nils-herrmann/clean_scirepeval huggingface dataset:
Classification and clustering via dictionary learning with structured incoherence and shared features

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as a union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. An incoherence promoting term encourages dictionaries associated to different classes to be as independent as possible, while still allowing for different classes to share features. This term directly acts on the dictionaries, thereby being applicable both in the supervised and unsupervised settings. Using learned dictionaries for classification and clustering makes this method robust and well suited to handle large datasets. The proposed framework uses a novel measurement for the quality of the sparse representation, inspired by the robustness of the ℓ1 regularization term in sparse coding. In the case of unsupervised classification and/or clustering, a new initialization based on combining sparse coding with spectral clustering is proposed. This initialization clusters the dictionary atoms, and therefore is based on solving a low dimensional eigen-decomposition problem, being applicable to large datasets. We first illustrate the proposed framework with examples on standard image and speech datasets in the supervised classification setting, obtaining results comparable to the state-of-the-art with this simple approach. We then present experiments for fully unsupervised clustering on extended standard datasets and texture images, obtaining excellent performance.
Graph Matching: Relax at Your Own Risk

Graph matching-aligning a pair of graphs to minimize their edge disagreements-has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational difficulty. Although many heuristics have previously been proposed in the literature to approximately solve graph matching, very few have any theoretical support for their performance. A common technique is to relax the discrete problem to a continuous problem, therefore enabling practitioners to bring gradient-descent-type algorithms to bear. We prove that an indefinite relaxation (when solved exactly) almost always discovers the optimal permutation, while a common convex relaxation almost always fails to discover the optimal permutation. These theoretical results suggest that initializing the indefinite algorithm with the convex optimum might yield improved practical performance. Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.
ODF reconstruction in q-ball imaging with solid angle consideration

Q-ball imaging (QBI) is a high angular resolution diffusion imaging (HARDI) technique which has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (ODF, the probability of diffusion in a given direction) from q-ball uses linear radial projection, neglecting the change in the volume element along the ray, thereby resulting in distributions different from the true ODFs. For instance, they are not normalized or as sharp as expected, and generally require post-processing, such as sharpening or spherical deconvolution. In this paper, we consider the mathematically correct definition of the ODF and derive a closed-form expression for it in QBI. The derived ODF is dimensionless and normalized, and can be efficiently computed from q-ball acquisition protocols. We describe our proposed method and demonstrate its significantly improved performance on artificial data and real HARDI volumes.
Affine invariant detection: edges, active contours, and segments

In this paper we undertake a systematic investigation of affine invariant object detection. Edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. In this case, affine invariant edges are obtained as a weighted difference of images at different scales. We then introduce the affine gradient as the simplest possible affine invariant differential function which has the same qualitative behavior as the Euclidean gradient magnitude. These edge detectors are the basis both to extend the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. The active contours are obtained as a gradient flow in a conformally Euclidean space defined by the image on which the object is to be detected. That is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance, the weight being given by functions of the affine edge detectors. The geodesic path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology.
Online motion detection in high angular resolution diffusion imaging

The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction can provide real-time feedback to the practitioner, especially appreciated for long acquisition protocols typical in Q-ball imaging. On top of the Kalman filter, we propose a method to evaluate online the reconstruction accuracy of the estimated ODF in constant solid angle. In addition, monitoring the residuals of the Kalman filter, we design, based on statistical tests, two algorithms for online detection of subject motion. The proposed techniques, tested on real and synthetic data under various experimental conditions, can detect rotation by angle less than 3°.
From active contours to anisotropic diffusion: connections between basic PDE's in image processing

We present mathematical and qualitative relations between a number of partial differential equations frequently used in image processing and computer vision. We show for example that classical active contours introduced for object detection by Terzopoulos (1988) and colleagues are connected to anisotropic diffusion flows as those defined by Perona and Malik (1990). We also deal with the relation of these flows with shock filters and variational approaches for image restoration.
Esophagus Silhouette Extraction and Reconstruction From Fluoroscopic Views for Cardiac Ablation Procedure Guidance

Cardiac ablation involves the risk of serious complications when thermal injury to the esophagus occurs. This paper proposes to reduce the risk of such injuries by a proactive visualization technique, improving physician awareness of the esophagus location in the absence of or in addition to a reactive monitoring device such as a thermal probe. This is achieved by combining a graphical representation of the esophagus with live fluoroscopy. Toward this goal, we present an automated method to reconstruct and visualize a 3-D esophagus model from fluoroscopy image sequences acquired using different C-arm viewing directions. In order to visualize the esophagus under fluoroscopy, it is first biomarked by swallowing a contrast agent such as barium. Images obtained in this procedure are then used to automatically extract the 2-D esophagus silhouette and reconstruct a 3-D surface of the esophagus internal wall. Once the 3-D representation has been computed, it can be visualized using fluoroscopy overlay techniques. Compared to 3-D esophagus imaging using CT or C-arm CT, our proposed fluoroscopy method requires low radiation dose and enables a simpler workflow on geometry-calibrated standard C-arm systems.
Clinical deep brain stimulation region prediction using regression forests from high-field MRI

