nils-herrmann/clean_scirepeval huggingface dataset:
Fragile Watermarking Based on Encoding of the Zeroes of the $z$-Transform

In this paper, a new fragile watermarking method for digital image authentication is proposed based on the zero locations of the z-transform. The z-transform domain is a new transform space for fragile watermark embedding. Our watermarking method is designed by exploiting the sensitivity of the positions of the zeroes of the z-transform around the unit circle to any change made on the host image. The watermarking system can localize the portions of a watermarked image that have been tampered with with high accuracy. In addition, the newly proposed scheme is more secure than normal least-significant bits-based fragile watermarking techniques. Experimental results as well as the theoretical analysis demonstrated the fragility and accuracy of the new method.
Automatic detection of schwalbe's line in the anterior chamber angle of the eye using HD-OCT images

Angle-closure glaucoma is a major cause of blindness in Asia and could be detected by measuring the anterior chamber angle (ACA) using gonioscopy, ultrasound biomicroscopy or anterior segment (AS) optical coherence tomography (OCT). The current software in the VisanteTM OCT system by Zeiss is based on manual labeling of the scleral spur, cornea and iris and is a tedious process for ophthalmologists. Furthermore, the scleral spur can not be identified in about 20% to 30% of OCT images and thus measurements of the ACA are not reliable. However, high definition (HD) OCT has identified a more consistent landmark: Schwalbe's line. This paper presents a novel algorithm which automatically detects Schwalbe's line in HD-OCT scans. The average deviation between the values detected using our algorithm and those labeled by the ophthalmologist is less than 0.5% and 0.35% in the horizontal and vertical image dimension, respectively. Furthermore, we propose a new measurement to quantify ACA which is defined as Schwalbe's line bounded area (SLBA).
Spherical finite rate of innovation theory for the recovery of fiber orientations

In this paper, we investigate the reconstruction of a signal defined as the sum of orientations from samples taken with a kernel defined on the 3D rotation group. A potential application is the recovery of fiber orientations in diffusion magnetic resonance imaging. We propose an exact reconstruction algorithm based on the finite rate of innovation theory that makes use of the spherical harmonics representation of the signal. The number of measurements needed for perfect recovery, which may be as low as 3K, depends only on the number of orientations and the bandwidth of the kernel used. Furthermore, the angular resolution of our method does not depend on the number of available measurements. We illustrate the performance of the algorithm using several simulations.
An efficient wavelet-based pattern matching scheme for ECG data compression

A novel coding scheme for ECG data compression is proposed in this paper. Following beat delineation, the periods of the beats are normalized by multi-rate processing. Amplitude normalization is performed afterwards, and discrete wavelet transform is applied to each normalized beat. Due to the period and amplitude normalization, the wavelet transform coefficients bear a high correlation across beats. To increase the compression ratio, a pattern matching unit is utilized. The difference between the actual period and the standard period, and amplitude scale factor are also retained for each beat. At the decoder, the inverse wavelet transform is computed from the reconstructed wavelet transform coefficients. The original amplitude and period of each beat are then recovered. The simulation results show that the performance of the proposed compression algorithm is quite satisfactory and achieves significant improvement in comparison with some recent techniques.
Reproducible Research: A Case Study of Sampling Signals with Finite Rate of Innovation

A case study on reproducible research in sampling theory of signals containing a finite rate of innovation is the topic of this paper. By building a solid research which is furthermore reproducible enables the researcher to build intuition in a research area and to progress at a much faster pace. Here, we show that the founding problem of sampling and exact reconstruction of periodic streams of Dirac pulse will be the basis of the sampling theory for signals with finite rate of innovation. The sampling theory can be extended to other signals such as piecewise polynomials, bandlimited signals with additive shot noise and the sum of bandlimited signals with piecewise polynomial signals. It is shown that the implementation is based on the one for streams of Dirac pulses, thus making the new research reproducible as well.
Two new parameters to assess anterior chamber angle for SS-OCT images

In this paper, we propose an automatic quantitative assessment method of the anterior chamber angle of the eye using swept source optical coherence tomography (SS-OCT) images. The angle structure was assessed by two new parameters: 1) mean of continuous measurement of angle opening distance (mAOD) centered on Schwalbe's line; and 2) area of the anterior chamber (AAC). In this research, two hundred and fifty-two SS-OCT images were analyzed. The measurements of open and closed angle in SS-OCT images were compared with that of Schwalbe's line based single angle assessment (AOD_sl, ARA_sl). Our continuous based AOD measurement shows over 93% correlation with the single based AOD angle assessment but provides a more reliable and accurate measurement for open and closed angle SS-OCT images. These newly proposed parameters give a better representation of the angle width for capturing the nature of the angle dynamics involved in different forms of angle closure or open angle glaucoma.
Speckle in ultrasound images: Friend or FOE?

