nils-herrmann/clean_scirepeval huggingface dataset:
Nine Voices, One Artist: Linguistic and Acoustic Analysis

Voice impersonators possess a flexible voice and thus can change their voice identity. They are able to imitate various people and characters which differ in age, gender, accent and voice quality. State of the art electronic voice conversion systems are not able to successfully mimic their human counterparts as they lack naturalness. To understand why human impersonators are successful and what parameters they rely on to change their voice, we analyze nine voices produced by a professional voice impersonator. We compute different acoustical measures and discuss their linguistic implications. The acoustical measures include pitch, speech rate and formant frequencies. Our results show that differences in the voice identity features such as age and gender are reflected in the acoustic parameters of the impersonations. The analysis is distinguished from previous studies on impersonators in giving full consideration to voice identity features.
Glottal activity detection using Finite Rate of Innovation methods

A novel algorithm which accurately determines the glottal closing instants and glottal opening instants from the Differentiated Electroglottographic signals is presented.The glottal closing instants and glottal opening instant are modeled as non-bandlimited signals and methods for sampling and reconstructing signals with Finite Rate of Innovation are then used to recover the glottal closing instants and glottal opening instants. In comparison with existing methods, the proposed scheme achieves improved performance by providing 99% reliable estimates of glottal closing and opening instants respectively.
Need for speed: Fast Stockwell transform (FST) with O(N) complexity

In this paper, we propose two fast, spline based, algorithms for computing the Stockwell Transform or the S-transform. It is a redundant, time-frequency representation that has certain desirable features which make it an attractive choice for signal analysis in different areas and motivated by its diverse applications, we seek to reduce its computational complexity. The S-transform bears an acute resemblance with the Gabor transform and can also be associated to the Continuous Wavelet Transform (CWT). Our formulation is based on the above mentioned connectivity with the two classical time-frequency tools. What singles out our approach is that it is recursive in nature and leads to a complexity of O(N) - for arbitrary scales, independent of scale of window.
P and T wave detectionon multichannel ECG using FRI

This paper proposes a new method for detecting P and T waves in multilead ECG based on the Finite Rate of Innovation(FRI) technique [8]. A simple QRS detection scheme will be presented followed by a novel P and T wave detection algorithm. The novelty here is the modelling of the P and T wave using a Gaussian kernel. Using a 2D wavelet decomposition, the approximation coefficients are windowed based on the QRS locations. The FRI method is then used to identify the Gaussian distribution present in the window which will in turn provide the locations of the P and T wave. This method was tested on more than an hour of clean and noisy data and shows good performance in the noisy case.
Perception based binary image watermarking

The use of a suitable perceptual model is necessary to minimize visual distortion in marked images, because minor modifications to the pixels can be perceptible since the pixels are either black or white. In this paper, a new perceptual model is proposed for binary images that is useful for data hiding applications. In our model, the distortion that occurs after flipping a pixel is estimated on the novel curvature-weighted distance difference (CWDD) measure between two contour segments. Through subjective tests the perceptual measure is validated and highly correlated with human perception.
Magnetic resonance image reconstruction using the annihilating filter method

Compressed sensing reconstruction algorithms exploit the sparsity of MRI images to significantly undersample the k-space. However, these algorithms are computationally expensive, may be slow to converge, and perform best when the samples are randomly selected. We propose a new sparse reconstruction algorithm based on the annihilating filter method to palliate these issues. This new method is non iterative and does not require random sampling. We demonstrate that our technique outperforms the basis pursuit theoretical limit for very sparse signals. As an application, we show clinical MRI images reconstructed using our method.
Robust Video Watermarking of H.264/AVC

