nils-herrmann/clean_scirepeval huggingface dataset:
Nonparametric image interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors

We present a Bayesian model for image interpolation and dictionary learning that uses two nonparametric priors for sparse signal representations: the beta process and the Dirichlet process. Additionally, the model uses spatial information within the image to encourage sharing of information within image subregions. We derive a hybrid MAP/Gibbs sampler, which performs Gibbs sampling for the latent indicator variables and MAP estimation for all other parameters. We present experimental results, where we show an improvement over other state-of-the-art algorithms in the low-measurement regime.
3D priors for scene learning from a single view

A framework for scene learning from a single still video camera is presented in this work. In particular, the camera transformation and the direction of the shadows are learned using information extracted from pedestrians walking in the scene. The proposed approach poses the scene learning estimation as a likelihood maximization problem, efficiently solved via factorization and dynamic programming, and amenable to an online implementation. We introduce a 3D prior to model the pedestrianpsilas appearance from any viewpoint, and learn it using a standard off-the-shelf consumer video camera and the Radon transform. This 3D prior or ldquoappearance modelrdquo is used to quantify the agreement between the tentative parameters and the actual video observations, taking into account not only the pixels occupied by the pedestrian, but also those occupied by the his shadows and/or reflections. The presentation of the framework is complemented with an example of a casual video scene showing the importance of the learned 3D pedestrian prior and the accuracy of the proposed approach.
Creating connected representations of cortical gray matter for functional MRI visualization

Describes a system that is being used to segment gray matter from magnetic resonance imaging (MRI) and to create connected cortical representations for functional MRI visualization (fMRI). The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. First, the white matter and cerebral spinal fluid (CSF) regions in the MR volume are segmented using a 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 the authors' case, the connected representation of gray matter is used to create a flattened representation of the cortex. Then, fMRI measurements are overlaid on the flattened representation, yielding a representation of the volumetric data within a single image. The software is freely available to the research community.
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb-type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.
Morphological image coding via bit-plane decomposition and a new skeleton representation

The image is processed by an error-diffusion technique to reduce the number of bit-planes without significant quality degradation. The bit-planes of the resulting image are converted to Gray code and represented by a modified morphological skeleton which uses an increasing size structuring element. Redundancy in this representation is reduced with an algorithm motivated by a geometric sampling theorem. These reduced modified morphological skeletons are coded with an entropy coding scheme devised particularly for efficient skeleton coding. A post-processing operation. as well as the possibility of geometric progressive transmission, are also discussed.<<ETX>>
Generalized Newton methods for energy formulations in image procesing

Many problems in image processing are solved via the minimization of a cost functional. The most widely used optimization technique is the gradient descent, often used due to its simplicity and applicability where other optimization techniques, e.g., those coming from discrete optimization, can not be used. Yet, gradient descent suffers from a slow convergence, and often to just local minima which highly depends on the condition number of the functional Hessian. Newton- type methods, on the other hand, are known to have a rapid (quadratic) convergence. In its classical form, the Newton method relies on the L2-type norm to define the descent direction. In this paper, we generalize and reformulate this very important optimization method by introducing a novel Newton method based on general norms. This generalization opens up new possibilities in the extraction of the Newton step, including benefits such as mathematical stability and smoothness constraints. We first present the derivation of the modified Newton step in the calculus of variation framework. Then we demonstrate the method with two common objective functionals: variational image deblurring and geodesic active contours. We show that in addition to the fast convergence, different selections norm yield different and superior results.
Segmentation-free measurement of cortical thickness from MRI

Estimating the thickness of cerebral cortex is a key step in many MR brain imaging studies, revealing valuable information on development or disease progression. In this work we present a new approach to measure the cortical thickness, based on minimizing line integrals over the probability map of the gray matter in the MRI volume. Previous methods often perform a binary-valued segmentation of the gray matter before measuring the thickness. Because of image noise and partial voluming, such a hard classification ignores the underlying tissue class probabilities assigned to each voxel, discarding potentially useful information. We describe our proposed method and demonstrate its performance on both artificial volumes and real 3D brain MRI data from subjects with Alzheimer's disease and healthy individuals.
On the non-uniform complexity of brain connectivity

A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides high- dimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensions/complexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.
Intelligent Synthesis Driven Model Calibration: Framework and Face Recognition Application

