nils-herrmann/clean_scirepeval huggingface dataset:
3D face recognition based on evolution of iso-geodesic distance curves

This paper presents a novel 3D face recognition method by means of the evolution of iso-geodesic distance curves. Specifically, the proposed method compares two neighboring iso-geodesic distance curves, and formalizes the evolution between them as a one-dimensional function, named evolution angle function, which is Euclidean invariant. The novelty of this paper consists in formalizing 3D face by an evolution angle functions, and in computing the distance between two faces by that of two functions. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 shows that our approach works very well on both neutral faces and non-neutral faces. By introducing a weight function, we also show a very promising result on non-neutral face database.
Robust independent component analysis

Independent component analysis (ICA) attempts to separate independent components present in the mixture signals. Several criteria have been suggested for ICA in the past, including kurtosis and negentropy. Kurtosis suffers from a drawback of being outlier sensitive. As a remedy, we propose robust ICA (RICA), which employs appropriate robust estimators. In this paper, we compare the robustness properties of RICA with kurtosis- and negentropy-based ICA. Since robust estimators are insensitive to outliers in contrast to maximum likelihood estimates (MLE), we demonstrate that in the presence of outliers, RICA works better than kurtosis- and negentropy-based ICA
Segmentation and compression of SAR imagery via hierarchical stochastic modeling

To abate the enormous costs incurred in the transmission and storage of SAR data, we present a segmentation driven compression technique using hierarchical stochastic modeling within a multiscale framework. Our approach to SAR image compression is unique in that we exploit the multiscale stochastic structure inherent in SAR imagery. This structure is well captured by a set of scale auto-regressive models that accurately characterize the evolution in scale. We thus use the local evolution in scale of SAR imagery to generate a segmentation map which is then used in tandem with the corresponding models to provide a robust, hierarchical compression technique.
On sampling planar curves

In this paper we address the problem of sampling and perfect reconstruction of closed planar curves belonging to a certain class. We successively study the following problems: (1) identifying the class of curves yielding perfect reconstruction from their samples and establishing efficient class identification procedures, (2) sampling and reconstruction algorithms, (3) applicability of general sampling methods to curves not satisfying the proposed model constraints. We propose a theoretically efficient and numerically implementable solution and illustrate its effectiveness with a number of examples.
Sparse null space basis pursuit and analysis dictionary learning for high-dimensional data analysis

Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently been of increasing research interest. Another interesting related problem based on a linear equality constraint, namely the sparse null space problem (SNS), first appeared in 1986, and has since inspired results on sparse basis pursuit. In this paper, we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role, and may be utilized to solve dictionary learning problems. Moreover, we propose an efficient algorithm of sparse null space basis pursuit, and extend it to a solution of analysis dictionary learning. Experimental results on numerical synthetic data and real-world data are further presented to validate the performance of our method.
Spatio-temporal wavelet analysis of body surface potential maps during PTCA-induced ischemia

Body surface potential maps taken during PTCA procedures are analyzed using the spatio-temporal wavelet transform (WT). Results are compared to a standard technique, integral mapping. The wavelet transform (WT) allows greater temporal resolution than integral maps, reproducing integral patterns but revealing additional information over smaller time scales. The spatial WT provides a compact representation of the maps to be used for quantification and pattern recognition.
Performance analysis of smoothed subspace-based estimation methods

A formal approach is presented to carry out a performance analysis of subspace-based estimation techniques applied to the problem of the direction of arrival of plane waves. This approach was inspired by the operator formalism, on account of its algebraic simplicity. It is a generalized approach that can be easily modified to analyze any subspace-based technique, as it uses a series expansion of projection operators on the signal and noise subspaces. A perturbation analysis is performed on the operators, thus avoiding a direct use of the eigenvectors and eigenvalues. This allows the analysis to be carried out to any desired order. This is made possible by using an original recurrence formula developed for the higher order terms in the expansion of the projection operators. This method is applied to study the root-min-norm algorithm with uncorrelated signals impinging from distinct directions on a uniform linear array and then extended to the correlated signal scenario. Simulations which verify the analytical derivations are provided.<<ETX>>
Image segmentation and edge enhancement with stabilized inverse diffusion equations

