nils-herrmann/clean_scirepeval huggingface dataset:
Toward active and unobtrusive engagement assessment of distance learners

Student behavior and lecturer oversight in the classroom is known to modulate study behaviors and impact performance and learning outcomes, but cannot at present be managed for distance learning students. Quantifying and automatically measuring student engagement during lectures in a scalable and accessible manner for these students is essential for improving academic success, but has not been studied widely in natural distance learning environments. We collect video recordings from a screen-mounted camera of students studying online lectures in a mostly unstructured setting and gather annotations from a panel of humans for assessing student engagement levels. We present results on the prediction of different representations of engagement, both with subject-independent and individual-specific models, and quantify the performance gap between the generalized and personalized models for engagement prediction. While the subject-independent performance is challenged by data sparsity, results show that the individual-specific models can predict engagement well even with very few labeled examples.
3D Vertebral Body Segmentation Using Shape Based Graph Cuts

Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs). In this paper, we propose a novel 3D shape based method to segment VBs in clinical computed tomography (CT) images without any user intervention. The proposed method depends on both image appearance and shape information. 3D shape information is obtained from a set of training data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. To segment a VB, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
Labelling color images by modelling the colors density using a linear combination of Gaussians and EM algorithm

Parametric density estimation is widely used to solve many image processing problems. We examined the parametric estimation using linear combination of 1D Gaussians in many works [1, 2, 3, 4]. In this work, we extend our model to estimate density of the colors in color images. We approximate the marginal density of each class in the empirical probability density function by a 3D Gaussian distribution. Then, the deviation between the estimated and the empirical densities is modelled using a linear combination of 3D Gaussians with positive and negative components. We estimate the parameters of this model using our modified EM algorithm. The proposed framework demonstrates very promising experimental results of color images labelling and can be integrated with many other frameworks.
Enhancing virtual colonoscopy with a new visualization measure

In this paper, a new 3D surface visualization measure is proposed. The proposed measure can be used in Virtual Colonoscopy (VC) as a caparison metric of different visualization approaches and each other. The new measure utilizes the projection direction of the camera axis and surface normal as well as the camera focal length to the surface distance ratio, thus, providing a more realistic interpretation of the actual visible surface projected. These enhancements avoid many of the shortcomings of older metrics. The Virtual Fly-Through and Fly-Over visualization approaches used in VC are compared. Result show the advantages of using the proposed measure using color coding Heat-maps to enhance the visualization process.
Non-Lambertian Model-based Facial Shape Recovery from Single Image Under Unknown General Illumination

Through depth perception, humans have the ability to determine distances based on a single 2D image projected on their retina, where shape-from-shading (SFS) provides a mean to mimic such a phenomenon. The goal of this paper is to recover 3D facial shape from a single image of unknown general illumination, while relaxing the non-realistic assumption of Lambert Ian reflectance. Prior shape, albedo and reflectance models from real data, which are metric in nature, are incorporated into the shape recovery framework. Adopting a frequency-space based representation of the image irradiance equation, we propose an appearance model, termed as Harmonic Projection Images, which accounts explicitly for different human skin types as well as complex illumination conditions. Assuming skin reflectance obeys Torrance-Sparrow model, we prove analytically that it can be represented by at most 5th order harmonic basis whose closed form is provided. The recovery framework is a non-iterative approach which incorporates regression-like algorithm in the minimization process. Our experiments on synthetic and real images illustrate the robustness of our appearance model vis-a-vis illumination variation.
A neural approach for single- and multi-image camera calibration

This paper presents the neurocalibration approach as a new neural-based solution for the problem of camera calibration. Unlike some existing neural approaches, our calibrating network can match the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. In order to improve the accuracy of calibration results, the paper demonstrates the application of the neurocalibration technique to multi-image camera calibration. In such a case, many images are taken by the same camera but from different (rotated and/or translated) positions. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.
Elastic Registration of the Corpus Callosum

Geometric analysis of postmortem normal and autistic human subjects reveal distinctions in deformations in the corpus callosum (CC) that may be used for image analysis-based studies of autism. Preliminary studies showed that the CC of autistic patients is quite distinct from normal controls. We use an implicit vector representation of CC to carry out the registration process which measures the differences between different CC's. This paper introduces a new method for the 2D shape registration problem by matching vector distance functions. A variational frame work is proposed for the global and local registration of CC's. A gradient descent optimization is used which can efficiently handle both the rigid and the non-rigid operations together. The registration of real CC extracted from postmortem data sets demonstrates the potential of the proposed approach.
On empirical estimation of the parameters of edge enhancement filters