A robust video watermarking scheme of the state-of-the-art video coding standard H.264/AVC is proposed in this brief. 2-D 8-bit watermarks such as detailed company trademarks or logos can be used as inconvertible watermark for copyright protection. A grayscale watermark pattern is first modified to accommodate the H.264/AVC computational constraints, and then embedded into video data in the compressed domain. With the proposed method, the video watermarking scheme can achieve high robustness and good visual quality without increasing the overall bit-rate. Experimental results show that our algorithm can robustly survive transcoding process and strong common signal processing attacks, such as bit-rate reduction, Gaussian filtering and contrast enhancement
Liver Tumor Volume Estimation By Semi-Automatic Segmentation Method

Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year. In order to make decisions such as liver resections, doctors will need to know the tumor volume, and further, the functional liver volume. Thus, an important task in radiology is the determination of tumor volume. Accurate segmentation of liver tumor from an abdominal image is one of the most important steps in 3D representation for liver volume measurement, liver transplant, and treatment planning. Since manual segmentation is inconvenient, time consuming and depends on the individual operator to a large extent, automatic segmentation is much more preferred. In this paper, an active contour model is used to segment tumors from CT abdominal images. Initial boundary is manually placed by operators outside the tumor region. The snake deforms to the tumor boundary with the minimization of energy function. We then calculate the tumor volume using the series of segmented tumor slices. Results show that this method is quite efficient in tumor volume estimation compared with the WHO criteria, which measures the tumor by multiplying the longest perpendicular diameters
Influence of polarity and field non-uniformity on the breakdown voltage characteristics of SF/sub 6/ gas under impulse voltages

A comprehensive study of electrical breakdown in SF/sub 6/ gas has been carried out under both polarities of lightning impulse (1.2/50 /spl mu/s) (LI) and switching impulse (250/2500 /spl mu/s) (SI) voltages over a pressure range of 0.1 to 0.49 MPa, using a rod-plane electrode configuration with various rod diameters (D) of sharp needle, (D/spl sime/0 mm), 1.5, 2, 3, 6 and 12 mm and for different gap spacings varying from 10 to 100 mm. Experiments were also conducted using 50 Hz a.c. voltages under the same experimental conditions for different gap spacings of 10 and 20 mm. The influence of field nonuniformity, the polarity and the type of voltage on the breakdown voltage characteristics of SF/sub 6/ gas have been analyzed. The impulse ratios have been calculated and their variation have been explained in terms of fundamental physical processes.<<ETX>>
`Print and Scan' Resilient Data Hiding in Images

Print-scan resilient data hiding finds important applications in document security and image copyright protection. This paper proposes methods to hide information into images that achieve robustness against printing and scanning with blind decoding. The selective embedding in low frequencies scheme hides information in the magnitude of selected low-frequency discrete Fourier transform coefficients. The differential quantization index modulation scheme embeds information in the phase spectrum of images by quantizing the difference in phase of adjacent frequency locations. A significant contribution of this paper is analytical and experimental modeling of the print-scan process, which forms the basis of the proposed embedding schemes. A novel approach for estimating the rotation undergone by the image during the scanning process is also proposed, which specifically exploits the knowledge of the digital halftoning scheme employed by the printer. Using the proposed methods, several hundred information bits can be embedded into images with perfect recovery against the print-scan operation. Moreover, the hidden images also survive several other attacks, such as Gaussian or median filtering, scaling or aspect ratio change, heavy JPEG compression, and rows and/or columns removal
Dimensionality reduction for image retrieval

Dimensionality reduction methods are of interest in applications such as content based image and video retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve the nearest neighbors of a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. We investigate the use of weighted multi-dimensional scaling (WMDS) for dimensionality reduction. The main objective of the WMDS is to preserve the local topology of the high dimensional space, i.e., to map the nearest neighbors in the high dimensional space to nearest neighbors in the lower dimensional space. In addition to the well known retrieval accuracy as a measure of performance, we propose two additional measures that take into account the ordinal relationships among the nearest neighbors. Experimental results are given.
Not all tags are created equal: Learning flickr tag semantics for global annotation

