nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.
A MRF-based approach for real-time subway monitoring

There has been an increase in the use of video surveillance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion density estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detection algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from different sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth motion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental results in a metro station of a metropolitan city demonstrates the performance and the potential of our method.
Background modeling and subtraction of dynamic scenes

and subtraction is a core component in motion analysis. The central idea behind such module is to create a probabilistic representation of the static scene that is compared with the current input to perform subtraction. Such approach is efficient when the scene to be modeled refers to a static structure with limited perturbation. In this paper, we address the problem of modeling dynamic scenes where the assumption of a static background is not valid. Waving trees, beaches, escalators, natural scenes with rain or snow are examples. Inspired by the work proposed by Doretto et al. (2003), we propose an on-line auto-regressive model to capture and predict the behavior of such scenes. Towards detection of events we introduce a new metric that is based on a state-driven comparison between the prediction and the actual frame. Promising results demonstrate the potentials of the proposed framework.
Discrete tracking of parametrized curves

A novel scheme for deformable tracking of curvilinear structures in image sequences is presented. The approach is based on B-spline snakes defined by a set of control points whose optimal configuration is determined through efficient discrete optimization. Each control point is associated with a discrete random variable in a MAP-MRF formulation where a set of labels captures the deformation space. In such a context, generic terms are encoded within this MRF in the form of pairwise potentials. The use of pairwise potentials along with the B-spline representation offers nearly perfect approximation of the continuous domain. Efficient linear programming is considered to recover the approximate optimal solution. The method is successfully applied to the tracking of guide-wires in fluoroscopic X-ray sequences of several hundred frames which requires extremely robust techniques.
Detecting multiple moving targets using deformable contours

This paper presents a framework for detecting multiple moving moving objects in a sequence of images. Using a statistical approach, where the inter-frame difference is modeled by a mixture of two Laplacian distributions and a deformable contour-based energy minimization approach, we reformulate the motion detection problem as a front propagation problem. Following the work of geodesic active contours, we transform the moving objects detection problem into an equivalent problem of geodesic computation, which is solved using a level set formulation scheme. To reduce the computational cost required by a direct implementation of the formulation scheme the narrow band technique is used. In order to further reduce the CPU time, a multi-scale approach has also been considered. Very promising experimental results are provided using real video sequences.
Atlas-based deformable mutual population segmentation

Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-to-case segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.
Postarthroplasty Examination Using X-Ray Images

Arthroplasty, the implantation of prostheses into joints, is a surgical procedure that is affecting a larger and larger number of patients over time. As a result, it is increasingly important to develop imaging techniques to noninvasively examine joints with prostheses after surgery, both statically and dynamically in 3-D. The static problem is considered here, with the aim to create a 3-D shape model of the bone as well as the prosthesis using a set of 2-D X-rays from various viewpoints. The most important challenge to be addressed is the lack of texture, the most common feature to recover shape from multiple views. In order to overcome this limitation, we reformulate the problem using a novel multiview segmentation approach where an active contours 3-D surface evolution with level-set implementation is used to recover the shape of bones and prostheses in postoperative joints. The recovered shape may then be used to track 3-D motions in dynamic X-ray sequences to obtain kinematic information.
Unsupervised co-segmentation through region matching

Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.
An Application of Graph Commute Times to Image Indexing

In this paper we provide an overview of an image representation approach based on the description of layout and appearance properties of groups of features. In each image a graph of quantized features of interest is constructed. The features that are assigned to the same codebook bin are then grouped to produce a collapsed graph; the image content is represented by the matrix of commute times of this collapsed graph. This novel image descriptor can be used to label satellite image databases; we demonstrate the relevance and the efficiency of our approach by addressing classification problems on a dataset of 0.6 m resolution Quick bird images.
Deep patch-based priors under a fully convolutional encoder-decoder architecture for interstitial lung disease segmentation

Interstitial lung diseases (ILD) encompass a large spectrum of diseases sharing similarities in their physiopathology and computed tomography (CT) appearance. In this paper, we propose the adaption of a deep convolutional encoder-decoder (CED) that has shown high accuracy for image segmentation. Such architectures require annotation of the total region with pathological findings. This is difficult to acquire, due to uncertainty in the definition and extent of disease patterns and the need of significant human effort, especially for large datasets. Therefore, often current methods use patch-based implementations of convolutional neural networks, which however tend to produce spatially inhomogeneous segmentations due to their local contextual view. We exploit the advantages of both architectures by using the output of a patch-based classifier as a prior to a CED. Our method could advance the state-of-the-art in lung tissue segmentation using only a small number of newly annotated images.
Gradient vector flow fast geodesic active contours

