nils-herrmann/clean_scirepeval huggingface dataset:
Towards accurate and efficient representation of image irradiance of convex-Lambertian objects under unknown near lighting

Surface irradiance signals are turned into outgoing radiance through the surface reflectance function, which can be significantly perturbed by the illumination conditions. Due to their low-frequency nature, irradiance signals can be represented using low-order basis functions, where spherical harmonics (SH) have been extensively used to provide such basis. 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 are no longer orthonormal. This reduced domain paves the way for even lower-dimensional approximation since full spherical representation is not needed. While harmonic basis are known to be optimal under distant light, light coming from near-by objects and indoor environments are common near light scenarios; it is essential to relax distant light assumption. Considering light source(s) distributed uniformly over the upper hemisphere, we propose the use of hemispherical harmonics (HSH) to model image irradiance of convex Lambertian objects perceived from single viewpoint under unknown near illumination. We prove analytically, and experimentally validated, that the Lambertian kernel has a more compact harmonic expansion in the hemispherical domain when compared to its spherical counterpart. We illustrate that HSH provide an efficient and accurate low-dimensional representation of image irradiance of Lambertian objects under near lighting conditions in contrast to SH.
Model-based multiview stereo via level sets with statistical shape prior

In this paper, we propose a methodology to incorporate 3D shape prior information in multi-view stereo. This is important for applications that deal with specific category of objects. The methodology is based on a new formulation of a level-set based energy functional. Shape prior model is then embedded in the energy functional to allow the reconstruction of an object with shape variations consistent with the training model examples. Several experiments to evaluate the proposed methodology are presented.
2D-3D registration of human ankle using X-ray and CT images

In this paper, we introduce a new framework for registering pre-operative 3D volumetric data to intra-operative 2D images. We are particularly interested in examining the problem of aligning CT volumes to corresponding X-ray images. Our objective is to apply the 2D-3D registration in the field of orthopedics, specifically on ankle surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws which are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. Our framework adopts the shear-warp factorization method to generate synthetic 2D images from the given 3D volume. Also, the alignment score is determined according to Exponential Correlation (EC) as a new similarity measure. We used a variety of methods to evaluate registration quality of our system. It has been tested on 15 clinical CT datasets. Evaluation results indicate the degree of accuracy of our registration framework.
On Cluster Validity Indexes in Fuzzy and Hard Clustering Algorithms for Image Segmentation

This paper addresses the issue of assessing the quality of the clusters found by fuzzy and hard clustering algorithms. In particular, it seeks an answer to the question on how well cluster validity indexes can automatically determine the appropriate number of clusters that represent the data. The paper surveys several key existing solutions for cluster validity in the domain of image segmentation. In addition, it suggests two new indexes. The first one is based on Akaike's information criterion (AIC). While AIC was devoted to other domains such as statistical estimation of model fitting, it is implemented here for the first time as a validation index. The second index is developed from the well-established idea of cross-validation. The existing and new indexes are evaluated and compared on several synthetic images corrupted with noise of varying levels and volumetric MR data.
3D reconstruction of the human jaw from a sequence of images

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.
Better Shading for Better Shape Recovery

The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery. The algorithm takes advantage of some local shading measures that have been driven under these assumptions. The method is successfully evaluated on real data of human teeth with ground-truth 3D shapes.
Integrating stereo and shape from shading

This paper presents a new method for integrating different low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of objects from intensity images. The integration process is based on correcting the 3D visible surface obtained from shape from shading using the sparse depth measurements from the stereo module by fitting a surface into the difference between the two data sets. A feedforward neural network is used to fit a surface to the error difference.
Clinically desired segmentation method for vertebral bodies

In this paper, we propose a clinically desired segmentation method for vertebral bodies (VBs) in computed tomography (CT) images. Three pieces of information (intensity, spatial interaction, and shape) are modeled to optimize a new probabilistic energy functional; and hence to obtain the optimum segmentation. The information of the intensity and spatial interaction are modeled using the Gaussian and Gibbs distribution, respectively. A shape model is proposed using a new probabilistic function to enhance the segmentation results. This model is a generic shape information which is obtained using the cervical, lumbar, and thoracic spinal regions. We propose a semiautomated segmentation algorithm which uses limited interventions only in the VB separation process. The overall segmentation process completes the task in very low execution time which is one of the most important contribution of this paper. The proposed method is validated with clinical CT images and on a phantom with various Gaussian noise levels. This study reveals that the proposed method is robust under various noise levels, less variant to the initialization, and quite faster than alternative methods. One of the most important contributions of our paper is to offer a segmentation framework which can be suitable to the clinical works with acceptable results.
Heat diffusion over weighted manifolds: A new descriptor for textured 3D non-rigid shapes