Deep Neural Networks (DNNs) that achieve state-of-the-art results are still prone to suffer performance degradation when deployed in many real-world scenarios due to shifts between the training and deployment domains. Limited data from a given setting can be enriched through synthesis, then used to calibrate a pre-trained DNN to improve the performance in the setting. Most enrichment approaches try to generate as much data as possible; however, this blind approach is computationally expensive and can lead to generating redundant data. Contrary to this, we develop synthesis, here exemplified for faces, methods and propose information-driven approaches to exploit and optimally select face synthesis types both at training and testing. We show that our approaches, without re-designing a new DNN, lead to more efficient training and improved performance. We demonstrate the effectiveness of our approaches by calibrating a state-of-the-art DNN to two challenging face recognition datasets.
3D elastic registration improves HARDI-derived fiber alignment and automated tract clustering

High angular resolution diffusion imaging (HARDI) allows population studies of fiber integrity and connectivity. Tractography can extract individual fibers. For group studies, fibers must be clustered into recognizable bundles found consistently across subjects. Nonlinear image registration may improve population clustering. To test this, we performed whole-brain tractography with an orientation distribution function based Hough transform method in 20 young adults scanned with 4 Tesla, 105-gradient HARDI. We warped all extracted fibers to a geometrically-centered template using a 3D elastic registration driven by fractional anisotropy maps, to align embedded tracts. Fiber alignment was evaluated by calculating distances among corresponding fibers across subjects. Before and after warping, we performed spectral clustering of the fibers using a k-means method, based on eigenvectors of a fiber similarity matrix. In tests with an overlap metric, non-rigid fiber warping yielded more robust clustering results. Non-rigid warping is therefore advantageous for population studies using multi-subject tract clustering.
What Can Casual Walkers Tell Us About A 3D Scene?

An approach for incremental learning of a 3D scene from a single static video camera is presented in this paper. In particular, we exploit the presence of casual people walking in the scene to infer relative depth, learn shadows, and segment the critical ground structure. Considering that this type of video data is so ubiquitous, this work provides an important step towards 3D scene analysis from single cameras in readily available ordinary videos and movies. On-line 3D scene learning, as presented here, is very important for applications such as scene analysis, foreground refinement, tracking, biometrics, automated camera collaboration, activity analysis, identification, and real-time computer-graphics applications. The main contributions of this work are then two-fold. First, we use the people in the scene to continuously learn and update the 3D scene parameters using an incremental robust (L1) error minimization. Secondly, models of shadows in the scene are learned using a statistical framework. A symbiotic relationship between the shadow model and the estimated scene geometry is exploited towards incremental mutual improvement. We illustrate the effectiveness of the proposed framework with applications in foreground refinement, automatic segmentation as well as relative depth mapping of the floor/ground, and estimation of 3D trajectories of people in the scene.
A Convex Optimization Framework for Active Learning

In many image/video/web classification problems, we have access to a large number of unlabeled samples. However, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunction with any type of classifiers, including those of the family of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. Our method can incorporate the data distribution in the selection process by using the appropriate dissimilarity between pairs of samples. We show the effectiveness of our framework in person detection, scene categorization and face recognition on real-world datasets.
Sparse Representation for Color Image Restoration

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in . This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.
Universal priors for sparse modeling

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. In this work, we use tools from information theory to propose a sparsity regularization term which has several theoretical and practical advantages over the more standard ¿0 or ¿1 ones, and which leads to improved coding performance and accuracy in reconstruction tasks. We also briefly report on further improvements obtained by imposing low mutual coherence and Gram matrix norm on the learned dictionaries.
Minimal Surfaces Based Object Segmentation

A geometric approach for 3D object segmentation and representation is presented. The segmentation is obtained by deformable surfaces moving towards the objects to be detected in the 3D image. The model is based on curvature motion and the computation of surfaces with minimal areas, better known as minimal surfaces. The space where the surfaces are computed is induced from the 3D image (volumetric data) in which the objects are to be detected. The model links between classical deformable surfaces obtained via energy minimization, and intrinsic ones derived from curvature based flows. The new approach is stable, robust, and automatically handles changes in the surface topology during the deformation.
Learnable low rank sparse models for speech denoising

