nils-herrmann/clean_scirepeval huggingface dataset:
Filtered Dynamics and Fractal Dimensions for Noisy Speech Recognition

We explore methods from fractals and dynamical systems theory for robust processing and recognition of noisy speech. A speech signal is embedded in a multidimensional phase-space and is subsequently filtered exploiting aspects of its unfolded dynamics. Invariant measures (fractal dimensions) of the filtered signal are used as features in automatic speech recognition (ASR). We evaluate the new proposed features as well as the previously proposed multiscale fractal dimension via ASR experiments on the Aurora 2 database. The conducted experiments demonstrate relative improved word accuracy for the fractal features, especially at lower signal-to-noise ratio, when they are combined with the mel-frequency cepstral coefficients
A Representation Theory for Morphological Image and Signal Processing

A unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis is presented. The unification requires a set-theoretic methodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translational-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be presented exactly as a minimal nonlinear superposition of morphological erosions or dilations. The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms. Although the developed theory is algebraic, its prototype operations are well suited for shape analysis; hence, the results also apply to systems that extract information about the geometrical structure of signals.
An improved energy demodulation algorithm using splines

A new algorithm is proposed for demodulating discrete-time AM-FM signals, which first interpolates the signals with smooth splines and then uses the continuous-time energy separation algorithm (ESA) based on the Teager-Kaiser energy operator. This spline-based ESA retains the excellent time resolution of the ESA based on discrete energy operators but performs better in the presence of noise. Further, its dependence on smooth splines allows some optimal trade-off between data fitting versus smoothing.
Region-based optical flow estimation

A correspondence method is developed for determining optical flow where the primitive motion tokens to be matched between consecutive time frames are regions. The computation of optical flow consists of three stages: region extraction, region matching, and optical flow smoothing. The computation is completed by smoothing the initial optical flow, where the sparse velocity data are either smoothed with a vector median filter or interpolated to obtain dense velocity estimates by using a motion-coherence regularization. The proposed region-based method for optical flow is simple, computationally efficient, and more robust than iterative gradient methods, especially for medium-range motion.<<ETX>>
Threshold Superposition in Morphological Image Analysis Systems

It is shown that four composite morphological systems, namely morphological edge detection, peak/valley extraction, skeletonization, and shape-size distributions obey a weak linear superposition, called threshold-linear superposition. The output image signal or measurement from each system is shown to be the sum of outputs due to input binary images that result from thresholding the input gray-level image at all levels. These results are generalized to a vector space formulation, e.g. to any finite linear combination of simple morphological systems. Thus many such systems processing gray-level images are reduced to corresponding binary image processing systems, which are easier to analyze and implement.
Demodulation of AM-FM Signals in Noise Using Multiband Energy Operators

with time-varying amplitude a and instantaneous frequency o i = 4 , using the operator Y(s) = ($2-ss' developed by Teager [I] and Kaiser , shown to be highly effective for detecting AM-FM modulations [31. For signals of the form (l), "(3) 5 u 2 q 2 and Y(i = a 2 0 t , with small approximation error under realistic conditions  This motivates the energy separaiwn algorithm @SA): SO) = a(t) cos[~i ) l , (1)
Robust AM-FM features for speech recognition

In this letter, a nonlinear AM-FM speech model is used to extract robust features for speech recognition. The proposed features measure the amount of amplitude and frequency modulation that exists in speech resonances and attempt to model aspects of the speech acoustic information that the commonly used linear source-filter model fails to capture. The robustness and discriminability of the AM-FM features is investigated in combination with mel cepstrum coefficients (MFCCs). It is shown that these hybrid features perform well in the presence of noise, both in terms of phoneme-discrimination (J-measure) and in terms of speech recognition performance in several different tasks. Average relative error rate reduction up to 11% for clean and 46% for mismatched noisy conditions is achieved when AM-FM features are combined with MFCCs.
Suppression of clutter by rank adaptive reweighted sparse coding