This paper proposes a new front propagation flow for boundary extraction. The proposed framework is inspired by the geodesic active contour model and leads to a paradigm that is relatively free from the initial curve position. Towards this end, it makes use of a recently introduced external boundary force, the gradient vector field that refers to a spatial diffusion of the boundary information. According to the proposed flow, the traditional boundary attraction term is replaced with a new force that guides the propagation to the object boundaries from both sides. This new geometric flow is implemented using a level set approach, thereby allowing dealing naturally with topological changes and important shape deformations. Moreover the level set motion equations are implemented using a recently introduced numerical approximation scheme, the Additive Operator Splitting Schema (AOS) which has a fast convergence rate and stable behavior. Encouraging experimental results are provided using real images.
Motion-based background subtraction using adaptive kernel density estimation

Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.
Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper.
Model-Based 3D Hand Pose Estimation from Monocular Video

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 temporal texture 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 provide 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. To this end, we introduce new occlusion forces and show that using all gradient terms greatly improves the performance of the method. Qualitative and quantitative experimental results demonstrate the potential of the approach.
Robust shadow and illumination estimation using a mixture model

Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of exact 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination environment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of samples generated from this illumination environment. We integrate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D reasoning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the augmented reality results.
Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration

Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.
LOCALLY ADAPTIVE AUTOREGRESSIVE ACTIVE MODELS FOR SEGMENTATION OF 3D ANATOMICAL STRUCTURES

Many techniques of knowledge-based segmentation consist of building statistical models that describe the deformations of the structure of interest, and then fit these models to the image data. In this paper, we introduce a novel family of shape prior models that aim to capture such varying support. To this end, 3D segmentation is considered by modeling the relationship between contours on consecutive slices using autoregression. Then, the segmentation is performed progressively on the 2D slices in a qualitative fashion, starting from the ones with strong data support toward the ones of limited support. Successive segmentation maps are linked through a locally adaptive autoregressive prediction mechanism - that is learned through training - where confidence of the data from prior slices constrains the results. Such prediction is integrated with a contour minimization technique, leading to a Bayesian sequential procedure that iteratively predicts and corrects 2D contours leading to complete reconstruction of 3D anatomical structures. A quantitative comparative study with 3D active shape models demonstrate the potential of the method.
Discrete Visual Perception

Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis.
Parameter-free/Pareto-driven procedural 3D reconstruction of buildings from ground-level sequences

In this paper we address multi-view reconstruction of urban environments using 3D shape grammars. Our formulation expresses the solution to the problem as a shape grammar parse tree where both the tree and the corresponding derivation parameters are unknown. Besides the grammar constraint, the solution is guided by an image support that is twofold. First, we seek for a derivation that induces optimal semantic partitions in the different views. Second, using structure-from-motion, noisy depth maps can be determined towards minimizing their distance from to the ones predicted by any potential solution. We show how the underlying data structure can be efficiently optimized using evolutionary algorithms with automatic parameter selection. To the best of our knowledge, it is the first time that the multi-view 3D procedural modeling problem is tackled. Promising results demonstrate the potentials of the method towards producing a compact representation of urban environments.
Sub-cortical brain structure segmentation using F-CNN'S

In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.
Uncertainties-driven Surface Morphing: The case of Photo-realistic Transitions between Facial Expressions

Reproduction of facial animation play a fundamental role in applications requiring human-computer interactions. The objective of this paper is to introduce a geometric mechanism that exploits a fix number of states and is able to execute a subsequent number of transitions between facial expressions. Standard stereo-based techniques are used to reproduce the geometry and appearance of the most characteristic facial expressions. A novel free-form-deformation technique based on uncertainty driven local geometric registration in the space of distance transforms is used to produce a one-to-one mapping between the surfaces and the associated textures. Standard techniques from image morphing introduce the temporal aspect in the process. Experimental results and comparisons with actual observation demonstrate the potentials of such an approach
Needle tracking through higher-order MRF optimization

We propose a Markov Random Field formulation for the tracking of needles in fluoroscopic images. A novel motion model makes it possible to capture the primarily rigid motion as well as deformations of the needle in a single second-order MRF graph. Needles are represented by B-splines and each control point is associated with a random variable in a MAP-MRF formulation. In addition to the control points we introduce a single additional random variable representing the rigid transformation needles undergo during interventions. The incorporation of rigid transformations allows to recover transformations even in the presence of large displacements which is not possible with existing MRF models for medical tool tracking.
Boosted metric learning for 3D multi-modal deformable registration

Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method.