Contrary to the popular belief of treating speckle related pixels as noise and filtering an ultrasound image for speckle noise removal, the practical importance and use of these pixels in performing a multi-organ segmentation of the thyroid gland is studied in this research work. In this work, speckle related pixels are classified into three echogenic levels and then used to segment an ultrasound image of the thyroid gland into the trachea, carotid, muscles and thyroid. Novel techniques are introduced to estimate the anterior boundaries of the thyroid gland using low pass filtered intensity gradients of the hyperechoic speckle pixels in transverse and longitudinal ultrasound scans, respectively. An energy functional similar to active contour models is defined to segment that carotid artery using hypoechoic speckle pixels. The proposed technique was executed on 88 images of the thyroid gland. Clinical significance of using speckles to segment is determined by validating on 32 images of the thyroid gland by measuring the overlap with the Ground Truth segmentation obtained from two expert doctors using Dice coefficient as the overlap measure.
A hybrid watermarking scheme for H.264/AVC video

A novel H.264/AVC watermarking method is proposed in this paper. By embedding the robust watermark into DCT domain and the fragile watermark into motion vectors respectively, the proposed method can jointly achieve both copyright protection and authentication. Our scheme outperforms other video watermarking schemes on higher watermarking capacity especially in lower compression bit-rates. Furthermore, being well aligned with Lagrangian optimization for mode choice featured in H.264/AVC, the proposed scheme only introduces small distortions into the video content. Experimental results also demonstrate that the proposed solution is very computationally efficient during watermark extraction.
Automatic removal of manually induced artefacts in ultrasound images of thyroid gland

Manually induced artefacts, like caliper marks and anatomical labels, render an ultrasound (US) image incapable of being subjected to further processes of Computer Aided Diagnosis (CAD). In this paper, we propose a technique to remove these artefacts and restore the image as accurately as possible. The technique finds application as a pre-processing step when developing unsupervised segmentation algorithms for US images that deal with automatic estimation of the number of segments and clustering. The novelty of the algorithm lies in the image processing pipeline chosen to automatically identify the artefacts and is developed based on the histogram properties of the artefacts. The algorithm was able to successfully restore the images to a high quality when it was executed on a dataset of 18 US images of the thyroid gland on which the artefacts were induced manually by a doctor. Further experiments on an additional dataset of 10 unmarked US images of the thyroid gland on which the artefacts were simulated using Matlab showed that the restored images were again of high quality with a PSNR > 38dB and free of any manually induced artefacts.
Automatic segmentation of common carotid artery in transverse mode ultrasound images

We consider the problem of carotid artery segmentation and develop an automated outlining technique based on the active disc formalism that we recently introduced. The outlining problem is posed as one of optimization of a locally defined contrast function with respect to the affine transformation parameters that characterize the active disc. It turns out that standard techniques based on gradient-descent minimization can be used to carry out the optimization, although more sophisticated optimizers could also be deployed. For the initialization, we use a matched filter with a template size chosen based on an estimate of the average size of the carotid artery. We report results of experimental validation on Brno university's signal processing (SP) lab database, which contains 971 transverse mode ultrasound images of the carotid artery. The images in the database are manually annotated using a circle with center and radius explicitly specified in pixels, which serves as the reference. The circular annotation is also a good match with the active disc template considered in this paper. The proposed method results in an average detection accuracy of 95.5% and an average Dice similarity measure of 87.36% and takes only a few seconds of processing time per image. Comparisons with other state-of-the-art techniques are also reported.
Noisy channel detection using the common annihilator with an application to electrocardiograms

Signal quality assessment is an important issue as noisy channels could mean lost information and unreliable data. In the field of Electrocardiograms (ECG), this is also important as noise could affect the detection of transient cardiac conditions which occur in these noisy channels. In VPW-FRI, the common annihilator is used to decompose multichannel signals with common root locations. Using information from the common annihilating filter, this paper will show that poor quality or noisy signals can be classified from the noiseless signals. This is done through the fact that each channel contributes to the solution of a pulse location and the channels which contribute below a certain threshold are deemed to be noisy or of poor quality. We will show that our algorithm works to the point where VPW-FRI is able to distinguish the features of the signal well despite any noise present.
Fractional Delay Filters Based on Generalized Cardinal Exponential Splines