Echocardiographic image sequences are frequently corrupted by quasi-static artifacts known as “clutter”. These artifacts are superimposed on the moving myocardium and hinder useful diagnosis. Prior work has shown the efficacy of blind source separation methods to suppress clutter while retaining underlying signal. However, the same image may be corrupted with clutter from multiple mechanisms or sources. Consequently, the spatiotemporal characteristics of the clutter vary depending on the mechanism. In this paper, a method is proposed that progressively detects and removes clutter of increasing temporal rank using sparse coding. The performance was quantified in vitro by measuring the mean squared error with respect to a ground truth dataset with no clutter. The error with respect to the ground truth was reduced from 1.3×10<sup>3</sup> ± 4.7×10<sup>2</sup> a.u. to 2.2 × 10<sup>−2</sup> ± 9.8×10<sup>−3</sup> a.u. The average clutter reduction in vivo was 11.9±0.5 dB.
Three-dimensional speckle reducing anisotropic diffusion

The two-dimensional speckle reducing anisotropic diffusion (SRAD) algorithm developed for despeckling ultrasound imagery to obtain a SRAD algorithm capable of enhancing volumetric ultrasound data is presented in this paper. First, an instantaneous coefficient of variation edge detector appropriate for 3D data set presented. Then, the 3D SRAD partial differential equation is formulated by replacing the gradient operator in the traditional diffusion mechanism with the derived 3D ICOV operator. We demonstrate the performance of the 3D SRAD using synthetic 3D ultrasound data acquired from an ellipsoid and real data acquired from the left ventricle (LV) of a murine heart.
Suppression of clutter by rank adaptive reweighted sparse coding

Echocardiographic image sequences are frequently corrupted by quasi-static artifacts known as “clutter”. These artifacts are superimposed on the moving myocardium and hinder useful diagnosis. Prior work has shown the efficacy of blind source separation methods to suppress clutter while retaining underlying signal. However, the same image may be corrupted with clutter from multiple mechanisms or sources. Consequently, the spatiotemporal characteristics of the clutter vary depending on the mechanism. In this paper, a method is proposed that progressively detects and removes clutter of increasing temporal rank using sparse coding. The performance was quantified in vitro by measuring the mean squared error with respect to a ground truth dataset with no clutter. The error with respect to the ground truth was reduced from 1.3×103 ± 4.7×102 a.u. to 2.2 × 10−2 ± 9.8×10−3 a.u. The average clutter reduction in vivo was 11.9±0.5 dB.
Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.
Object Identification by Marked Point Process

In this paper, we propose an algorithm for the identification of objects from a noisy and cluttered background in video sequences. Our algorithm is based on the marked point process (MPP) framework, which provides a useful tool for integrating object spatial information into the identification process. The maximum a posteriori (MAP) estimation of a set of points corresponding to the centroids of objects observed in the image is obtained via a Markov chain Monte Carlo algorithm. The optimal solution, in terms of the MAP principle, is computed with respect to all objects in the scene, rather than single objects. The algorithm is applied to real data: intravital microscopic rolling leukocyte video datasets. A quantitative study of our approach demonstrates that the proposed approach can serve as a fully automated substitute to the tedious manual rolling leukocyte detection process
Social behavior analysis of Drosophila larvae via motion activity recognition

We aim to analyze the social behavior of Drosophila larvae through automated identification of their motion activity pattern. Social activity recognition is an essential step in identifying the correlation between specific neuronal structures in their brain to the social behavior of the species. Larvae videos are obtained using low-cost overhead video; subsequently, the motion features of the larvae generated by computing a gradient weighted optical flow. These features are used to create a global histogram of gradient-weighted optic flow over a specific time frame. The method for estimating optical flow is based on an implementation by Gautama and Van Hulle that expands on the classical Horn and Schunck method by using spatial Gabor filter pair to find the intensity gradient and recurrent neural networks to find velocity. Activity classification is obtained by using a support vector machine and yields 82% overall accuracy in classification.
Implicit Evolution of Open Ended Curves

