nils-herrmann/clean_scirepeval huggingface dataset:
Semiparametric skew distributions in shape representation

In this paper, we address the problem of shape modeling and template learning from a novel viewpoint - using a new class of semiparametric skew-symmetric distributions. The proposed method, called "semi-parametric skew-symmetric shape model" (SSSM) learns the template from several observed realizations of a shape and models them as a joint distribution of angle and distance from the centroid of all points on the boundary. To be specific, we are interested in learning templates for simple shapes, which may, however, have arbitrary and irregular boundary. The method entails sampling an aggregate of realizations and identifying scattered data points of the available boundary realizations that lie within some neighborhood of a given angle. From the clusters of data points, we learn a bimodal distribution of the radii distances for given angles and subsequently synthesize the overall joint distributions according to some prior on the angles. Finally, we substantiate our proposed methodology with a number of examples.
Towards a unified view of estimation: variational vs. statistical

A connection between the maximum a posteriori (MAP) estimation and the variational formulation based on the minimization of a given variational integral subject to some noise constraints is established in this paper. A MAP estimator which uses a Markov or a maximum entropy random field model for the prior distribution can be viewed as a minimizer of a variational problem. Inspired by the maximum entropy principle, a nonlinear variational filter called improved entropic gradient descent flow is proposed. It minimizes a hybrid functional between the neg-entropy variational integral and the total variation subject to some noise constraints. Simulation results showing a much improved performance of the proposed filter in the presence of Gaussian and Laplacian noise are analyzed and illustrated.
On spatial smoothing and linear prediction

The relationship between Cadzow's signal subspace algorithm and the spatially smoothed minimum-norm algorithm of Tufts-Kumaresan is investigated. It is shown that Cadzow's algorithm can be realized by subarray averaging lower rank approximations to the array covariance matrix. A data-domain algorithm that is applicable in a correlated signal environment is formulated. This algorithm offers the advantage of lower word length requirements, and obviates the necessity of computing higher order statistics. It may also be extended in a straightforward way to incorporate signal enumeration. Simulation results are given that contrast the performance of this algorithm to the signal eigenvector approach.<<ETX>>
Introduction to the special session on Topological Data Analysis, ICASSP 2016

Topological Data Analysis (TDA) is a topic which has recently seen many applications. The goal of this special session is to highlight the bridge between signal processing, machine learning and techniques in topological data analysis. In this way, we hope to encourage more engineers to start exploring TDA and its applications. This paper briefly introduces the standard techniques used in this area, delineates the common theme connecting the works presented in this session, and concludes with a brief summary of each of the papers presented.
Simplifying the homology of networks via strong collapses

There has recently been increased interest in applications of topology to areas ranging from control and sensing, to social network analysis, to high-dimensional point cloud data analysis. Here we use simplicial complexes to represent the group relationship structure in a network. We detail a novel algorithm for simplifying homology and “hole location” computations on a complex by reducing it to its core using a strong collapse. We show that the homology and hole locations are preserved and provide motivation for interest in this reduction technique with applications in sensor and social networks. Since the complexity of finding “holes” is quintic in the number of simplices, the proposed reduction leads to significant savings in complexity.
Robust detection of periodic patterns in gene expression microarray data using topological signal analysis

In this paper, we present a new approach for analyzing gene expression data that builds on topological characteristics of time series. Our goal is to identify cell cycle regulated genes in micro array dataset. We construct a point cloud out of time series using delay coordinate embeddings. Persistent homology is utilized to analyse the topology of the point cloud for detection of periodicity. This novel technique is accurate and robust to noise, missing data points and varying sampling intervals. Our experiments using Yeast Saccharomyces cerevisiae dataset substantiate the capabilities of the proposed method.
An information divergence measure for ISAR image registration

Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. A generalized information-theoretic measure called Jensen-Renyi divergence is proposed. Some properties such as convexity and its upper bound are derived. Using the Jensen-Renyi divergence, we propose a new approach to the problem of ISAR (inverse synthetic aperture radar) 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 a much improved performance of the proposed method in image registration.
A stochastic diffusion approach to signal denoising