We introduce a family of first-order multidimensional ordinary differential equations (ODEs) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations "stabilized inverse diffusion equations" (SIDEs). Existence and uniqueness of solutions, as well as stability, are proven for SIDEs. A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona-Malik equation. In an experiment, SIDE's are shown to suppress noise while sharpening edges present in the input signal. Their application to image segmentation is also demonstrated.
On the detection of abandoned objects with a moving camera using robust subspace recovery and sparse representation

We consider the application of sparse-representation and robust-subspace-recovery techniques to detect abandoned objects in a target video acquired with a moving camera. In the proposed framework, the target video is compared to a previously acquired reference video, which is assumed to have no abandoned objects. The detection method explores the low-rank similarities among the reference and target videos, as well as the sparsity of the differences between the two video sequences caused by the unexpected object in the target video. A three-step procedure is then presented adapting a previous low-rank and sparse image representation to the problem at hand. Performance of the proposed technique is verified using a large video database for abandoned-object detection in a cluttered environment. Results demonstrate the technique effectiveness even in the presence of some significant camera shake along its trajectory.
Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function

In this paper, we present a novel intrinsic geometric representation of 3D objects. We add the proposed modeling of objects to their topological graphs to ensure a full and compact description necessary for shape-based retrieval, recognition and analysis of 3D models. In our approach, we address the challenges due to pose variability, computational complexity and noisy data by intrinsically and simply describing a 3D object by a global geodesic function. We exploit the geometric features contained in the dense set of iso-levels of this function. Using Whitney easy embedding theorem, we embed the manifold of the geodesic iso-levels in Ropf3 and obtain a single space curve as our geometry descriptor. 3D shape comparison is then reduced to comparing the resulting modeling curves. To quantify the dissimilarities between them we simply compute an L2 distance between classical Euclidian invariants applied to space curves. The experimental results show that in addition to being straightforward and easy to compute, our modeling technique achieves a high level of discrimination, and appears to be robust to both noise and decimation.
Tracking nonstationarities with a wavelet transform

Nonstationary signal parameter estimation/detection is challenging on account of the underlying stationarity assumption in most of the classical techniques. The authors present a framework for a class of nonstationary processes via a multiscale analysis. This framework gives insight into the problem, and new results are obtained on multiscale autoregressive integrated moving average (ARIMA) processes. The possibility of inducing stationarity at different resolution levels of nonstationary processes by an appropriate wavelet transform is shown. This permits use of classical estimation/detection techniques. The approach is extended to wavelet package decompositions.<<ETX>>
Brain MRI T1-Map and T1-weighted image segmentation in a variational framework

In this paper we propose a constrained version of Mumford-Shah's[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T1-Map and T1-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the three-tissue segmentation possible. Our method uses region based active contours which have proven to be robust. The method is validated by two real objects which were used to generate T1-Maps and also by two simulated brains of T1-weighted data from the BrainWeb[3] public database.
3D Face Recognition using Euclidean Integral Invariants Signature

A novel 3D face representation and recognition approach is presented in this paper. We represent a 3D face by a set of level curves of geodesic function starting from the nose tip, which is invariant under isometric transformation of the surfaces. A pose change induces a special Euclidean transformation (a composition of a rotation and a translation) of the surface that represents a face and leads to the Euclidean transformation of the iso-geodesic curves. A change of facial expression induces isometric transformation of the iso-geodesic curves. Although the set of isometric transformations of a surface is larger than the set of Euclidean transformations in 3D, we assume that iso-geodesic curves undergo piecewise Euclidean transformations, i.e. the transformation of relatively small segments of the level curves is Euclidean. A Euclidean invariant integral signature for curves in 3D is presented in this paper. Euclidean invariant integral signature provides a classification of spatial curves which is independent of their position in 3D space and parameterization, and is not sensitive to noise. A recognition procedure based on comparing face feature in the invariant signature space is proposed. Substantiating examples are provided with an achieved classification accuracy of 95% for faces with various poses and facial expressions.
A generalized divergence measure for robust image registration

Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. We define a new generalized divergence measure, namely, the Jensen-Renyi (1996, 1976) divergence. Some properties such as convexity and its upper bound are derived. Based on the Jensen-Renyi divergence, we propose a new approach to the problem of image registration. Some appealing advantages of registration by Jensen-Renyi divergence are illustrated, and its connections to mutual information-based registration techniques are analyzed. As the key focus of this paper, we apply Jensen-Renyi divergence for inverse synthetic aperture radar (ISAR) image registration. The goal is to estimate the target motion during the imaging time. Our approach applies Jensen-Renyi divergence to measure the statistical dependence between consecutive ISAR image frames, which would be maximal if the images are geometrically aligned. Simulation results demonstrate that the proposed method is efficient and effective.
BI-sparsity pursuit for robust subspace recovery

The success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result, a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness of our method by experiments on real-world vision data.
Distributed Localization of Coverage Holes Using Topological Persistence

We develop distributed algorithms to detect and localize coverage holes in sensor networks. We neither assume coordinate information of the nodes, neither any distances between the nodes. We use algebraic topological methods to define a coverage hole, and develop provably correct algorithm to detect a hole. We then partition the network into smaller subnetworks, while ensuring that the holes are preserved, and checking for holes in each. We show that repeating this process leads to localizing the coverage holes. We demonstrate the improved complexity of our algorithm using simulations.
Edge detection and processing using shearlets

Mathematically wavelets are not very effective in representing images containing distributed discontinuities such as edges. This paper deals with a new multiscale directional representation called the shearlet transform that has been shown to represent specific classes of images with edges optimally. Techniques based on this transform for edge detection and analysis are presented. Unlike previously developed directional filter based techniques for edge detection, shearlets provide a theoretical basis for characterizing how edges will behave in such representations. Experiments demonstrate that this novel approach is very competitive for the purpose of edge detection and analysis.
3D Mixed Invariant and its Application on Object Classification

A new integro-differential invariant for curves in 3D transformed by affine group action is presented in this paper. The derivatives involved are of the first order, and therefore this invariant is significantly less sensitive to noise than classical affine differential invariants, the simplest of which involves derivatives of order 5. A classification procedure based on characteristic curves of an object surface is considered using our proposed mixed invariants. Substantiating examples are provided to verify efficiency and discriminant power of the characteristic spatial curve based 3D object classification.
Minimax Description Length for Signal Denoising and Optimized Representation

Approaches to wavelet-based denoising (or signal enhancement) have generally relied on the assumption of normally distributed perturbations. To relax this assumption, which is often violated in practice, we derive a robust wavelet thresholding technique based on the minimax description length (MMDL) principle. We first determine the least favorable distribution in the /spl epsiv/-contaminated normal family as the member that maximizes the entropy. We show that this distribution, and the best estimate based upon it, namely the maximum-likelihood estimate, together constitute a saddle point. The MMDL approach results in a thresholding scheme that is resistant to heavy tailed noise. We further extend this framework and propose a novel approach to selecting an adapted or best basis (BB) that results in optimal signal reconstruction. Finally, we address the practical case where the underlying signal is known to be bounded, and derive a two-sided thresholding technique that is resistant to outliers and has bounded error.
Robust wavelet thresholding for noise suppression

