fast algorithm, a real-time system has been implemented on a standard PC and only a small fraction of the CPU power is used for tracking. Results from standard test sequences and our realtime system are presented.
Moderately rough surface underground imaging via short-pulse quasi-ray Gaussian beams

An adaptive framework is presented for ultra-wideband ground penetrating radar imaging of shallow-buried low-contrast dielectric objects in the presence of a moderately rough air-soil interface. The proposed approach works with sparse data and relies on recently developed Gabor-based narrow-waisted quasi-ray Gaussian beam algorithms as fast forward scattering predictive models. First, a nonlinear inverse scattering problem is solved to estimate the unknown coarse-scale roughness profile. This sets the stage for adaptive compensation of clutter-induced distortion in the underground imaging problem, which is linearized via Born approximation and subsequently solved via various pixel-based and object-based techniques. Numerical simulations are presented to assess accuracy, robustness and computational efficiency for various calibrated ranges of problem parameters. The proposed approach has potential applications to antipersonnel land mine remediation.
A vision augmented navigation system

We focus on the use of an on-board camera as an additional navigation sensor for an autonomous helicopter. We focus on the critical landing phase, where we exploit the structure of the problem to obtain computationally tractable algorithms. We use the other available navigational elements present on the helicopter to simplify the vision processing and resolve typical image interpretation ambiguities. Thus, the processing of the vision system is done not in isolation, but in the larger context of the overall navigation problem. By using a tight integration of vision with inertial estimates the algorithm can be implemented at modest frame rates (/spl sim/5 Hz) with off-the-shelf hardware.
Applications of a class of nonlinear filters to problems in power electronics

A class of nonlinear filters with potential application in power electronics is considered. The class of filters is characterized by the inclusion of a sorting element in the signal path. The sorting operation allows these filters to suppress impulsive noise while preserving edges and monotonic sections of signals. They are generally simple to implement offline and should be included as an additional set of analysis tools for the power systems designer. Emphasis is placed on the median filter, since it is the most accessible filter of the class. A working knowledge of issues arising in design and implementation is developed.<<ETX>>
Tomographic reconstruction using curve evolution

In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve evolution techniques. We use a low order parametric model to describe the shape and texture of the object support as well as the background. This model uses a set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. The problem of determining the unknown contour and texture coefficients of the object and background medium is then formulated as a non-linear estimation problem. By designing a new, "tomographic flow", the resulting problem is recast into a curve evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy Radon transformed data and noisy ground penetrating radar data. The reconstruction results and computational cost are compared with those of conventional regularization methods. The results indicate that our curve evolution methods achieve improved shape reconstruction with reduced computation requirements.
Automated 3-D intraocular ultrasound detection of elevated intracranial pressure

Elevated intracranial pressure (ICP) is often associated with traumatic brain injury and can cause substantial complications. Elevated ICP also causes dilation of the optic nerve sheath, which can be observed and quantitated using intraocular ultrasound. We report on the use of 3-D ultrasound and automated processing algorithms to reduce operator variability in the assessment of sheath diameter. Using data from three subjects, we show that our approach appears to reduce operator variability by approximately 25%. These encouraging preliminary results support the development of an intraocular ultrasound system for non-invasive detection of elevated ICP.
Three-dimensional characterization of carotid plaque structure and deformation with noninvasive ultrasound imaging

We have shown that by using a directionally sensitive, edge preserving filter on B-mode U/S images of ex-vivo carotid vessels we can obtain 3D edge fields of the vessel boundaries that are more distinct than edges from conventional gradient calculation. With these enhanced edge fields we can then extract the 3D vessel boundaries. Bland-Altman plots show that our method for edge field enhancement and boundary extraction yields more reproducible IMT measurements than the gold standard of manual point selection. Lastly, we demonstrate a robust estimator of velocity and strain that exhibits enhanced performance over conventional methods.
A new model-based technique for enhanced small-vessel measurements in X-ray cine-angiograms

Arterial diameter estimation from X-ray cine angiograms is important for quantifying coronary artery disease (CAD) and for evaluating therapy. However, diameter measurement in vessel cross sections /spl les/1.0 mm is associated with large measurement errors. The authors present a novel diameter estimator which reduces both magnitude and variability of measurement error. They use a parametric nonlinear imaging model for X-ray cine angiography and estimate unknown model parameters directly from the image data. The authors' technique allows them to exploit additional diameter information contained within the intensity profile amplitude, a feature which is overlooked by existing methods. This method uses a two-step procedure: the first step estimates the imaging model parameters directly from the angiographic frame and the second step uses these measurements to estimate the diameter of vessels in the same image. In Monte-Carlo simulation over a range of imaging conditions, the authors' approach consistently produced lower estimation error and variability than conventional methods. With actual X-ray images, the authors' estimator is also better than existing methods for the diameters examined (0.4-4.0 mm). These improvements are most significant in the range of narrow vessel widths associated with severe coronary artery disease.
Compressed Sensing of Monostatic and Multistatic SAR

In this letter, we study the impact of compressed data collections from a synthetic aperture radar (SAR) sensor on the reconstruction quality of a scene of interest. Different monostatic and multistatic SAR measurement configurations produce different Fourier sampling patterns. These patterns reflect different spectral and spatial diversity tradeoffs that must be made during task planning. Compressed sensing theory argues that the mutual coherence of the measurement probes is related to the reconstruction performance of sparse domains. With this motivation, we propose a closely related t%-average mutual coherence parameter as a sensing configuration quality parameter and examine its relationship to the reconstruction behavior of various monostatic and ultranarrow-band multistatic configurations. We investigate how this easily computed metric is related to SAR reconstruction quality.
A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data

In this paper, we investigate the problems of anomaly detection and localization from noisy tomographic data. These are characteristic of a class of problems that cannot be optimally solved because they involve hypothesis testing over hypothesis spaces with extremely large cardinality. Our multiscale hypothesis testing approach addresses the key issues associated with this class of problems. A multiscale hypothesis test is a hierarchical sequence of composite hypothesis tests that discards large portions of the hypothesis space with minimal computational burden and zooms in on the likely true hypothesis. For the anomaly detection and localization problems, hypothesis zooming corresponds to spatial zooming - anomalies are successively localized to finer and finer spatial scales. The key challenges we address include how to hierarchically divide a large hypothesis space and how to process the data at each stage of the hierarchy to decide which parts of the hypothesis space deserve more attention. For the latter, we pose and solve a nonlinear optimization problem for a decision statistic that maximally disambiguates composite hypotheses. With no more computational complexity, our optimized statistic shows substantial improvement over conventional approaches. We provide examples that demonstrate this and quantify how much performance is sacrificed by the use of a suboptimal method as compared to that achievable if the optimal approach were computationally feasible.