nils-herrmann/clean_scirepeval huggingface dataset:
Multi-modal medical volumes fusion by surface matching

Presents a fast, six degrees of freedom, registration technique to accurately locate the position and orientation of medical volumes (e.g. CT, MRI) with respect to each other for the same patient. The technique uses surface registration and maximization of mutual information. We have developed a novel technique for surface registration which produces highly accurate results when registering two different volumes of the same individual generated from the same modality, such as preoperative MR and intraoperative MR volumes. In case surface registration is not able to accurately register the different volumes, the result is enhanced by multi-modal volume registration. The gain of this combination is to have an accurate alignment and to reduce time needed for registration. For the multi-modal volume registration, the maximization of mutual information (MI) as a matching criterion is used based on genetic algorithms (GA) as a search engine. Our results demonstrate that our registration technique allows for fast, accurate, robust and completely automatic registration of multimodality medical volumes.
Face and eye detection on hard datasets

Face and eye detection algorithms are deployed in a wide variety of applications. Unfortunately, there has been no quantitative comparison of how these detectors perform under difficult circumstances. We created a dataset of low light and long distance images which possess some of the problems encountered by face and eye detectors solving real world problems. The dataset we created is composed of reimaged images (photohead) and semi-synthetic heads imaged under varying conditions of low light, atmospheric blur, and distances of 3m, 50m, 80m, and 200m. This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.
Clinical crowns shape reconstruction - An image-based approach

Precise knowledge of the 3D shape of clinical crowns is crucial for the treatment of malocclusion problems as well as several endodontic procedures. While Computed Tomography (CT) would present such information, it is believed that there is no threshold radiation dose below which it is considered safe. In this paper, we propose an image-based approach which allows for the construction of plausible human jaw models in vivo, without ionizing radiation, using fewer sample points in order to reduce the cost and intrusiveness of acquiring models of patients teeth/jaws over time. We assume that human teeth reflectance obeys Wolff-Oren-Nayar model where we experimentally prove that teeth surface obeys the microfacet theory. The inherent relation between the photometric information and the underlying 3D shape is formulated as a statistical model where the coupled effect of illumination and reflectance is modeled using the Helmhotlz Hemispherical Harmonics (HSH)-based irradiance harmonics whereas the Principle Component Regression (PCR) approach is deployed to carry out the estimation of dense 3D shapes. Vis-`a-vis dental applications, the results demonstrate a significant increase in accuracy in favor of the proposed approach where our system is evaluated on a database of 16 jaws.
Realistic 3D reconstruction of the human teeth using shape from shading with shape priors

Dentistry usually requires accurate 3-D representation of the teeth and jaw for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost-effective solution in that regard. In this paper, we develop an approach for realistic 3D reconstruction of the human teeth using shape from shading with statistical shape priors. Our work has addressed several challenges including near illumination, camera perspective projection, while taking into account the deviation from the simplifying Lambertian assumption. We use the Oren-Nayar reflectance model for diffuse rough surfaces with the roughness parameter being physically measured by an optical surface profiler. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human teeth. Our experiments provide quantitative metric results for the proposed approach. As compared to state-of-art SFS approaches, our approach is able to recover geometric details of a tooth occlusal surface.
Nonmetric calibration of camera lens distortion: differential methods and robust estimation

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also provide fast, closed-form solutions to the distortion coefficients. We prove that including both the distortion center and the decentering coefficients in the nonlinear optimization step may lead to instability of the estimation algorithm. Our approach provides a way to get around this, and, at the same time, it reduces the search space of the calibration problem without sacrificing the accuracy and produces more stable and noise-robust results. In addition, while almost all existing nonmetric distortion calibration methods needs user involvement in one form or another, we present a robust approach to distortion calibration based on the least-median-of-squares estimator. Our approach is, thus, able to proceed in a fully automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered projections of three-dimensional linear segments. Experiments to evaluate the performance of this approach on synthetic and real data are reported.
Extraction of the superficial facial vasculature, vital signs waveforms and rates using thermal imaging

This work deals with non-invasive and non-intrusive measurements of the human facial vasculature from thermal imaging, and estimates the waveforms and rates of the arterial pulse. The paper addresses the issues involved with use of Long-Wave IR imaging in studies of human biometrics, and focuses on measurements of human's heart rate based on a small number of thermal video frames. Infrared videos of 30 subjects were recorded at two distances. Vascular mapping and multi-resolution analysis are used to calculate a subject's heart rate using only 512 Long-Wave IR video frames (17.07 seconds). This semi-automatic process is typically 89–99% accurate, with average accuracies at both distances at approximately 93%. The work is a step in a comprehensive computer vision system for computational studies on human-machine interface.
Density estimation using a new AIC-type criterion and the EM algorithm for a linear combination of Gaussians