We present a stochastic formulation of a linear diffusion equation (or heat equation), and in light of the potential applications ranging from signal denoising to image enhancement/segmentation of its nonlinear extensions, we propose a more general nonlinear stochastic diffusion. The constructed stochastic framework, in contrast to traditional deterministic approaches, unveils the sources of of existing limitations and allows us to further significantly improve the performance by addressing the key problem. Substantiating examples are provided.
Spectral estimation in highly transient data

We propose a new framework for estimating different frequencies in piece-wise periodic signals with time varying amplitude and phase. Through a 3-dimensional delay embedding of the introduced model, we construct a union of intersecting planes where each plane corresponds to one frequency. The equations of each of these planes only depend on the associated frequency, and are used to calculate the tone in each segment. A sparse subspace clustering technique is utilized to find the segmentation of the data, and the points in each cluster are used to compute the normal vectors. In the presence of white Gaussian noise, principal component analysis is used to robustly perform this computation. Experimental results demonstrate the effectiveness of the proposed framework.
Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm

We consider subspace clustering under sparse noise, for which a non-convex optimization framework based on sparse data representations has been recently developed. This setup is suitable for a large variety of applications with high dimensional data, such as image processing, which is naturally decomposed into a sparse unstructured foreground and a background residing in a union of low-dimensional subspaces. In this framework, we further discuss both performance and implementation of the key optimization problem. We provide an analysis of this optimization problem demonstrating that our approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. We also propose a sequential algorithmic solution, which is particularly useful for extremely large data sets and online vision applications such as video processing.
A model-free causality measure based on multi-variate delay embedding

We further delay embedding framework application to multiple time series for extraction of their potential causal interactions. We introduce a novel geometric model-free causality measure that can efficiently detect linear and nonlinear causal interactions between time series with no prior information or parameter estimation. Using multivariate delay embedding, we construct a point cloud from a set of time domain signals and propose the inverse of its fractal dimension as a causal interaction measure between the corresponding time series. Correlation dimension estimation is fully exploited as a fractal-based method for uncovering the dimensions of generated point clouds. Extensive simulation results are presented to substantiate the capabilities of the proposed approach.
Human Activity as a Manifold-Valued Random Process

Most of the previous shape-based human activity models are built with either a linear assumption or an extrinsic interpretation of the nonlinear geometry of the shape space, both of which proved to be problematic on account of the nonlinear intrinsic geometry of the associated shape spaces. In this paper, we propose an intrinsic stochastic modeling of human activity on a shape manifold. More importantly, within an elegant and theoretically sound framework, our work effectively bridges the nonlinear modeling of human activity on a nonlinear space, with the classic stochastic modeling in a Euclidean space, and thereby provides a foundation for a more effective and accurate analysis of the nonlinear feature space of activity models. From a video sequence, human activity is extracted as a sequence of shapes. Such a sequence is considered as one realization of a random process on a shape manifold. Different activities are then modeled as manifold valued random processes with different distributions. To address the problem of stochastic modeling on a manifold, we first construct a nonlinear invertible map of a manifold valued process to a Euclidean process. The resulting process is then modeled as a global or piecewise Brownian motion. The mapping from a manifold to a Euclidean space is known as a stochastic development. The advantage of such a technique is that it yields a one-one correspondence, and the resulting Euclidean process intrinsically captures the curvature on the original manifold. The proposed algorithm is validated on two activity databases and compared with the related works on each of these. The substantiating results demonstrate the viability and high-accuracy of our modeling technique in characterizing and classifying different activities.
Beyond union of subspaces: Subspace pursuit on Grassmann manifold for data representation

Discovering the underlying structure of a high-dimensional signal or big data has always been a challenging topic, and has become harder to tackle especially when the observations are exposed to arbitrary sparse perturbations. In this paper, built on the model of a union of subspaces (UoS) with sparse outliers and inspired by a basis pursuit strategy, we exploit the fundamental structure of a Grassmann manifold, and propose a new technique of pursuing the subspaces systematically by solving a non-convex optimization problem using the alternating direction method of multipliers. This problem as noted is further complicated by non-convex constraints on the Grassmann manifold, as well as the bilinearity in the penalty caused by the subspace bases and coefficients. Nevertheless, numerical experiments verify that the proposed algorithm, which provides elegant solutions to the sub-problems in each step, is able to de-couple the subspaces and pursue each of them under time-efficient parallel computation.
Multiphase Joint Segmentation-Registration and Object Tracking for Layered Images