This paper presents a prediction framework of brain subcortical structures which are invisible on clinical low-field MRI, learning detailed information from ultrahigh-field MR training data. Volumetric segmentation of Deep Brain Stimulation (DBS) structures within the Basal ganglia is a prerequisite process for reliable DBS surgery. While ultrahigh-field MR imaging (7 Tesla) allows direct visualization of DBS targeting structures, such ultrahigh-fields are not always clinically available, and therefore the relevant structures need to be predicted from the clinical data. We address the shape prediction problem with a regression forest, non-linearly mapping predictors to target structures with high confidence, exploiting ultrahigh-field MR training data. We consider an application for the subthalamic nucleus (STN) prediction as a crucial DBS target. Experimental results on Parkinson's patients validate that the proposed approach enables reliable estimation of the STN from clinical 1.5T MRI.
Geodesic active contours

A novel scheme for the detection of object boundaries is presented. The technique is based on active contours deforming according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric as defined by the image content. This geodesic approach for object segmentation allows to connect classical "snakes" based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved as showed by a number of examples. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well.<<ETX>>
Segmenting neurons in electronic microscopy via geometric tracing

Describes a system that is being used for the segmentation of neurons in images obtained from electronic microscopy. These images are extremely noisy, and ordinary active contours techniques detect spurious objects and fail to detect the neuron boundaries. The algorithm here described is based on combining robust anisotropic diffusion with minimal weighted-path computations. After the image is regularized via anisotropic diffusion, the user clicks points on the boundary of the desired object, and the algorithm completes the boundary between those points. This tracing is based on computing paths of minimal weighted distance, where the weight is given by the image edge content. Thanks to advanced numerical algorithms, the algorithm is very fast and accurate. The authors compare their results with those obtained with PictureIt, a commercially available general purpose image processing package developed by Microsoft.
Robust Large Margin Deep Neural Networks

The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a bounded spectral norm of the network's Jacobian matrix in the neighbourhood of the training samples is crucial for a deep neural network of arbitrary depth and width to generalize well. This is a significant improvement over the current bounds in the literature, which imply that the generalization error grows with either the width or the depth of the network. Moreover, it shows that the recently proposed batch normalization and weight normalization reparametrizations enjoy good generalization properties, and leads to a novel network regularizer based on the network's Jacobian matrix. The analysis is supported with experimental results on the MNIST, CIFAR-10, LaRED, and ImageNet datasets.
A geometric approach to segmentation and analysis of 3D medical images

A geometric scheme for detecting, representing, and measuring 3D medical data is presented. The technique based on deforming 3D surfaces, represented via level-sets, towards the medical objects, according to intrinsic geometric measures of the data. The 3D medical object is represented as a (weighted) minimal surface in a Riemannian space whose metric is induced from the image. This minimal surface is computed using the level-set methodology for propagating interfaces, combined with a narrow band technique which allows fast implementation. This computation technique automatically handles topological changes. Measurements like volume and area are performed on the surface, exploiting the representation and the high accuracy intrinsic to the algorithm.
A Convex Model for Nonnegative Matrix Factorization and Dimensionality Reduction on Physical Space

A collaborative convex framework for factoring a data matrix X into a nonnegative product AS , with a sparse coefficient matrix S, is proposed. We restrict the columns of the dictionary matrix A to coincide with certain columns of the data matrix X, thereby guaranteeing a physically meaningful dictionary and dimensionality reduction. We use l1, ∞ regularization to select the dictionary from the data and show that this leads to an exact convex relaxation of l0 in the case of distinct noise-free data. We also show how to relax the restriction-to-X constraint by initializing an alternating minimization approach with the solution of the convex model, obtaining a dictionary close to but not necessarily in X. We focus on applications of the proposed framework to hyperspectral endmember and abundance identification and also show an application to blind source separation of nuclear magnetic resonance data.
Distancecut: Interactive Segmentation and Matting of Images and Videos

An interactive algorithm for soft segmentation and matting of natural images and videos is presented in this paper. The technique follows and extends Protiere et al. (2007), where the user first roughly scribbles/labels different regions of interest, and from them the whole data is automatically segmented. The segmentation and alpha matte are obtained from the fast, linear complexity, computation of weighted distances to the user-provided scribbles. These weighted distances assign probabilities to each labeled class for every pixel. The weights are derived from models of the image regions obtained from the user provided scribbles via kernel density estimation. The matting results follow from combining this density and the computed weighted distances. We present the underlying framework and examples showing the capability of the algorithm to segment and compute alpha mattes, in interactive real time, for difficult natural data.
Audio restoration from multiple copies