We propose a new approach that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). The proposed algorithm approximates the marginal density of each class using a Gaussian distribution. Number of the classes and their distributions parameters are estimated using a new Akaike Information Criterion (AlC)-type criterion and the Expectation- Maximization (EM) approach. Each class does not follow perfect Gaussian distribution so we refine the initial LCG model using a modified EM algorithm. The modified EM algorithm approximates the marginal density of each class using a LCG with positive and negative components. Experiments in segmenting multimodal medical images show that the developed technique gives promising accurate results.
On the use of hemispherical harmonics for modeling images of objects under unknown distant illumination

A surface reflectance function represents the process of turning irradiance signals into outgoing radiance. Irradiance signals can be represented using low-order basis functions due to their low-frequency nature. Spherical harmonics (SH) have been used to provide such basis. However the incident light at any surface point is defined on the upper hemisphere; full spherical representation is not needed. We propose the use of hemispherical harmonics (HSH) to model images of convex Lambertian objects under distant illumination. We formulate and prove the addition theorem for HSH in order to provide an analytical expression of the reflectance function in the HSH domain. We prove that the Lambertian kernel has a more compact harmonic expansion in the HSH domain when compared to its SH counterpart. Our experiments illustrate that the 1st order HSH outperforms 1st and 2nd order SH in the process of image reconstruction as the number of light sources grows.
Computationally light forehead segmentation from thermal images

Thermal images are often paired with visible spectrum images captured simultaneously-as surface features are easier to identify with proper illumination-to determine the thermal feature locations. Not all thermal images are necessarily recorded this way, yet determining salient feature regions is beneficial to other tasks. The forehead region is useful to the field of vital signs analysis and the goal of this work is automatically identifying the forehead region solely from thermal images. The anatomy of the forehead and its thermo-dynamic properties lend itself to minimizing the variance of a facial thermogram. Experiments conducted indoors on 32 subjects under normal, exercise and pain conditions. The proposed solution identifies the forehead region with 90-95% accuracy in 80ms or less, without initialization or training data, and can be easily implemented in parallel.
Learning a non-linear combination of Mahalanobis distances using statistical inference for similarity measure

In this study, the authors learn a similarity measure that discriminates between inter-class and intra-class samples based on a statistical inference perspective. A non-linear combination of Mahalanobis is proposed to reflect the properties of a likelihood ratio test. Since an object's appearance is influenced by the identity of the object and variations in the capturing process, the authors represent the feature vector, which is the difference between two samples in the differences space, as a sample that is drawn from a mixture of many distributions. This mixture consists of the identities distribution and other distributions of the variations in the capturing process, in case of dissimilar samples. However, in the case of similar samples, the mixture consists of the variations in the capturing process distributions only. Using this representation, the proposed similarity measure accurately discriminates between inter-class and intra-class samples. To highlight the good performance of the proposed similarity measure, it is tested on different computer vision applications: face verification and person re-identification. To illustrate how the proposed learning method can easily be used on large scale datasets, experiments are conducted on different challenging datasets: labelled faces in the wild (LFW), public figures face database, ETHZ and VIPeR. Moreover, in these experiments, the authors evaluate different stages, for example, features detector, descriptor type and descriptor dimension, which constitute the face verification pipeline. The experimental results confirm that the learning method outperforms the state-of-the-art.
A 3D reconstruction of the human jaw from a single image

Accurate 3D modeling of the human teeth/jaw helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. In this paper, we attempt to improve the limitations of previous 3D tooth reconstruction algorithms by developing a new approach for shape reconstruction from single image shading with Two-Dimensional Principle Component Analysis (2D-PCA) shape priors. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case and we experimentally prove that the teeth surface follows the microfacet theory. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human jaws. Our experiments provide promising quantitative metric results for the proposed approach. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw that is inexpensive and does not use ionizing radiation.
Integrating shape from shading and range data using neural networks

This paper presents a framework for integrating multiple sensory data, sparse range data and dense depth maps from shape from shading in order to improve the 3D reconstruction of visible surfaces of 3D objects. The integration process is based on propagating the error difference between the two data sets by fitting a surface to that difference and using it to correct the visible surface obtained from shape from shading. A feedforward neural network is used to fit a surface to the sparse data. We also study the use of the extended Kalman filter for supervised learning and compare it with the backpropagation algorithm. A performance analysis is done to obtain the best neural network architecture and learning algorithm. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements.
Pedestrian detection using mixed partial derivative based histogram of oriented gradients

Recently, several approaches for pedestrian detection have been investigated using discriminatively trained part based models with which Histogram of Oriented Gradients (HOG) showed to be a robust feature. In this paper, we propose a new feature based on HOG to be used with the discriminatively trained part framework for pedestrian detection. Our method is based on computing the image mixed partial derivatives to be used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG. Our approach was tested on the PASCAL2007 and INRIA person dataset and showed to have an outstanding performance.
Validation of Finite Element Models of Liver Tissue Using Micro-CT