This paper proposes an approach for modeling textured 3D non-rigid models based on Weighted Heat Kernel Signature(W-HKS). As a first contribution, we show how to include photometric information as a weight over the shape manifold, we also propose a novel formulation for heat diffusion over weighted manifolds. As a second contribution we present a new discretization method for the proposed equation using finite element approximation. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. We also propose a new method to introduce the scale invariance for the weighted heat kernel signature. The performance is tested on two benchmark datasets. The results have indeed confirmed the high performance of the proposed approach on the textured shape retrieval problem, and showed that the proposed method is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.
Accurate and fast 3D colon segmentation in CT colonography

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.
Illumination-invariant Statistical Shape Recovery with Contiguous Occlusion

Spherical harmonics (SH) has been an attractive fit for illumination modeling in shape recovery after the conclusion drawn by Basri and Jacobs . The main challenge is the computation of the spherical harmonics projection (SHP) images to be robust against imaging conditions other than illumination. Occlusions due to wearing apparel and makeup, or even incompliance to the requirement of the statistical model introduce errors in the reconstructed SHP images which in turn has a direct impact on the recovered shape. In this paper, we propose to cast errors introduced due to occlusion as: (1) statistical outliers which are determined and rejected using robust statistics and (2) local spatial erroneous continuous regions where Markov Gibbs random field with the homogenous isotropic Potts model is adopted to model the occlusion's spatial interaction. Our results show the effectiveness of the proposed algorithms in handling high levels of contiguous occlusion compared to one of the state-of-the-art statistical illumination invariant shape-from-shading [18]. In particular, MGRF and robust estimation using Geman-McClure function outperform the singular value decomposition (SVD) performance approach which is very sensitive to the presence of occlusion even at low levels. In the meantime, the performance of Lorenztian function approaches SVD due to the presence of errors caused by basis of different identity than the shape to be reconstructed. We provide empirical validation of our conclusions by simulations and real experiments.
Less is more: Cropping to improve facial recognition with thermal images

Numerous studies have investigated the functionality of the human facial recognition mechanisms with aims to improve computer vision methodologies. Understanding these mechanisms is important for applications in computer vision, as one goal of researchers is to create automatic facial recognition systems to equal the robustness of the human facial recognition system. Work in the field of human facial recognition shows that humans are able to recognize faces under very challenging scenarios, including partial occlusion. However, most human facial recognition experiments are conducted in the visible and near-infrared spectrums. During human facial recognition experiments with thermal images conducted in the lab, we found that removing the lower face yields better recognition results than when using the entire face. These tests included standard facial recognition algorithms and the results showed Rank 1 Recognition improvements of 10–25%, depending on the amount of occlusion derived from certain anatomical landmarks.
MRA data segmentation using level sets

In this paper, we use a level set based segmentation algorithm to extract the vascular tree from magnetic resonance angiography, "MRA". Classification model finds an optimal partition of homogeneous classes with regular interfaces. Regions and their interfaces are represented by level set functions. The algorithm initializes level sets in each image slice using automatic seed initialization and then iteratively, each level set approaches the steady state and contains the vessel or nonvessel area. The algorithm is applied on each slice of the volume to build up the tree. The results are validated using a phantom that simulates the "MRA". The approach is fast and accurate. Results on various cases demonstrate the accuracy of the approach.
Probability density estimation by linear combinations of Gaussian kernels- generalizations and algorithmic evaluation

This paper examines parametric density estimation using a variable weighted sum of Gaussian kernels, where the weights may take positive and negative values. Various statistical properties of the estimator are studied as well as its extensions to multidimensional probability density estimation. Identification of the estimator parameters are computed by a modified EM algorithm and the number of kernels are estimated by information theoretic approach, using the Akiake Information Criterion (AIC). This paper provides empirical evaluation of the estimator with respect to window-based estimators and the classical linear combinations of Gaussian estimator that uses only positive weights, showing its robustness (in terms of accuracy and speed) for various applications in image and signal analysis and machine learning.
Nonmetric lens distortion calibration: closed-form solutions, robust estimation and model selection