We introduce a theoretical framework for implicit evolution of an open ended curve in a two-dimensional image plane. This approach is particularly suitable for identifying thin filamentous structures present in 2D images. The open ended curve is represented as the of the level set of a function (called the*) defined on the curve. The iterative evolution of the curvature map is guided by a diffusion equation and constrained by the imaging force, such as the image intensity gradient. The centerline of the evolved curvature map provides the position of the curve in subsequent iterations. We have tested this new model on both synthetic and real images that contain structures including rivers/roads/arteries. Nine experiments show that our model is successful in identifying complex topological filaments with a low 9% RMSE pixel error, and that the technique withstands effects of shape irregularities such as kinks, bending, circularity and inconsistent edges.
Segmentation of the myocardium from myocardial contrast echocardiography

Myocardial contrast echocardiography (MCE) is a promising new technique that allows quantification of myocardium perfusion and therefore accurate diagnosis of coronary artery disease. MCE data, however, have previously required tedious and time-consuming off-line manual image processing. This paper presents results that demonstrate success of an automatic segmentation approach utilizing active shape models. A shape model was created from a training set of eleven manually drawn contours, which was then applied to twenty-two MCE images. Standard success metrics show that error from this automatic method is comparable to error found among manually drawn contours. Additionally, a more robust calculation of the key blood flow parameters was developed which can accommodate error in the segmentation, verified by high correlation between manually and automatically derived parameters.
Anisotropic edge detection using mean field annealing

An edge detection technique that optimizes edge localization while providing edge continuity and edge thinning is introduced. The solution is obtained by annealing a mean field neural network, providing inexpensive solutions with high parameter insensitivity. Anisotropic diffusion is used to provide localized edge data through the scale-space. Analysis of network parameters, diffusion parameters, network convergence, and scale-space equivalence is provided. Results are shown for real image data and compared with the results of other important edge detection schemes.<<ETX>>
Nonlinear image estimation using piecewise and local image models

We introduce a new approach to image estimation based on a flexible constraint framework that encapsulates meaningful structural image assumptions. Piecewise image models (PIMs) and local image models (LIMs) are defined and utilized to estimate noise-corrupted images, PIMs and LIMs are defined by image sets obeying certain piecewise or local image properties, such as piecewise linearity, or local monotonicity. By optimizing local image characteristics imposed by the models, image estimates are produced with respect to the characteristic sets defined by the models. Thus, we propose a new general formulation for nonlinear set-theoretic image estimation. Detailed image estimation algorithms and examples are given using two PIMs: piecewise constant (PICO) and piecewise linear (PILI) models, and two LIMs: locally monotonic (LOMO) and locally convex/concave (LOCO) models. These models define properties that hold over local image neighborhoods, and the corresponding image estimates may be inexpensively computed by iterative optimization algorithms. Forcing the model constraints to hold at every image coordinate of the solution defines a nonlinear regression problem that is generally nonconvex and combinatorial. However, approximate solutions may be computed in reasonable time using the novel generalized deterministic annealing (GDA) optimization technique, which is particularly well suited for locally constrained problems of this type. Results are given for corrupted imagery with signal-to-noise ratio (SNR) as low as 2 dB, demonstrating high quality image estimation as measured by local feature integrity, and improvement in SNR.
Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes

Due to movement of the specimen, vasodilation, and intense clutter, the intravital location of a vessel boundary from video microscopy is a difficult but necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. This paper details an active contour model for vessel boundary detection and tracking. In developing the method, two innovations are introduced. First, the B-spline model is combined with the gradient vector flow (GVF) external force. Second, a multiscale gradient vector flow (MSGVF) is employed to elude clutter and to reliably localize the vessel boundaries. Using synthetic experiments and video microscopy obtained via transillumination of the mouse cremaster muscle, we demonstrate that the MSGVF approach is superior to the fixed-scale GVF approach in terms of boundary localization. In each experiment, the fixed scale approach yielded at least a 50% increase in root mean squared error over the multiscale approach. In addition to delineating the vessel boundary so that cells can be detected and tracked, we demonstrate the boundary location technique enables automatic blood flow velocity computation in vivo.
Multigrid anisotropic diffusion