In this work, the authors aim at validating some soft tissue deformation models using high-resolution micro-computed tomography (micro-CT) images. The imaging technique plays a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) for small force loads and provides good capabilities of creating accurate 3D models of soft tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite-element models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remain a challenge. In this study, ex vivo lamb liver tissue is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We find that the LEM is more applicable to lamb liver than the LVEM for smaller force loads (<20 g) and that the NHM is closer to reality than the LVEM for the range of force loads from 5 to 40 g.
Face recognition at-a-distance using texture, Sparse-Stereo, and Dense-Stereo

This paper proposes a framework for face recognition at a distance based on texture, Sparse-Stereo, and Dense-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. In this paper we introduce our stereo-based indoor/outdoor environment and different ranges human faces database. Also, we propose a front-to-end system of 3D face reconstruction and recognition. We first detect the facial region and extract its landmark points, which are used to initialize the face alignment algorithm. The fitted mesh vertices, generated from the face alignment process, provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. Also the dense 3D is reconstructed for the cropped stereo pair based on graph cut approach. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirm the feasibility of the proposed work for long distance recognition of human faces.
A new unsupervised approach for the classification of multispectral data

In this paper, we present a new approach for unsupervised pixel-wise classifcation of multispectral data. In this approach, we estimate the number of classes in a data set as well as the parameters of each class. This approach doesn't require training samples. A scene is modeled as a realization of a random field formed of a mixture distribution. We employ an EM algorithm to estimate the parameters of this mixture. The algorithm iterates to estimate the proper number of classes that compose the scene, and also the parameters of these classes. Theoretically, the algorithm can be applied for arbitrary forms of distributions for the classes in the scene, either parametric or non parametric. This paper focuses on the parametric estimation case. The approach has been applied to synthetic and real world multispectral data and provided quite encouraging results compared to other classical classification approaches.
Towards efficient image irradiance modelling of convex Lambertian surfaces under single viewpoint and frontal illumination

Under local illumination assumption, phenomenological appearance models capture surface appearance through the mathematical modelling of the reflection process. Theoretically, due to the arbitrariness of the lighting function, the space of all possible images of a fixed-pose object under all possible illumination conditions is infinite dimensional. Nonetheless, due to their low- frequency nature, irradiance signals can be represented using low-order basis functions, where spherical harmonics (SH) has been extensively adopted. When capturing image irradiance from a single viewpoint, the visible part of the object's surface constructs the upper hemisphere of the surface normals where the SH is no longer orthonormal. In this paper, we propose the use of hemispherical harmonics (HSH) to model image irradiance of convex Lambertian objects perceived from single viewpoint under unknown distant as well as near illumination. We prove analytically, and validate experimentally, that the Lambertian reflectance kernel has a more compact harmonic expansion in the hemispherical domain when compared to its spherical counterpart. Our experiments illustrate that, despite of having poor approximation accuracy under very close lights, such behavior improves exponentially with little increase in the distance to the light source relative to the object size.
An automated vertebra identification and segmentation in CT images

In this paper, we propose a new 3D framework to identify and segment VBs and TBs in clinical computed tomography (CT) images without any user intervention. The Matched filter is employed to detect the VB region automatically on axial axis. To identify the VB on coronal and sagittal axis, we use a new developed approach based on 4 points automatically placed on cortical shell. To segment the identified VB, the graph cuts method which integrates a linear combination of Gaussians (LCG) and Markov Gibbs Random Field (MGRF) are used. Then, the cortical and trabecular bones are segmented using local volume growing methods. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest

This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.
CardEye: a 3D trinocular active vision system

CardEye is an experimental, trinocular, 3D active vision system. Our goal is to create a flexible, precise tool for active vision research. The system uses an agile trinocular vision head mounted on a robotic arm. It has five degrees of freedom: pan, tilt, roll, vergence and variable baseline in addition to the automated zoom and focus of the lenses. It utilizes an active lighting device to assist in the surface reconstruction process. In this paper we describe the architecture of the system together with its functionality.
A New Formulation for Shape from Shading for Non-Lambertian Surfaces

Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are presented to show the efficiency of the proposed method . To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.
Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT

The quality of the lung nodule models determines the success of lung nodule detection. This paper describes aspects of our data-driven approach for modeling lung nodules using the texture and shape properties of real nodules to form an average model template per nodule type. The ELCAP low dose CT (LDCT) scans database is used to create the required statistics for the models based on modern computer vision techniques. These models suit various machine learning approaches for nodule detection including Bayesian methods, SVM and Neural Networks, and computations may be enhanced through genetic algorithms and Adaboost. The eminence of the new nodule models are studied with respect to parametric models showing significant improvements in both sensitivity and specificity.