nils-herrmann/clean_scirepeval huggingface dataset:
Modular linear iconic matching using higher order graphs

We introduce a novel approach to tackle iconic linear mapping between two images. We adopt a grid-based parametrization of the deformation field that is encoded by a higher order graphical model. In the proposed formulation, latent variables correspond to local grid displacement vectors and unary potentials locally quantify the level of alignment between the two images. Higher order constraints that involve third and forth order potentials, enforce the linearity of the resulting transformation. The resulting formulation is modular with respect to the image metric used to evaluate the correctness of mapping as well as with respect to the nature of the linear transformation (rigid, similarity, or affine). Inference on this graph is performed through dual decomposition. Comparison with classic algorithms demonstrates the potential of our approach.
A level set approach for shape-driven segmentation and tracking of the left ventricle

Knowledge-based segmentation has been explored significantly in medical imaging. Prior anatomical knowledge can be used to define constraints that can improve performance of segmentation algorithms to physically corrupted and incomplete data. In this paper, the objective is to introduce such knowledge-based constraints while preserving the ability of dealing with local deformations. Toward this end, we propose a variational level set framework that can account for global shape consistency as well as for local deformations. In order to improve performance, the problems of segmentation and tracking of the structure of interest are dealt with simultaneously by introducing the notion of time in the process and looking for a solution that satisfies that prior constraints while being consistent along consecutive frames. Promising experimental results in magnetic resonance and ultrasonic cardiac images demonstrate the potentials of our approach.
Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
Optimal similarity registration of volumetric images

This paper proposes a novel approach to optimally solve volumetric registration problems. The proposed framework exploits parametric dictionaries for sparse volumetric representations, ℓ1 dissimilarities and DC (Difference of Convex functions) decomposition. The SAD (sum of absolute differences) criterion is applied to the sparse representation of the reference volume and a DC decomposition of this criterion with respect to the transformation parameters is derived. This permits to employ a cutting plane algorithm for determining the optimal relative transformation parameters of the query volume. It further provides a guarantee for the global optimality of the obtained solution, which–to the best of our knowledge–is not offered by any other existing approach. A numerical validation demonstrates the effectiveness and the large potential of the proposed method.
Parsing Facades with Shape Grammars and Reinforcement Learning

In this paper, we use shape grammars (SGs) for facade parsing, which amounts to segmenting 2D building facades into balconies, walls, windows, and doors in an architecturally meaningful manner. The main thrust of our work is the introduction of reinforcement learning (RL) techniques to deal with the computational complexity of the problem. RL provides us with techniques such as Q-learning and state aggregation which we exploit to efficiently solve facade parsing. We initially phrase the 1D parsing problem in terms of a Markov Decision Process, paving the way for the application of RL-based tools. We then develop novel techniques for the 2D shape parsing problem that take into account the specificities of the facade parsing problem. Specifically, we use state aggregation to enforce the symmetry of facade floors and demonstrate how to use RL to exploit bottom-up, image-based guidance during optimization. We provide systematic results on the Paris building dataset and obtain state-of-the-art results in a fraction of the time required by previous methods. We validate our method under diverse imaging conditions and make our software and results available online.
Hierarchical 3D diffusion wavelet shape priors

In this paper, we propose a novel representation of prior knowledge for image segmentation, using diffusion wavelets that can reflect arbitrary continuous interdependencies in shape data. The application of diffusion wavelets has, so far, largely been confined to signal processing. In our approach, and in contrast to state-of-the-art methods, we optimize the coefficients, the number and the position of landmarks, and the object topology - the domain on which the wavelets are defined - during the model learning phase, in a coarse-to-fine manner. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. We report results on two challenging medical data sets, that illustrate the impact of the soft parameterization and the potential of the diffusion operator.
Sparsity, redundancy and optimal image support towards knowledge-based segmentation

In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation and image support. In this context we consider a control-point based shape representation. Their sparse distribution is derived based on a shape model metric learned from the training data, and the ambiguity of local appearance with regard to segmentation changes. The resulting sparse model of the object improves reconstruction and search behavior, in particular for data that exhibit a heterogeneous distribution of image information and shape complexity. Furthermore, it goes beyond conventional image-based segmentation approaches since it is able to identify reliable image structures which are then encoded within the model and used to determine the optimal segmentation map. We report promising experimental results comparing our approach with standard models on MRI data of calf muscles - an application where traditional image-based methods fail - and CT data of the left heart ventricle.
Biomedical image analysis using markov random fields & efficient linear programing