The authors develop an empirical measure for the selection of the Gaussian filter that is commonly used for edge enhancement. The measure is based totally on the image at hand. Edge enhancement by a Gaussian filter has two distinct advantages: (1) the filter is fully described by a single parameter, the standard deviation sigma ; (2) the two-dimensional filter is separable and can be easily implemented. The filter's spatial support is a function of sigma . This support is normally in the range of +or-3.5 sigma . An empirical measure is described for the selection of the Gaussian filter's spatial support using the power spectrum density of the input image. Classic Fourier analysis is used to obtain a measure for the spatial support of the Gaussian filter given a particular image. Experimental results suggest that this measure can be used as an aid in deciding the Gaussian filter's spatial support needed to enhance the edges.<<ETX>>
A 3-D reconstruction system for the human jaw using a sequence of optical images

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning, and surgical simulation. Dentistry requires an accurate three-dimensional (3-D) representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system constructs a 3-D model of the patient's dental occlusion using an intraoral video camera. A modified shape from shading (SFS) technique, using perspective projection and camera calibration, extracts the 3-D information from a sequence of two-dimensional (2-D) images of the jaw. Data fusion of range data and 3-D registration techniques develop the complete jaw model. Triangulation is then performed, and a solid 3-D model is reconstructed. The system performance is investigated using ground truth data, and the results show acceptable reconstruction accuracy.
Rejecting pseudo-faces using the likelihood of facial features and skin

Face detection and Facial feature extraction are considered among the most studied topics in the field of biometrics. In real-world uncontrolled scenarios, high rate of false alarm is still a major problem. This paper presents a solution to reduce false alarm rate resulting from any generic face detector, through a fast post-processing algorithm based on utilizing a probabilistic framework for facial feature extraction combined with skin model. In particular, the facial features detection method inspects the geometry of the face and the likelihood of each facial feature location while the skin detector utilizes the complimentary information of color. Experimental results show a significant improvement over the state-of-the-art on the FDDB and LFPW database.
Selective part models for detecting partially occluded faces in the wild

Faces in the wild have recently captured the focus of researchers for all facial analysis problems. Partial occlusion is a major problem for analyzing faces captured in unconstrained non-cooperative conditions. Even detecting the faces in such conditions is a challenging problem that needs to be solved before any further analysis of such faces can be done. In this paper, we propose modelling the face as a collection of parts that can be selected from the visible regular facial features and some other objects that can possibly occlude faces such as sunglasses, caps and hands. The proposed algorithm is more toward scene understanding in the sense that it is not only detecting faces but it also suggests the visible parts of these faces and even some of the occluding objects which can help in any further analysis. Experimental results show a state of the art performance on the challenging FDDB database with a thorough analysis of the performance with different types of partial occlusion.
Support Vector Machines for Camera Calibration Problem

This paper presents a statistical learning-based solution to the camera calibration problem in which the support vector machines (SVM) are used for the estimation of the projection matrix elements. The projection matrix is obtained explicitly by using a dot product kernel in the formulation of the SVM algorithm. The mean field theory is used to approximate an efficient learning procedure for the SVM algorithm. In order to assess the robustness of the proposed approach against noise, the experiments using synthetic data are carried out at different noise levels. The proposed approach is evaluated also with real 3D reconstruction experiments. The experimental results illustrate that the proposed calibration approach is efficient and more robust against noise than other known approaches for camera calibration.
Shape from shading for hybrid surfaces as applied to tooth reconstruction

Accurate 3-D modeling of the human teeth helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral surgeons, 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. This paper evaluates 3-D tooth reconstruction using three SFS models. Based on quantitative error analysis and curvature analysis using Robust Point Matching (RPM) and visualization results, the assumption of a perspective camera projection and an Oren-Nayar reflectance model has been proved to be the most ideal for extracting crown information from an image of a human tooth. Three reconstructed teeth have been shown to demonstrate the performance of this model.
Neurocalibration: a neural network that can tell camera calibration parameters

Camera calibration is a primary crucial step in many computer vision tasks. We present a new neural approach for camera calibration. Unlike some existing neural approaches, our calibrating network can tell the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. The neurocalibration technique is shown to solve four different types of calibration problems that are found in computer vision applications. Moreover, it can be extended to the more difficult problem of calibrating cameras with automated active lenses. The validity and performance of our technique are tested with both synthetic data under different noise conditions and with real images. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.
Two Sequential Stages Classifier for Multispectral Data

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.
What Can an Image of a Smooth Lambertian Surface Tell About Camera Nonlinearity?