A method for removing impulse noise from audio signals by fusing multiple copies of the same recording is introduced in this paper. The proposed algorithm exploits the fact that while in general multiple copies of a given recording are available, all sharing the same master, most degradations in audio signals are record-dependent. Our method first seeks for the optimal non-rigid alignment of the signals that is robust to the presence of sparse outliers with arbitrary magnitude. Unlike previous approaches, we simultaneously find the optimal alignment of the signals and impulsive degradation. This is obtained via continuous dynamic time warping computed solving an Eikonal equation. We propose to use our approach in the derivative domain, reconstructing the signal by solving an inverse problem that resembles the Poisson image editing technique. The proposed framework is here illustrated and tested in the restoration of old gramophone recordings showing promising results; however, it can be used in other applications where different copies of the signal of interest are available and the degradations are copy-dependent.
An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms

Electron tomography allows for the determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for the analysis of features in tomograms is an important problem, and a challenging prospect due to the low signal-to-noise ratios that are inherent to biological electron microscopic images. This is, in part, a consequence of the tremendous complexity of biological specimens. We report on a new method for the automated segmentation of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. The optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest, providing a proper setting for the fast and accurate minimization of the segmentation energy. This method provides tools for the semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of HIV infection. The segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition. It could also enable studies of much larger data sets, such as those which might be obtained from the tomographic analysis of HIV-infected cells from studies of large populations.
Simultaneous ODF estimation and tractography in HARDI

We consider the problem of tracking white matter fibers in high angular resolution diffusion imaging (HARDI) data while simultaneously estimating the local fiber orientation profile. Prior work showed that an unscented Kalman filter (UKF) can be used for this problem, yet existing algorithms employ parametric mixture models to represent water diffusion and to define the state space. To address this restrictive model dependency, we propose to extend the UKF to HARDI data modeled by orientation distribution functions (ODFs), a more generic diffusion model. We consider the spherical harmonic representation of the HARDI signal as the state, enforce nonnegativity of the ODFs, and perform tractography using the directions at which the ODFs attain their peaks. In simulations, our method outperforms filtered two-tensor tractography at different levels of noise by achieving a reduction in mean Chamfer error of 0.05 to 0.27 voxels; it also produced in vivo fiber tracking that is consistent with the neuroanatomy.
Segmentation-free skeletonization of gray-scale images via PDEs

A simple approach to compute the skeletons of gray-scale images using partial differential equations is presented. The proposed scheme works directly on the gray-scale images, without the necessity of pre-segmentation (binarization), or the addition of shock capturing schemes. This is accomplished by deforming the given image according to a family of modified continuous-scale erosion/dilation equations. With the scheme here proposed, the skeleton of multiple objects can be simultaneously computed. Examples on synthetic and real images are provided.
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding

Surveillance cameras today often capture NIR (near infrared) images in low-light environments. However, most face datasets accessible for training and verification are only collected in the VIS (visible light) spectrum. It remains a challenging problem to match NIR to VIS face images due to the different light spectrum. Recently, breakthroughs have been made for VIS face recognition by applying deep learning on a huge amount of labeled VIS face samples. The same deep learning approach cannot be simply applied to NIR face recognition for two main reasons: First, much limited NIR face images are available for training compared to the VIS spectrum. Second, face galleries to be matched are mostly available only in the VIS spectrum. In this paper, we propose an approach to extend the deep learning breakthrough for VIS face recognition to the NIR spectrum, without retraining the underlying deep models that see only VIS faces. Our approach consists of two core components, cross-spectral hallucination and low-rank embedding, to optimize respectively input and output of a VIS deep model for cross-spectral face recognition. Cross-spectral hallucination produces VIS faces from NIR images through a deep learning approach. Low-rank embedding restores a low-rank structure for faces deep features across both NIR and VIS spectrum. We observe that it is often equally effective to perform hallucination to input NIR images or low-rank embedding to output deep features for a VIS deep model for cross-spectral recognition. When hallucination and low-rank embedding are deployed together, we observe significant further improvement, we obtain state-of-the-art accuracy on the CASIA NIR-VIS v2.0 benchmark, without the need at all to re-train the recognition system.
Segmenting cortical gray matter for functional MRI visualization

We describe a system that is being used to segment gray matter and create connected cortical representations from MRI. The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. First, the white matter and CSF regions in the MR volume are segmented using some novel techniques of posterior anisotropic diffusion. Then, the user selects the cortical white matter component of interest, and its structure is verified by checking for cavities and handles. After this, a connected representation of the gray matter is created by a constrained growing-out from the white matter boundary. Because the connectivity is computed, the segmentation can be used as input to several methods of visualizing the spatial pattern of cortical activity within gray matter. In our case, the connected representation of gray matter is used to create a representation of the flattened cortex. Then, fMRI measurements are overlaid on the flattened representation, yielding a representation of the volumetric data within a single image.
A short-graph fourier transform via personalized pagerank vectors

The short-time Fourier transform (STFT) is widely used to analyze the spectra of temporal signals that vary through time. Signals defined over graphs, due to their intrinsic complexity, exhibit large variations in their patterns. In this work we propose a new formulation for an STFT for signals defined over graphs. This formulation draws on recent ideas from spectral graph theory, using personalized PageRank vectors as its fundamental building block. Furthermore, this work establishes and explores the connection between local spectral graph theory and localized spectral analysis of graph signals. We accompany the presentation with synthetic and real-world examples, showing the suitability of the proposed approach.