Computer-aided diagnosis through biomedical image analysis is increasingly considered in health sciences. This is due to the progress made on the acquisition side, as well as on the processing one. In vivo visualization of human tissues where one can determine both anatomical and functional information is now possible. The use of these images with efficient intelligent mathematical and processing tools allows the interpretation of the tissues state and facilitates the task of the physicians. Segmentation and registration are the two most fundamental tools in bioimaging. The first aims to provide automatic tools for organ delineation from images, while the second focuses on establishing correspondences between observations inter and intra subject and modalities. In this paper, we present some recent results towards a common formulation addressing these problems, called the Markov Random Fields. Such an approach is modular with respect to the application context, can be easily extended to deal with various modalities, provides guarantees on the optimality properties of the obtained solution and is computationally efficient.
Single view reconstruction using shape grammars for urban environments

In this paper we introduce a novel approach to single view reconstruction using shape grammars. Our approach consists in modeling architectural styles using a set of basic shapes and a set of parametric rules, corresponding to increasing levels of detail. This approach is able to model elaborate and varying architectural styles, using a tree representation of variable depth and complexity. Towards reconstruction, the parameters of the rules are optimized using image-based and architectural costs. This is done through an efficient MRF formulation based on the shape grammar itself. The resulting framework can produce precise 3D models from single views, can deal with lack of texture and the presence of occlusions and specular reflections, while maintaining the ability to cope with very complex architectural styles. Promising results demonstrate the potential of our approach.
Uncalibrated stereo rectification for automatic 3D surveillance

We describe a stereo rectification method suitable for automatic 3D surveillance. We take advantage of the fact that in a typical urban scene, there is ordinarily a small number of dominant planes. Given two views of the scene, we align a dominant plane in one view with the other. Conjugate epipolar lines between the reference view and plane-aligned image become geometrically identical and can be added to the rectified image pair line by line. Selecting conjugate epipolar lines to cover the whole image is simplified since they are geometrically identical. In addition, the polarities of conjugate epipolar lines are automatically preserved by plane alignment, which simplifies stereo matching.
Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data

In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection. The deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space. Promising results on large scale experiments demonstrate the extreme potentials of our method.
Multimodal inference of articulated spine models from higher order energy functions of discrete MRFS

In this paper, we introduce a novel approach based on higher order energy functions which have the ability to encode global structural dependencies to infer articulated 3D spine models to CT volume data. A personalized geometrical model is reconstructed from biplanar X-rays before spinal surgery in order to create a spinal column representation which is modeled by a series of intervertebral transformations based on rotation and translation parameters. The shape transformation between the standing and lying poses is then achieved through a Markov Random Field optimization graph, where the unknown variables are the deformations applied to the intervertebral transformations. Singleton and pairwise potentials measure the support from the data and geometrical dependencies between neighboring vertebrae respectively, while higher order cliques are introduced to integrate consistency in regional curves. Optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. A qualitative evaluation of the vertebra model alignment obtained from the proposed method gave promising results while the quantitative comparison to expert identification yields an accuracy of 1.8 ± 0.7 mm based on the localization of surgical landmarks.
Modelling shapes with uncertainties: higher order polynomials, variable bandwidth kernels and non parametric density estimation

In this paper, we introduce a new technique for shape modelling in the space of implicit polynomials. Registration consists of recovering an optimal one-to-one transformation of a higher order polynomial along with uncertainties measures that are determined according to the covariance matrix of the correspondences at the zero isosurface. In the modelling phase, these measures are used to weight the importance of the training samples phase according to a variable bandwidth non-parametric density estimation process. The selection of the most appropriate kernels to represent the training set is done through the maximum likelihood criterion. Excellent results for patterns of digits, related with the registration and the modelling aspects of our approach demonstrate the potentials of our method
MRF Optimization via Dual Decomposition: Message-Passing Revisited

A new message-passing scheme for MRF optimization is proposed in this paper. This scheme inherits better theoretical properties than all other state-of-the-art message passing methods and in practice performs equally well/outperforms them. It is based on the very powerful technique of Dual Decomposition  and leads to an elegant and general framework for understanding/designing message-passing algorithms that can provide new insights into existing techniques. Promising experimental results and comparisons with the state of the art demonstrate the extreme theoretical and practical potentials of our approach.
Detection and location of moving objects using deterministic relaxation algorithms