This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method.
Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
Active contours: an overview with applications to motion artifact cancellation in MRI

Motion can be estimated by detecting the edges of a moving object using Active Contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquistion, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using Active Contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images.
Active contours: an overview with applications to motion artifact cancellation in MRI

Motion can be estimated by detecting the edges of a moving object using active contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquisition, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using active contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images.
Color segmentation using an Eigen color representation

In real images, color segmentation is a challenging problem because of natural variations in light and shadows. Most existing color models have limitations in distinctive representation of colors in real scenes. Color segmentation approaches are highly dependent on the robustness of the used color model to these variations. In this paper, we present a novel color representation for color segmentation. The proposed model is a fusion of classical color representations. It is calculated using a closed form linear transformation from a high dimensional classical feature space to a lower dimensional new space. We prove the high performance of the proposed model in terms of robustness to natural variations and distinction between different colors, when compared with classical color models. Through color segmentation of real images, we provide a quantitative and qualitative comparison between the proposed model and classical models. The experimental results support the potential of the proposed model.
Monocular, vision based, autonomous refueling system

This paper describes design and implementation of a vision based platform for automated refueling tasks. The platform is an autonomous docking system in principle, with the specific application - refueling of vehicles. The system is. based on monochromatic, monocular vision, and it utilizes very specialized image processing schemes. Image processing consists of very fast filtering and segmenting algorithms, as well as moment's computation. A robotic arm with 6 joints (FANUC M-6i), and a controller unit (R-B), does the physical work. A serial interface, with very high-level commands, connects a supercomputing machine and the robot's controller. A practical setup would probably be scaled down to a special design robot, and a single processor, controller with special VLSI chips for image processing. Results are very promising; the robot can identify the cap position, orientation, and height in real time with acceptable accuracy and reliability.
PDE-based robust robotic navigation

In this paper, we propose a general, robust, and fast path planning framework for robotic navigation using level set methods. A medial point of the map is selected automatically to be a point source that transmits two wave fronts of different speeds. The first front propagates with a moderate speed to capture the map topology, while the second one propagates much faster at medial points such that the safest paths intersect the propagating fronts at those points of maximum positive curvature, which are identified by solving an ordinary differential equation (ODE). The motion of the front is governed by a nonlinear partial differential equation (PDE), which is efficiently solved using robust level set methods. The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum paths for partial or complete configuration space. Optimum planned paths can be controlled according to the surrounding environment and running conditions in order to follow the safest, shortest, or hybrid paths. We demonstrate the robustness of the proposed method by correctly extracting planned paths of complex maps with several obstacles.
Image irradiance harmonics: a phenomenological model of image irradiance of arbitrary surface reflectance

Phenomenological appearance models capture surface appearance through mathematical modelling of the reflection process. Theoretically, the space of all possible images of a fixed-pose object under all possible illumination conditions is infinite dimensional. Nonetheless, because of their low-frequency nature, irradiance signals can be represented using low-order basis functions. Discounting subsurface scattering and surface emittance, this work seeks to address the question; how to compactly and accurately represent image irradiance under unknown general illumination, given that a surface point sees its surrounding world through the local upper hemisphere oriented by the surface normal at this point. In this study, we formulate the image formation process of isotropic surface reflectance under arbitrary distant illumination in the frequency space while addressing the physical compliance of hemispherical basis for representing surface reflectance, for example, Helmholtz reciprocity and isotropy. The term `irradiance harmonics' is also defined which enables decoupling illumination and reflectance from the underlying geometry and pose. We provide a closed form of the energy content being maintained by different reflectance modes of the proposed irradiance harmonics. Since specular materials tend to require more basis functions when compared with diffuse ones, the presented harmonics captures same cumulative energy content, by providing larger number of orthogonal irradiance basis, at lower illumination orders when compared to similar basis in literature.
A NEW VARIATIONAL APPROACH FOR 3D SHAPE REGISTRATION

Shape-based approaches became very popular in medical imaging. The key issue in these methods is the shape representation and registration. This paper introduces a new method for the 3D shape registration problem by matching vector distance functions. The vector distance function representation is more flexible than the conventional signed distance map since it enables us to better control the shapes registration process by using more general transformations. Based on this model, a variational frame work is proposed for the global and local registration of shapes which does not need any point correspondences. A gradient descent optimization is used and efficiently can handle both the rigid and the non-rigid operations together. The registration of real 3D human teeth models demonstrates the potential of the proposed approach