Fractional delay filters (FDFs) play an important role in certain areas of digital signal processing and communication engineering, where it is desirable to generate delays that are of the order of a fraction of the sampling period. In this paper, we advocate the use of generalized cardinal exponential splines-a class of compactly supported functions that is much richer than the existing B-spline family-for designing precision FDFs. One advantage of using generalized cardinal exponential splines is that it provides ready access to several spline families and other kernels which could be used for FDF design. The B-spline and Lagrange interpolator based FDFs are a special case of our proposition. We also discuss a design example and show that it is possible to design filters that have lower interpolation errors as compared to its B-spline counterparts.
Image Authentication and Restoration Using Irregular Sampling for Traffic Enforcement Applications

Traffic image sequences are important information for the purpose of traffic system control and traffic accidents surveillance, for example, the determination of offending vehicles. A semi-fragile watermarking method for the automatic authentication and restoration of traffic images using irregular sampling is described. Watermarks are embedded into the pinned field of the pinned sine transform (PST) of the original image, which reflects local malicious tampering on the texture of the image. When tampered blocks are detected, the restoration problem is formulated as an irregular sampling problem in approximation subspaces. These blocks are then reconstructed, making use of the information embedded in the same watermarked image, through iterative projections onto convex sets in approximation sub-spaces. The restoration process is a variant of the Papoulis-Gerchberg algorithm, and is robust to common image processing operations such as lossy transcoding and image filtering
Glottal and Vocal Tract Characteristics of Voice Impersonators

Voice impersonators possess a flexible voice which allows them to imitate and create different voice identities. These impersonations present a challenge for forensic analysis and speaker identification systems. To better understand the phenomena underlying successful voice impersonation, we collected a database of synchronous speech and ElectroGlottoGraphic (EGG) signals from three voice impersonators each producing nine distinct voice identities. We analyzed glottal and vocal tract measures including F0, speech rate, vowel formant frequencies, and timing characteristics of the vocal folds. Our analysis confirmed that the impersonators modulated all four parameters in producing the voices, and provides a lower bound on the scale of variability that is available to impersonators. Importantly, vowel formant differences across voices were highly dependent on vowel category, showing that such effects cannot be captured by global transformations that ignore the linguistic parse. We address this issue through the development of a no-reference objective metric based on the vowel-dependent variance of the formants associated with each voice. This metric both ranks the impersonators natural voices highly, and correlates strongly with the results of a subjective listening test. Together, these results demonstrate the utility of voice variability data for the development of voice disguise detection and speaker identification applications.
Sampling and Reconstruction of Sparse Signals in Fractional Fourier Domain

Sampling theory for continuous time signals which have a bandlimited representation in fractional Fourier transform (FrFT) domain-a transformation which generalizes the conventional Fourier transform-has blossomed in the recent past. The mechanistic principles behind Shannon's sampling theorem for fractional bandlimited (or fractional Fourier bandlimited) signals are the same as for the Fourier domain case i.e. sampling (and reconstruction) in FrFT domain can be seen as an orthogonal projection of a signal onto a subspace of fractional bandlimited signals. As neat as this extension of Shannon's framework is, it inherits the same fundamental limitation that is prevalent in the Fourier regime-what happens if the signals have singularities in the time domain (or the signal has a nonbandlimited spectrum)? In this paper, we propose a uniform sampling and reconstruction scheme for a class of signals which are nonbandlimited in FrFT sense. Specifically, we assume that samples of a smoothed version of a periodic stream of Diracs (which is sparse in time-domain) are accessible. In its parametric form, this signal has a finite number of degrees of freedom per unit time. Based on the representation of this signal in FrFT domain, we derive conditions under which exact recovery of parameters of the signal is possible. Knowledge of these parameters leads to exact reconstruction of the original signal.
Echogenicity based approach to detect, segment and track the common carotid artery in 2D ultrasound images

Automatic detection and segmentation of the common carotid artery in transverse ultrasound (US) images of the thyroid gland play a vital role in the success of US guided intervention procedures. We propose in this paper a novel method to accurately detect, segment and track the carotid in 2D and 2D+t US images of the thyroid gland using concepts based on tissue echogenicity and ultrasound image formation. We first segment the hypoechoic anatomical regions of interest using local phase and energy in the input image. We then make use of a Hessian based blob like analysis to detect the carotid within the segmented hypoechoic regions. The carotid artery is segmented by making use of least squares ellipse fit for the edge points around the detected carotid candidate. Experiments performed on a multivendor dataset of 41 images show that the proposed algorithm can segment the carotid artery with high sensitivity (99.6 ±m 0.2%) and specificity (92.9 ±m 0.1%). Further experiments on a public database containing 971 images of the carotid artery showed that the proposed algorithm can achieve a detection accuracy of 95.2% with a 2% increase in performance when compared to the state-of-the-art method.
Sampling Signals With a Finite Rate of Innovation on the Sphere