Two important problems in motion analysis are addressed in this paper: change detection and moving object location. For the first problem, the inter-frame difference is modelized by a mixture of Laplacian distributions, a Gibbs random field is used for describing the label field, and HCF (highest confidence first) algorithm is used for solving the resulting optimization problem. The solution of the second problem is based on the observation of two successive frames alone. Using the results of change detection an adaptive statistical model for the couple of image intensities is identified. Then the labeling problem is solved using HCF algorithm. Results on real image sequences illustrate the efficiency of the proposed method.
Dense non-rigid surface registration using high-order graph matching

In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1) casting 3D surface registration into a graph matching problem that combines both geometric and appearance similarities and intrinsic embedding information, 2) the first implementation of high-order graph matching algorithm that solves a non-convex optimization problem, and 3) an efficient two-stage optimization approach to constrain the search space for dense surface registration. Our method is validated through a series of experiments demonstrating its accuracy and efficiency, notably in challenging cases of large and/or non-isometric deformations, or meshes that are partially occluded.
Optical flow estimation with uncertainties through dynamic MRFs

In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution.
Unsupervised classification of skeletal fibers using diffusion maps

In this paper, we propose an application of diffusion maps to fiber tract clustering in the human skeletal muscle. To this end, we define a metric between fiber tracts that encompasses both diffusion and localization information. This metric is incorporated in the diffusion maps framework and clustering is done in the embedding space using k-means. Experimental validation of the method is performed over a dataset of diffusion tensor images of the calf muscle of thirty subjects and comparison is done with respect to ground-truth segmentation provided by an expert.
Graph-based guide-wire segmentation through fusion of contrast-enhanced and fluoroscopic images

In this paper, we present a novel method that fuses, through a graph matching, segmentation of the blood vessels in contrast-enhanced images with segmentation of the guide-wires in the fluoroscopic images. This is achieved through a bottom up approach that first extracts local geometric primitives of interest in both images. Fusion between two graphs built with these primitives is performed through spectral matching and allows the definition of an improved criterion of ordering of the wire primitives. Given such criterion, local ordering is used towards reconstruction of multiple curvilinear structures that inherit visual support from both images. An evaluation performed on a broad variety of clinical situations validates the effectiveness of our approach.
Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications

and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.
Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle

In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use support vector machines to classify diffusion tensors and we extend linear classification to the non linear case. To this end, we discuss and evaluate three different classes of kernels on the space of symmetric definite positive matrices that are well suited for the classification of tensor data. We impose spatial constraints by means of a Markov random field model that takes into account the result of SVM classification. Experimental results are provided for diffusion tensor images of human skeletal muscles. They demonstrate the potential of our method in discriminating the different muscle groups.
Segmentation, ordering and multi-object tracking using graphical models

In this paper, we propose a unified graphical-model framework to interpret a scene composed of multiple objects in monocular video sequences. Using a single pairwise Markov random field (MRF), all the observed and hidden variables of interest such as image intensities, pixels' states (associated object's index and relative depth), objects' states (model motion parameters and relative depth) are jointly considered. Particular attention is given to occlusion handling by introducing a rigorous visibility modeling within the MRF formulation. Through minimizing the MRF's energy, we simultaneously segment, track and sort by depth the objects. Promising experimental results demonstrate the potential of this framework and its robustness to image noise, cluttered background, moving camera and background, and even complete occlusions.
Group-wise MDL based registration of small animals in video sequences

We propose a method for the non-rigid tracking of small animals in video sequences. Based on an image sequence showing the animal, first a sub-set of images with coherent pose is chosen automatically. Then a robust rigid registration determines the coarse animal body pose in the set of frames and a subsequent non-rigid group-wise registration using the minimum description length principle learns a deformation model of the animal. It obtains a non-rigid mapping between the individual positions and shapes of the animal during the sequence, based on discrete sets of landmark candidates and according texture features. This is of high relevance in small animal research to integrate signals from multiple frames, and cannot be achieved by standard continuous registration methods. We report first experimental results on video sequences of a rat.
Motion-based enhancement of optical imaging

Optical imaging offers the possibility to study and image biology at molecular scale in small animal with applications such as oncology or gene expression studies. New device enable cinematic acquisitions but in some cases signals could suffer from weak spatial information. The aim of this paper is to improve localization of the luminescent data for a freely moving animal using motion information obtained with an additional camera. To this end, we propose an approach that performs a non-rigid registration in the scene video and use the corresponding motion vectors to filter the optical one. Due to the lack of contrast and texture, our method introduces silhouette constraints and landmarks on the mouse skin within a variation framework. Motion is represented using thin-plate-splines and the objective function is optimized using a gradient conjugate descent approach. Experimental results demonstrate the potentials of the proposed framework.