nils-herrmann/clean_scirepeval huggingface dataset:
Intrinsic dense 3D surface tracking

This paper presents a novel intrinsic 3D surface distance and its use in a complete probabilistic tracking framework for dynamic 3D data. Registering two frames of a deforming 3D shape relies on accurate correspondences between all points across the two frames. In the general case such correspondence search is computationally intractable. Common prior assumptions on the nature of the deformation such as near-rigidity, isometry or learning from a training set, reduce the search space but often at the price of loss of accuracy when it comes to deformations not in the prior assumptions. If we consider the set of all possible 3D surface matchings defined by specifying triplets of correspondences in the uniformization domain, then we introduce a new matching cost between two 3D surfaces. The lowest feature differences across this set of matchings that cause two points to correspond, become the matching cost of that particular correspondence. We show that for surface tracking applications, the matching cost can be efficiently computed in the uniformization domain. This matching cost is then combined with regularization terms that enforce spatial and temporal motion consistencies, into a maximum a posteriori (MAP) problem which we approximate using a Markov Random Field (MRF). Compared to previous 3D surface tracking approaches that either assume isometric deformations or consistent features, our method achieves dense, accurate tracking results, which we demonstrate through a series of dense, anisometric 3D surface tracking experiments.
A Framework for Efficient Structured Max-Margin Learning of High-Order MRF Models

We present a very general algorithm for structured prediction learning that is able to efficiently handle discrete MRFs/CRFs (including both pairwise and higher-order models) so long as they can admit a decomposition into tractable subproblems. At its core, it relies on a dual decomposition principle that has been recently employed in the task of MRF optimization. By properly combining such an approach with a max-margin learning method, the proposed framework manages to reduce the training of a complex high-order MRF to the parallel training of a series of simple slave MRFs that are much easier to handle. This leads to a very efficient and general learning scheme that relies on solid mathematical principles. We thoroughly analyze its theoretical properties, and also show that it can yield learning algorithms of increasing accuracy since it naturally allows a hierarchy of convex relaxations to be used for loss-augmented MAP-MRF inference within a max-margin learning approach. Furthermore, it can be easily adapted to take advantage of the special structure that may be present in a given class of MRFs. We demonstrate the generality and flexibility of our approach by testing it on a variety of scenarios, including training of pairwise and higher-order MRFs, training by using different types of regularizers and/or different types of dissimilarity loss functions, as well as by learning of appropriate models for a variety of vision tasks (including high-order models for compact pose-invariant shape priors, knowledge-based segmentation, image denoising, stereo matching as well as high-order Potts MRFs).
Shape registration in implicit spaces using information theory and free form deformations

We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher-dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the mutual information criterion supports various transformation models and is optimized to perform global registration; then, a B-spline-based incremental free form deformations (IFFD) model is used to minimize a sum-of-squared-differences (SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: 1) it naturally deals with shapes of arbitrary dimension (2D, 3D, or higher) and arbitrary topology (multiple parts, closed/open) and 2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one-to-one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well-known shape registration algorithms
3D cardiac segmentation with pose-invariant higher-order MRFs

This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations. The ventricle surface is determined through triangulation where image discontinuities can be easily evaluated and the Divergence theorem provides an exact calculation of regional statistics acting on the image or a derived feature space. Promising results using boosting along with the learned prior demonstrate the potential of our method.
Beyond pairwise energies: Efficient optimization for higher-order MRFs

In this paper, we introduce a higher-order MRF optimization framework. On the one hand, it is very general; we thus use it to derive a generic optimizer that can be applied to almost any higher-order MRF and that provably optimizes a dual relaxation related to the input MRF problem. On the other hand, it is also extremely flexible and thus can be easily adapted to yield far more powerful algorithms when dealing with subclasses of high-order MRFs. We thus introduce a new powerful class of high-order potentials, which are shown to offer enough expressive power and to be useful for many vision tasks. To address them, we derive, based on the same framework, a novel and extremely efficient message-passing algorithm, which goes beyond the aforementioned generic optimizer and is able to deliver almost optimal solutions of very high quality. Experimental results on vision problems demonstrate the extreme effectiveness of our approach. For instance, we show that in some cases we are even able to compute the global optimum for NP-hard higher-order MRFs in a very efficient manner.
Tagged cardiac MR image segmentation using boundary & regional-support and graph-based deformable priors

Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics is achieved through a global formulation. Prior is enforced through a point-distribution model, where distances between landmark positions are learned and enforced during the segmentation process. The use of divergence theorem leads to an elegant pairwise formulation where image support and prior knowledge are jointly encoded within a pairwise MRF and the segmentation is achieved efficiently by employing MRF inference algorithms. Promising results on numerous examples demonstrate the potentials of our method.
Gradient vector flow fast geometric active contours

In this paper, we propose an edge-driven bidirectional geometric flow for boundary extraction. To this end, we combine the geodesic active contour flow and the gradient vector flow external force for snakes. The resulting motion equation is considered within a level set formulation, can deal with topological changes and important shape deformations. An efficient numerical schema is used for the flow implementation that exhibits robust behavior and has fast convergence rate. Promising results on real and synthetic images demonstrate the potentials of the flow.
Joint Object Segmentation and Behavior Classification in Image Sequences

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent both with prior knowledge and with new image information. We demonstrate the effectiveness of our framework via a particular implementation for a hand gesture recognition application. The prior models are learned from training data using principal components analysis and they adapt dynamically to the content of new images. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior classification in challenging conditions involving occlusions of the target object before a complex background.
Discrete symmetric image registration

Image registration is in principle a symmetric problem. Nonetheless, most intensity-based non-rigid algorithms are asymmetric. In this paper, we propose a novel symmetric deformable registration algorithm formulated in a Markov Random Fields framework where both images are let to deform towards a common domain that lies halfway between two image domains. A grid-based deformation model is employed and the latent variables correspond to the displacements of the grid-nodes towards both image domains. First-order interactions between the unknown variables model standard smoothness priors. Efficient linear programming is consider to recover the optimal solution. The discrete nature of our algorithm allows the handling of both mono- and multi-modal registration problems. Promising experimental results demonstrate the potentials of our approach.
Graph-based knowledge-driven discrete segmentation of the left ventricle

In this paper, we propose a novel similarity-invariant approach to model-based segmentation of the left ventricle. The method assumes a control point representation of the model and an arbitrary interpolation strategy. First, we construct the prior manifold using the distributions of the relative normalized distances between pairs of control points within the training set. Then, we introduce a geometric partition of the space using a Voronoi decomposition that aims to determine relationships between the control points and the image domain. Knowledge-based segmentation can then be expressed using a Markov Random Field, where the pairwise potentials encode the variation of the shape, while the singleton potentials refer to the data term through the Voronoi decomposition of the space. State-of-the art techniques from linear programming are considered to optimize the designed function.
Compressed sensing subtracted rotational angiography with multiple sparse penalty

Digital Subtraction Rotational Angiography (DSRA) is a clinical protocol that allows three-dimensional (3D) visualization of vasculature during minimally invasive procedures. C-arm systems that are used to generate 3D reconstructions in interventional radiology have limited sampling rate and thus, contrast resolution. To address this particular subsampling problem, we propose a novel iterative reconstruction algorithm based on compressed sensing. To this purpose, we exploit both spatial and temporal sparsity of DSRA. For computational efficiency, we use a proximal implementation that accommodates multiple ℓ1-penalties. Experiments on both simulated and clinical data confirm the relevance of our strategy for reducing subsampling streak artifacts.
High-Level Bottom-Up Cues for Top-Down Parsing of Facade Images

We address the problem of parsing images of building facades. The goal is to segment images, assigning to the resulting regions semantic labels that correspond to the basic architectural elements. We assume a top-down parsing framework based on a 2D shape grammar that encodes a prior knowledge on the possible composition of facades. The algorithm explores the space of feasible solutions by generating the possible configurations of the facade and comparing it to the input data by means of a local, pixel- or patch-based classifier. We propose new bottom-up cues for the algorithm, both for evaluation of a candidate parse and for guiding the exploration of the space of feasible solutions. The method that we propose benefits from detection-based information and leverages on the similar appearance of elements that repeat in a given facade. Experiments performed on standard datasets show that this use of more discriminative bottom-up cues improves the convergence in comparison to state-of-the-art algorithms, and gives better results in terms of precision and recall, as well as computation time and performance deviation.
Automatic skeletal muscle segmentation through random walks and graph-based seed placement