In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.
Multi-modal image fusion using window-based ICA and fractal dimension

The goal of multi-modal image fusion is to combine complementary information from multisensory data such that the fused image is more suitable for the purpose of human visual perception, computer-processing tasks and detection applications. In this paper, we first use independent component analysis (ICA) as the primary transformation to obtain adaptive analysis bases trained by similar reference images; we then utilize Fractal Dimension (FD) to detect textural information and an intensity histogram to sense objects of interest as a new additional fusion rule. Substantiating examples show an improved performance in perception and in an enhancement of textural information and objects of interest.
Time-invariant orthonormal wavelet representations

A simple construction of an orthonormal basis starting with a so-called mother wavelet, together with an efficient implementation gained the wavelet decomposition easy acceptance and generated a great research interest in its applications. An orthonormal basis may not, however, always be a suitable representation of a signal, particularly when time (or space) invariance is a required property. The conventional way around this problem is to use a redundant decomposition. We address the time-invariance problem for orthonormal wavelet transforms and propose an extension to wavelet packet decompositions. We show that it,is possible to achieve time invariance and preserve the orthonormality. We subsequently propose an efficient approach to obtain such a decomposition. We demonstrate the importance of our method by considering some application examples in signal reconstruction and time delay estimation.
Fast minimization of structural risk by nearest neighbor rule

In this paper, we present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine (SVM) approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle, in a manner which does not involve selection of a convenient feature space. Simulation results on real data indicate that this method significantly reduces the computational cost of the conventional SVMs, and achieves a nearly comparable test error performance.
Identification of a discrete planar symmetric shape from a single noisy view

In this paper, we propose a method for identifying a discrete planar symmetric shape from an arbitrary viewpoint. Our algorithm is based on a newly proposed notion of a view's skeleton. We show that this concept yields projective invariants which facilitate the identification procedure. It is, furthermore, shown that the proposed method may be extended to the case of noisy data to yield an optimal estimate of a shape in question. Substantiating examples are provided.
Combining classifiers for bone fracture detection in X-ray images

In medical applications, sensitivity in detecting medical problems and accuracy of detection are often in conflict. A single classifier usually cannot achieve both high sensitivity and accuracy at the same time. Methods of combining classifiers have been proposed in the literature. This paper presents a study of probabilistic combination methods applied to the detection of bone fractures in X-ray images. Test results show that the effectiveness of a method in improving both accuracy and sensitivity depends on the nature of the method as well as the proportion of positive samples.
Elliptic arc vectorization for 3D pie chart recognition

In this paper, we present a novel approach to vectorize elliptic arcs for 3D pie chart recognition and data extraction. As a preprocessing step, a set of straight line segments are obtained first. By estimating the parameters based on the line segments, we are able to generate elliptic arc candidates. Curve fitting is then performed to reject false positives and further refine the parameters of each elliptic arc. The vectorized elliptic arcs are the key components in the 3D pie charts that allow us to determine the chart type and to extract data values embedded. Experiments using a group of testing images show that the proposed method works well and is computationally efficient.
Hide and seek: Uncovering facial occlusion with variable-threshold robust PCA

Face images are very important in human social activities, which can be severely hampered when they are corrupted by occluders such as eyeglasses, face marks, and scarfs. Existing methods for removing occlusions in face images can be grouped into three broad categories, namely PCA, robust PCA (RPCA), and sparse coding. The major weaknesses of these methods are inconsistent performance across test conditions and possible corruption of unoccluded part of the recovered target image. This paper presents variable-threshold RPCA (VRPCA) method based on RPCA with variable thresholding. Comprehensive tests show that VRPCA is able to preserve the unoccluded parts of the target image with practically zero error. Compared to existing methods, it is more accurate, reliable, and consistent across various test conditions.
Representing visual schemas in neural networks for scene analysis