Approaches to wavelet-based denoising (or signal enhancement) have so far relied on the assumption of normally distributed perturbations. To relax this assumption, which is often violated in practice, we derive a robust wavelet thresholding technique based on the minimax description length principle. We first determine the least favorable distribution in the /spl epsi/-contaminated normal family as the member that maximizes the entropy. We show that this distribution and the best estimate based upon it, namely the maximum likelihood estimate, constitute a saddle point. This results in a threshold that is more resistant to heavy-tailed noise, but for which the estimation error is still potentially unbounded. We address the practical case where the underlying signal is known to be bounded, and derive a two-sided thresholding technique that is resistant to outliers and has bounded error. We provide illustrative examples.
Persistent Homology of Delay Embeddings and its Application to Wheeze Detection

We propose a new approach to detect and quantify the periodic structure of dynamical systems using topological methods. We propose to use delay-coordinate embedding as a tool to detect the presence of harmonic structures by using persistent homology for robust analysis of point clouds of delay-coordinate embeddings. To discover the proper delay, we propose an autocorrelation like (ACL) function of the signals, and apply the introduced topological approach to analyze breathing sound signals for wheeze detection. Experiments have been carried out to substantiate the capabilities of the proposed method.
Two decades of array signal processing research: the parametric approach

The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.
A topological collapse for document summarization

As a useful tool to summarize documents, keyphrase extraction extracts a set of single or multiple words, called keyphrases, that capture the primary topics discussed in a document. In this paper we propose DoCollapse, a topological collapse-based unsupervised keyphrase extraction method that relies on networking document by semantic relatedness of candidate keyphrases. A semantic graph is built with candidates keyphrases as vertices and then reduced to its core using topological collapse algorithm to facilitate final keyphrase selection. Iteratively collapsing dominated vertices aids in removing noisy candidates and revealing important points. We conducted experiments on two standard evaluation datasets composed of scientific papers and found that DoCollapse outperforms state-of-the-art methods. Results show that simplifying a document graph by homology-preserving topological collapse benefits keyphrase extraction.
An information theoretic approach for speech source enumeration

The solution of speech related problems such as source location or separation relies on a prior estimation of the number of sources. In this paper we propose a method for speech source enumeration based on the different relative delays that sources at different locations register at two microphones. The Probability Density Function (PDF) of the estimated delays exhibits peaks associated with each source. The Minimum Description Length (MDL) criterion is applied to the prediction error of a linear model fitted to the delay estimates. The method is validated for the estimation of different number of sources and different mixtures.
Real-time multiple DOA estimation of speech sources in wireless acoustic sensor networks

Indoor localization of multiple speech sources in wireless acoustic sensor networks (WASNs) is an open and interesting problem with many practical applications, but the presence of noise and reverberations complicates the problem. In this paper, a distributed algorithm for multiple DOA estimation of speech sources in WASNs is presented. The method exploits the sparsity of speech sources in the time-frequency domain to obtain DOA estimations locally in each node of the network. The DOA estimations of different nodes are further combined to increase the accuracy of the local DOA estimations. Since the local DOAs are estimated using only the microphones of the same node, the synchronization between input channels and localization of the microphones from different nodes are not an issue.
Mahalanobis-based Adaptive Nonlinear Dimension Reduction

We define a new adaptive embedding approach for data dimension reduction applications. Our technique entails a local learning of the manifold of the initial data, with the objective of defining local distance metrics that take into account the different correlations between the data points. We choose to illustrate the properties of our work on the isomap algorithm. We show through multiple simulations that the new adaptive version of isomap is more robust to noise than the original non-adaptive one.
Compression and transmission of facial images over very narrowband wireless channels

Law enforcement officers on mobile duty are often confronted with ID authentication of subjects entailing the transmission of a driver's license picture over wireless channels that are very narrowband. To access mug shots in a reliable and timely manner, a real time compression and decompression method with high compression ratios is required at the server database and the mobile client unit. The presented technique minimizes the size of the data sent over the channel by locally storing common features of the human face in the client computers. Pre-processing of server database images, such as facial feature extraction, are used to extract these common facial features, obtained via ravines and image singularities. The implemented file transfer protocols are based on basic TCP/IP client-server models and make use of socket programming. Experimental results show a 5/spl times/ improvement in transfer time over typically saturated channels.