Large collaborative datasets offer the challenging opportunity of creating systems capable of extracting knowledge in the presence of noisy data. In this work we explore the ability to automatically learn tag semantics by mining a global georeferenced image collection crawled from Flickr with the aim of improving an automatic annotation system. We are able to categorize sets of tags as places, landmarks, and visual descriptors. By organizing our dataset of more than 1.69 million images using a quadtree we can efficiently find geographic areas with sufficient density to provide useful results for place and landmark extraction. Precision-recall curves for our techniques compared with previous existing work used to identify place tags and manual groundtruth landmark annotation show the merit of our methods applied on a world scale.
System for automatic registration of remote sensing images

Describes a system for automatic and semi-automatic registration/mosaic of remote sensing images. Information provided by the user can be used to speed up processing or to avoid mismatched control points. A statistical procedure is used to characterize good and bad registrations. Based on this "good fit-bad fit" statistical test the user can stop, modify the parameters, or continue the processing. Several tests have been performed by registering optical, radar, multi-sensor, high-resolution images and video sequences. We have included very difficult image registration examples in order to show the strengths and limits of our system. An online registration system demo containing several examples can be executed using a Web browser.
Object Tracking with Ratio Cycles Using Shape and Appearance Cues

We present a method for object tracking over time sequence imagery. The image plane is represented with a 4-connected planar graph where vertices are associated with pixels. On each image, the outer contour of the object is localized by finding the optimal cycle in the graph such that a cost function based on temporal, appearance and shape priors is minimized. Our contribution is the particle filtering-based framework to integrate the shape cue with the temporal and appearance cues. We demonstrate that incorporating the shape prior yields promising performance improvement over temporal and appearance priors on various object tracking scenarios.
Camera Alignment Using Trajectory Intersections in Unsynchronized Videos

This paper addresses the novel and challenging problem of aligning camera views that are unsynchronized by low and/or variable frame rates using object trajectories. Unlike existing trajectory-based alignment methods, our method does not require frame-to-frame synchronization. Instead, we propose using the intersections of corresponding object trajectories to match views. To find these intersections, we introduce a novel trajectory matching algorithm based on matching Spatio-Temporal Context Graphs (STCGs). These graphs represent the distances between trajectories in time and space within a view, and are matched to an STCG from another view to find the corresponding trajectories. To the best of our knowledge, this is one of the first attempts to align views that are unsynchronized with variable frame rates. The results on simulated and real-world datasets show trajectory intersections are a viable feature for camera alignment, and that the trajectory matching method performs well in real-world scenarios.
Nearest neighbor search for relevance feedback

We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support high-level concept queries. The paper addresses those scenarios in which a similarity or distance matrix is updated during each iteration of the relevance feedback search and a new set of nearest neighbors is computed. This repetitive nearest neighbor computation in high dimensional feature spaces is expensive, particularly when the number of items in the data set is large. In this context, we suggest a search algorithm that supports relevance feedback for the general quadratic distance metric. The scheme exploits correlations between two consecutive nearest neighbor sets thus significantly reducing the overall search complexity. Detailed experimental results are provided using 60 dimensional texture feature dataset.
Content-based search of video using color, texture, and motion

We present an implementation of a system for content-based search and retrieval of video based on low-level visual features. The system consists of three parts, automatic video partition, feature extraction, video search and retrieval. Three primary features, color texture and motion are used for indexing. They are represented by color histograms, Gabor texture features, and motion histograms. Most of the processing is done directly in the MPEG compressed domain. Testing on sports and movie databases have shown good retrieval performance.
Optical-to-SAR image registration using the active contour model

An elastic contour matching method for the registration of the optical images with synthetic aperture radar (SAR) images is proposed. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying an active contour model (Snake). The centroids of the closed contours and the salient points along the open contours are used as control points, through which the transformation parameters between the images can be estimated. The proposed algorithm has outperformed manual registration in terms of root mean square error at the control points.<<ETX>>
An efficient low-dimensional color indexing scheme for region-based image retrieval