Using object recognition in simple scenes as the task, two fundamental problems in neural network systems are addressed: (1) processing large amounts of input with limited resources, and (2) the representation and use of structured knowledge. The solution to the first problem is to process a small amount of the input in parallel, and successively focus on other parts of the input. This strategy requires that the system maintains structured knowledge for describing and interpreting successively gathered information. The proposed system, VISOR (Visual Schemas for Object Representation), consists of two main modules. The low-level visual module extracts featural and positional information from the visual input. The schema module encodes structured knowledge about possible objects, and provides top-down information for the low-level visual module to focus attention at different parts of the scene. Working cooperatively with the low-level visual module, it builds a globally consistent interpretation of successively gathered visual information.<<ETX>>
Mesh construction from non-uniformly distributed and noisy 3D points recovered from image sequence

This paper describes a novel method for constructing triangle meshes from noisy and non-uniformly distributed 3D points. It consists of two steps: noise point removal uses a clustering algorithm and epipolar constraint method to identify and remove noise points from the 3D points; and a constructive polygonization algorithm interpolates cleaned 3D points to construct a triangle mesh of the object.
Medical volume image summarization

Medical volume images are large in size. They cannot be efficiently transmitted and visualized as candidates for medical image retrieval and relevance feedback. On the other hand, 2D images that are small in size and rich in 3D details can be efficiently transmitted and visualized as candidates. This paper presents an algorithm that summarizes the 3D details in a volume image into a single 2D image. It applies soft segmentation to highlight the anatomy of interest in the volume, and automatically selects a salient view that contains the most amount of semantic information as the summarization of the volume image. Experimental results show that the proposed method can well summarize medical volume images of different anatomical structures. Compared to representation of volume images using 2D slices and conventional volume rendering, our summarized images are rich in 3D details, and they can be transmitted and visualized very efficiently.
Removal of abdominal wall for 3D visualization and segmentation of organs in CT volume

3D visualization and segmentation of organs in abdominal volume images are important in medical image processing for applications such as diagnosis, treatment and surgical planning. However, the abdominal wall leads to difficulties in both visualization and segmentation. These difficulties can be eliminated by removing the abdominal wall. This paper presents an algorithm that removes abdominal wall by registering a 3D flipping-free deformable model to the inner boundary of the wall. To our best knowledge, it is the first work in removing the abdominal wall for the purpose of 3D visualization and segmentation of the organs.
Detecting femur fractures by texture analysis of trabeculae

30% of women and 13% of men worldwide suffer from osteoporotic bone fractures worldwide. In large hospitals, doctors need to visually inspect a large number of X-ray images to identify the fracture cases, which typically constitute about 12% of all the X-ray images examined. Automated fracture detection can help to screen for obvious cases and flag suspicious cases for closer examinations. This paper describes a method of detecting femur fractures by analyzing trabecular texture patterns. Test results show that it is more accurate than an existing method based on neck-shaft angle. Moreover, combining the methods further improve the overall performance of fracture detection.
Think big, solve small: Scaling up robust PCA with coupled dictionaries

Recent advances in robust principle component analysis offers a powerful method for solving a wide variety of low-level vision problems. However, if the input data is very large, especially when high-resolution images are involved, it makes RPCA computationally prohibitive for many real applications. To tackle this problem, we propose a fixed-rank RPCA method that uses coupled dictionaries (FRPCA-CD) to handle high-resolution images. FRPCA-CD downsamples high-resolution images into low-resolution images, performs FRPCA on the low-level images to obtain the low-rank matrix, which is reconstructed at original resolution by coupled dictionaries. Comprehensive tests performed on video background recovery, noise reduction in photometric stereo, and image reflection removal problems show that FRPCA-CD can reduce computation time and memory space drastically without sacrificing accuracy.
3D segmentation of soft organs by flipping-free mesh deformation

Segmentation of 3Dsoft organs from complex volume images is a very important and challenging task. The objects of interest may have inhomogeneous voxel intensities and some object boundaries may be indistinct. Existing algorithms are either sensitive to noise or computationally expensive. This paper presents a novel algorithm that overcomes these shortcomings. The algorithm adopts a novel flipping-free mesh deformation and registration method that can easily incorporate geometric constraints to reduce sensitivity to noise. It efficiently deforms the 3D model in large displacements reducing total computational costs. These properties are confirmed by comprehensive test results.