In this paper we present a framework for real time enhancement of speech signals. Our method leverages a new process-centric approach for sparse and parsimonious models, where the representation pursuit is obtained applying a deterministic function or process rather than solving an optimization problem. We first propose a rank-regularized robust version of non-negative matrix factorization (NMF) for modeling time-frequency representations of speech signals in which the spectral frames are decomposed as sparse linear combinations of atoms of a low-rank dictionary. Then, a parametric family of pursuit processes is derived from the iteration of the proximal descent method for solving this model. We present several experiments showing successful results and the potential of the proposed framework. Incorporating discriminative learning makes the proposed method significantly outperform exact NMF algorithms, with fixed latency and at a fraction of it's computational complexity.
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting

An interactive framework for soft segmentation and matting of natural images and videos is presented in this paper. The proposed technique is based on the optimal, linear time, computation of weighted geodesic distances to the user-provided scribbles, from which the whole data is automatically segmented. The weights are based on spatial and/or temporal gradients, without explicit optical flow or any advanced and often computationally expensive feature detectors. These could be naturally added to the proposed framework as well if desired, in the form of weights in the geodesic distances. A localized refinement step follows this fast segmentation in order to accurately compute the corresponding matte function. Additional constraints into the distance definition permit to efficiently handle occlusions such as people or objects crossing each other in a video sequence. The presentation of the framework is complemented with numerous and diverse examples, including extraction of moving foreground from dynamic background, and comparisons with the recent literature.
Robust anisotropic diffusion

Relations between anisotropic diffusion and robust statistics are described in this paper. Specifically, we show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new "edge-stopping" function based on Tukey's biweight robust estimator that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in an image that has been smoothed with anisotropic diffusion. Additionally, we derive a relationship between anisotropic diffusion and regularization with line processes. Adding constraints on the spatial organization of the line processes allows us to develop new anisotropic diffusion equations that result in a qualitative improvement in the continuity of edges.
LOCO-I: a low complexity, context-based, lossless image compression algorithm

LOCO-I (low complexity lossless compression for images) is a novel lossless compression algorithm for continuous-tone images which combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.
Toward Multiple Catheters Detection in Fluoroscopic Image Guided Interventions

Catheters are routinely inserted via vessels to cavities of the heart during fluoroscopic image guided interventions for electrophysiology (EP) procedures such as ablation. During such interventions, the catheter undergoes nonrigid deformation due to physician interaction, patient's breathing, and cardiac motions. EP clinical applications can benefit from fast and accurate automatic catheter tracking in the fluoroscopic images. The typical low quality in fluoroscopic images and the presence of other medical instruments in the scene make the automatic detection and tracking of catheters in clinical environments very challenging. Toward the development of such an application, a robust and efficient method for detecting and tracking the catheter sheath is developed. The proposed approach exploits the clinical setup knowledge to constrain the search space while boosting both tracking speed and accuracy, and is based on a computationally efficient framework to trace the sheath and simultaneously detect one or multiple catheter tips. The algorithm is based on a modification of the fast marching weighted distance computation that efficiently calculates, on the fly, important geodesic properties in relevant regions of the image. This is followed by a cascade classifier for detecting the catheter tips. The proposed technique is validated on 1107 fluoroscopic images acquired on multiple patients across four different clinics, achieving multiple catheter tracking at a rate of 10 images/s with a very low false positive rate of 1.06.
Vector probability diffusion

The basic motivation of this work is to introduce contextual information into image segmentation tasks by adding spatial coherence to the posterior probabilities corresponding to the classes present in the scene. A method for isotropic and anisotropic diffusion of vector probabilities in general, and posterior probabilities in particular, is introduced. The technique is based on diffusing via coupled partial differential equations restricted to the semi-hyperplane corresponding to probability functions. Both the partial differential equations and their corresponding numerical implementation guarantee that the vector remains a probability vector, having all its components positive and adding to one. Applying the method to posterior probabilities in classification problems, spatial and contextual coherence is introduced before the maximum a posteriori (MAP) decision, thereby improving the classification results.
A Graph-Based Foreground Representation and its Application in Example Based People Matching in Video

In this work, we propose a framework for foreground representation in video and illustrate it with a multi-camera people matching application. We first decompose the video into foreground and background. A low-level coarse segmentation of the foreground is then used to generate a simple graph representation. A vertex in the graph represents the "appearance" of a corresponding segment in the foreground, while the relationship between two segments is encoded by an edge between the corresponding vertices. This provides a simple yet powerful and general representation of the foreground, which can be very useful in problems such as people detection and tracking. We illustrate the effectiveness of this model using an "example based query" type of application for people matching in videos. Matching results are provided in multiple-camera situations and also under occlusion.