In this work, an efficient low-dimensional color indexing scheme for region-based image retrieval is presented. The colors in each image region are first quantized so that only a small number of cluster centroids are needed to represent the region color information. The proposed color feature descriptor consists of these quantized colors and their percentages in the region. A similarity distance measure is defined and shown to be equivalent to the quadratic color histogram distance measure. The quantized colors are indexed in the 3-D color space so that high-dimensional indexing can be avoided. During the search process, each quantized color in the query is used as a separate cue to find matches containing that color. The matches from all the query colors are then joined to obtain the final retrievals. Experimental results show that the proposed scheme is fast and accurate compared to the color histogram approach.
Pattern retrieval in image databases based on adaptive signal decomposition

Efficient retrieval of information from image and other multimedia databases is an important and challenging problem. Among techniques developed for this purpose, query by example is the most effective way for searching large collections of images. In this paper we propose a new approach to extracting image features based on the local intensity pattern. The user can select any region of interest within an image and request that the system retrieve all similar looking patterns. No domain specific or context information is used in analysis, and the feature representation has desirable properties such as reduced dimensionality, preserving perceptual similarity, and offering a natural hierarchical indexing structure. Experimental results are presented.<<ETX>>
Video Annotation Through Search and Graph Reinforcement Mining

Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media databases with independent annotation instances, we present an approach to automatically annotate multimedia documents that uses mining techniques to discover new annotations from similar documents and to filter existing incorrect annotations. The annotation set is not limited to words that have training data or for which models have been created. It is limited only by the words in the collective annotation vocabulary of all the database documents. A graph reinforcement method driven by a particular modality (e.g., visual) is used to determine the contribution of a similar document to the annotation target. The graph supplies possible annotations of a different modality (e.g., text) that can be mined for annotations of the target. Experiments are performed using videos crawled from YouTube. A customized precision-recall metric shows that the annotations obtained using the proposed method are superior to those originally existing for the document. These extended, filtered tags are also superior to a state-of-the-art semi-supervised technique for graph reinforcement learning on the initial user-supplied annotations.
Shape prior segmentation of multiple objects with graph cuts

We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.
Multi-Label Learning With Fused Multimodal Bi-Relational Graph

The problem of multi-label image classification using multiple feature modalities is considered in this work. Given a collection of images with partial labels, we first model the association between different feature modalities and the images labels. These associations are then propagated with a graph diffusion kernel to classify the unlabeled images. Towards this objective, a novel Fused Multimodal Bi-relational Graph representation is proposed, with multiple graphs corresponding to different feature modalities, and one graph corresponding to the image labels. Such a representation allows for effective exploitation of both feature complementariness and label correlation. This contrasts with previous work where these two factors are considered in isolation. Furthermore, we provide a solution to learn the weight for each image graph by estimating the discriminative power of the corresponding feature modality. Experimental results with our proposed method on two standard multi-label image datasets are very promising.
Fast and Adaptive Pairwise Similarities for Graph Cuts-based Image Segmentation

We introduce the use of geodesic distances and geodesic radius for calculating pairwise similarities needed in various graph cuts based methods. By using geodesics on an edge strength function we are able to calculate similarities between pixels in a more natural way. Our technique improves the speed and reliability of calculating similarities and leads to reasonably good image segmentation results. Our algorithm takes an edge strength function as its input and its speed is independent of the feature dimension or the distance measure used.
Retina Layer Segmentation and Spatial Alignment of Antibody Expression Levels

The expression levels of rod opsin and glial fibrillary acidic protein (GFAP) capture important structural changes in the retina during injury and recovery. Quantitatively measuring these expression levels in confocal micrographs requires identifying the retinal layer boundaries and spatially corresponding the layers across different images. In this paper, a method to segment the retinal layers using a parametric active contour model is presented. Then spatially aligned expression levels across different images are determined by thresholding the solution to a Dirichlet boundary value problem. Our analysis provides quantitative metrics of retinal restructuring that are needed for improving retinal therapies after injury.
Graph partitioning active contours (GPAC) for image segmentation

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.