A multigrid anisotropic diffusion algorithm for image processing is presented. The multigrid implementation provides an efficient hierarchical relaxation method that facilitates the application of anisotropic diffusion to time-critical processes. Through a multigrid V-cycle, the anisotropic diffusion equations are successively transferred to coarser grids and used in a coarse-to-fine error correction scheme. When a coarse grid with a trivial solution is reached, the coarse grid estimates of the residual error can be propagated to the original grid and used to refine the solution. The main benefits of the multigrid approach are rapid intraregion smoothing and reduction of artifacts due to the elimination of low-frequency error. The theory of multigrid anisotropic diffusion is developed. Then, the intergrid transfer functions, relaxation techniques, diffusion coefficients, and boundary conditions are discussed. The analysis includes the examination of the storage requirements, the computational cost, and the solution quality. Finally, experimental results are reported that demonstrate the effectiveness of the multigrid approach.
Terrain Moisture Classification Using GPS Surface-Reflected Signals

In this letter, a novel method of land-surface classification using surface-reflected global positioning system (GPS) signals in combination with digital imagery is presented. Two GPS-derived classification features are merged with visible image data to create terrain moisture classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, the use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping
Speckle removal by distance-driven anisotropic diffusion of SAR temporal stacks

Radar images are often collected over the same region over time. To provide smoothing of such imagery without effacing temporal changes in the scene, we put forth an anisotropic diffusion technique. Traditionally, with synthetic aperture radar images, the mean of time series is utilized to produce a single despeckled image that discards temporal information. In contrast, we propose a statistical approach designed to reduce speckle noise in each image. The new filter incorporates temporal information essential to detection of potential change events for transportation infrastructure. Results demonstrate the efficacy of the approach, showing lower mean squared error than leading methods.
Tracking leukocytes from in vivo video microscopy using morphological anisotropic diffusion

The study of inflammatory disease hinges upon the behavior and movement of leukocytes and their interaction with the endothelium. We put forth a method for tracking leukocytes in vivo, whereas tracking has been demonstrated previously only for in vitro experiments. The tracker is based on the enhancing capability of morphological anisotropic diffusion, a partial differential equation model for adaptively filtering imagery that retains structures of interest. Morphological anisotropic diffusion excels over standard diffusion in the ability to preserve objects of a certain shape and scale, and it improves upon standard morphological filters in terms of edge preservation and adaptive smoothing. We use the video frames enhanced by morphological diffusion for edge-based registration and background removal in the tracking process.
Oil sand image segmentation using the inclusion filter

Oil sands may constitute two thirds of the world's oil reserves. To efficiently harvest this important resource, image analysis is required to quantify production related performance in terms of particle size distribution. We utilize connected filters to simplify the oil sand images and to generate a robust segmentation. Specifically, a self-dual operator called the inclusion filter is applied to the difficult segmentation problem. The inclusion filter removes minor interior regions and clutter based on the connected component relationships defined by the adjacency forest. We show that the use of the inclusion filter significantly improves the edge fidelity and the insensitivity to initialization for the oil sand application.
Content based image retrieval: The foundation for future case-based and evidence-based ophthalmology

For medical and epidemiologic investigators and caregivers, one powerful functionality yet to be developed is the ability to group retinal images based upon common pathologic appearance. Such a tool would enable advances in evidence-based medicine and would accelerate automated or computer-assisted screening and diagnosis. In this report, we show that current, traditional content based image retrieval methods are insufficient to sort dichotomous images (age-related macular degeneration and Stargardt disease) and then propose novel feature extraction techniques that may improve retrieval performance. Prior to processing of the images, a specialized diffusion method to enhance the contrast, reduce the discontinuity, and eliminate edge artifacts is applied to facilitate segmentation. A robust statistic is applied to find abnormal areas and to differentiate AMD from SD. Two methods of analyzing the subretinal deposits are presented - a granulometry based on area morphology and an AM-FM model. Preliminary data show that the image analysis tools show promise as a useful retrieval tool.
Processing issues in the automated inspection of power insulators

In this paper, we present an approach to automated inspection of power insulators. In constructing the program, we have analyzed the potential options in processing that affect the inspection process. The location of the insulator is first determined by using a binary template matching process. Then, the disks of the insulator are extracted by a filtering operation. We have compared the performance of three order statistic-based filters for this inspection and found that the median filter performs optimally for our application. Finally, the connected components labelling technique is applied to count the area, width, and height of the disk and the number of disks in the insulator.