In this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome of this optimization process is fed to a powerful graph-based diffusion segmentation method (random walker) that is able to produce very promising results through a fully automated approach. Validation on challenging data sets demonstrates the potentials of our method.
Detecting mutually-salient landmark pairs with MRF regularization

In this paper, we present a framework for extracting mutually-salient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low dis-criminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist's evaluation of the established correspondences is presented.
Geodesic active regions for supervised texture segmentation

The paper presents a novel variational method for supervised texture segmentation. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method. A new approach named Hermes is proposed, which exploits aspects from the well-known front propagation algorithms and compares favorably to them. Very promising experimental results are provided using real video sequences.
A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach

We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isometry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.
Curvilinear structures extraction in cluttered bioimaging data with discrete optimization methods

Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios. In this article, we propose a modular approach to curvilinear structures extraction based on recent advances in discrete optimization on the basis of aggregate clustering. Given an initial clustering of the detected points, first a pair-wise Markov Random Field (MRF) is considered to determine consistent elongated structures while penalizing their number and rejecting outliers. The outcome of this process is then locally refined through a non-submodular MRF aiming at center-line extraction process and guided by local geometric consistency between segments expressing the intra-cluster variability. Promising results for microtubules delineation in TIRFM images as well as for guidewires segmentation in fluoroscopic images demonstrate the potentials of the method.
Model-based hand tracking with texture, shading and self-occlusions

A novel model-based approach to 3D hand tracking from monocular video is presented. The 3D hand pose, the hand texture and the illuminant are dynamically estimated through minimization of an objective function. Derived from an inverse problem formulation, the objective function enables explicit use of texture temporal continuity and shading information, while handling important self-occlusions and time-varying illumination. The minimization is done efficiently using a quasi-Newton method, for which we propose a rigorous derivation of the objective function gradient. Particular attention is given to terms related to the change of visibility near self-occlusion boundaries that are neglected in existing formulations. In doing so we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Experimental results demonstrate the potential of the formulation.
Knowledge-based registration & segmentation of the left ventricle: a level set approach

In this paper, we propose a level set formulation to deal with the segmentation and registration of the left ventricle in Magnetic Resonance (MR) images. Our approach is based on the integration of visual information, anatomical constraints and a flexible shape-driven cardiac model. The visual information is expressed through an intensity-based grouping module. The anatomical constraint accounts for the relative positions of the structures of interest. Global shape consistency is introduced by seeking for the lowest potential of the distance between the solution and the prior model. Registration is obtained using the same criterion where the transformation that aligns the latest segmentation map to either the shape model or to the previous segmentation result (temporal domain) is to be recovered.
Conjoint use of coded-aperture collimators and MLEM algorithm: Towards large blood vessels reconstruction at 511 KEV

In this paper, we propose a novel approach to improve the trade-off between the spatial resolution and the sensitivity of existing nuclear imaging systems in emission tomography for 511 keV-photons. Although emission tomography suffers from the low amount of emitted photons in the object, partial transparency of collimators and low sensitivity of detectors, we are able to reconstruct radioactive spatial distribution from projections obtained through a specifically designed coded-aperture collimator, with a MLEM-algorithm. We address both the selection of the collimator and the reconstruction approach. Very promising experiments towards 3D-vessels reconstruction demonstrate the potentials of our approach.
Quasi real-time sub-space 3D deformable fusion

In this paper we propose a novel and robust approach for de-formable fusion using a metric defined in an appropriate subspace which is adaptive to the image-content/image-modality. We adopt a graph-based formulation that assumes that intensities of corresponding pixels in the two image domains are related through an unknown piece-wise constant linear function. This relation is propagated to an appropriate sub-space (wavelets coefficients) where a criterion that couples the estimation of the deformation field with optimal transport function on the subspace and the smoothness of the deformation is considered. Message passing methods efficiently implemented using jump flooding are considered to get the optimal parameters of the deformation field and the transport function. Promising results are obtained in near real-time with a unique parameter setting.