The state of the art in sampling theory now contains several theorems for signals that are non-bandlimited. For signals on the sphere however, most theorems still require the assumptions of bandlimitedness. In this work we show that a particular class of non-bandlimited signals, which have a finite rate of innovation, can be exactly recovered using a finite number of samples. We consider a sampling scheme where K weighted Diracs are convolved with a kernel on the rotation group. We prove that if the sampling kernel has a bandlimit L = 2K then (2K - 1)(4K - 1) + 1 equiangular samples are sufficient for exact reconstruction. If the samples are uniformly distributed on the sphere, we argue that the signal can be accurately reconstructed using 4K2 samples and validate our claim through numerical simulations. To further reduce the number of samples required, we design an optimal sampling kernel that achieves accurate reconstruction of the signal using only 3K samples, the number of parameters of the weighted Diracs. In addition to weighted Diracs, we show that our results can be extended to sample Diracs integrated along the azimuth. Finally, we consider kernels with antipodal symmetry which are common in applications such as diffusion magnetic resonance imaging.
Automatic measurements of choroidal thickness in EDI-OCT images

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, measurement of choroidal thickness depends on the manual labeling, which is tedious and subjective of inter-observer differences. In this paper, we propose a fast and accurate algorithm that could measure the choroidal thickness automatically. The lower boundary of the choroid is detected by searching the biggest gradient value above the retinal pigment epithelium (RPE) and the upper boundary is formed by finding the shortest path of the graph formed by valley pixels using dynamic programming. The average of Dice's Coefficient on 10 EDI-OCT images is 94.3%, which shows good consistency of the algorithm with the manual labeling. The processing time for each image is about 2 seconds.
Imperceptible data embedding in sharply-contrasted binary images

Data embedding in sharply-contrasted binary images like text, drawing, signature and cartoon is a challenging issue due to simple pixel statistics in such images. Arbitrary modification to the pixels can be visually perceptible in the process of data embedding. The use of a valid perceptual model is important to minimize the effect of such visual distortion in binary images. In this paper, a novel perceptual model is used to embed significant amount of information such that the original and the marked images before and after data embedding process are perceptually similar. In our model, the distortion that occurs after flipping a pixel is estimated on the curvature-weighted distance difference (CWDD) measure between two contour segments.
Slant transform watermarking for textured images

We propose a digital watermarking algorithm based on the Slant transform for the copyright protection of complex textured images. A lot of images in practical applications, such as remote sensing and medical archiving, contain complex textures. With more AC energies concentrating in the high frequency components, images with complex textures usually suffer more from common image processing operations than smooth images do. Therefore, watermarking robustness is more an issue for textured images though it is commonly recognized that highly textured images provide more capacity for data hiding without introducing too much degradation to the visual quality of original images. Experimental results showed that our Slant transform watermarking provides significant advantage for watermarking insertion and retrieval for images with complex textures such as satellite images. An analytical comparative study on the performance of the Slant transform adapting our earlier watermarking schemes for fast Hadamard transform will be performed based on its robustness against various Stirmark and Checkmark attacks.
Analysis of Neonatal EEG Signals using Stockwell Transform

In this paper, we investigate the Stockwell transform, a linear time-frequency spectral localisation technique, on non-stationary, multicomponent neonatal seizure EEG signals. The seizure signals of interest are namely slow wave and sharp spike seizures. The performance of Stockwell transform is compared to that of existing quadratic time-frequency representation, namely the Choi-Williams Distribution and the B Distribution, on both simulated and real EEG seizure signals. The results show that the Stockwell Transform yields distinctive, interference free time-frequency patterns corresponding to the neonatal EEG seizure signals. By capturing both high- frequency spike components and predominantly low frequency components of neonatal seizures concurrently and accurately, the Stockwell Transform is able to distinguish these two types of neonatal seizure signals with unique signatures. These signatures can then be effectively used for seizure modelling, detection and prediction.
Comparison of scene-based interpolation methods applied to CT abdominal images

Three-dimensional (3-D) interpolation from 2-D image slices is widely used to aid the display, analysis and other biomedical image processing. We investigate the performance of 5 scene-based interpolation methods: linear, cubic spline, modified cubic spline and sine-based functions (Dirichlet apodization and Hanning apodization). We test our methods on four sets of computed tomography (CT) abdominal images, which have more organs in them compared to other biomedical images. Results show that, contrary to the 1-D or 2-D cases, linear interpolation acts as well as, even slightly better than all the other methods in the sense of signal to noise ratio in most cases, while the computational load of linear interpolation is only about half of the other methods. The reason for the relative high performance of linear interpolation is probably the large distance between consecutive images, which indicates low inter-slice correlation.