We address the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement in one form or another, we present an automatic approach based on the robust the-least-median-of-squares (LMedS) estimator. Our approach is thus less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3D linear segments. Our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a nonlinear optimization algorithm to straighten imaged lines. Moreover we propose a method for distortion model selection based on geometrical inference. Successful experiments to evaluate the performance of this approach on synthetic and real data are reported.
Variational Curve Skeletons Using Gradient Vector Flow

Representing a 3D shape by a set of 1D curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, subvoxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (\beta-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from these nodes along the cost field constructed by the second front (\alpha-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape and hence are less sensitive to noise.
Surfacing Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching

This paper introduces anew free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information (seen from certain points) and produces images, called "surface signatures," at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose using template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real-time registration. We show that the signature representation can be used to recover scaling transformation as well as matching objects in 3D scenes in the presence of clutter and occlusion. Applications presented include: free-form object matching, multimodal medical volumes registration, and dental teeth reconstruction from intraoral images.
Curve/Surface Representation and Evolution Using Vector Level Sets with Application to the Shape-Based Segmentation Problem

In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in (H. E. Abd El Munim, et al., Oct. 2005). Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework
Face Recognition at-a-Distance Using Texture, Dense- and Sparse-Stereo Reconstruction

This paper introduces a framework for long-distance face recognition using dense and sparse stereo reconstruction, with texture of the facial region. Two methods to determine correspondences of the stereo pair are used in this paper: (a) dense global stereo-matching using maximum-a-posteriori Markov Random Fields (MAP-MRF) algorithms and (b) Active Appearance Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse correspondences. Experiments are performed using combinations of different features extracted from the dense and sparse reconstructions, as well as facial texture. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces.
3D Vertebrae Segmentation in CT Images with Random Noises

Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However, the patient receives higher radiation dose in this case. In this paper, we use our robust 3D framework to segment vertebral bodies (VBs) in clinical computed tomography (CT) images with different noise levels. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG). Initial segmentation based on the LCG models is then iteratively refined by using Markov Gibbs random field(MGRF) with analytically estimated potentials. Experiments on the data sets show that the proposed segmentation approach is more accurate and robust than other known alternatives.
A novel framework for N-D multimodal image segmentation using graph cuts

This work proposes a new MAP-based segmentation framework of multimodal images. In this work a joint MGRF model is used to describe the image. The main focus here is a more accurate model identification. For a known number of classes in the given image, the empirical distributions of this image signals are precisely approximated by a LCG distributions with positive and negative components. Gibbs potential, which is used to identify the spatial interaction between the neighboring pixels, is analytically estimated. Finally, an energy function using the previous models is formulated and is globally minimized using graph cuts. Experiments show that the developed technique gives promising accurate results compared to other known algorithms.
Robust segmentation of lung tissue in chest CT scanning

This paper deals with segmentation of the lung tissues from low dose CT (LDCT) scans of the chest. Goal is correct segmentation as well as maintaining the details of the lung region in the chest cavity. In particular, it is essential that the lung nodules inside the lungs as well as on the boundary regions be maintained for subsequent steps that aim at automatic detection and classification of nodules from LDCT scanning; a step for early diagnosis of lung cancer. An approach for segmentation based on combination of EM algorithm and morphological operations is presented. This algorithm is compared with respect two other approaches that are based on level sets and energy optimization by the Graph Cuts technique. Performance evaluation is conducted on a labeled data set from the Early Lung Cancer Action Program (ELCAP) database. The new segmentation approach provides comparable results to Level sets and Graph-Cuts, with the advantage of faster execution time, and minimal user interception.
Parametric and non-parametric techniques for identifying images of F-actin distribution in endothelial cells with applied agonists

This research deals with developing automatic classification algorithms for identifying images of F-actin distribution in endothelial cells with different treatment of agonists. Parametric and non-parametric classification techniques were investigated such as statistical and artificial neural network classifiers. First and second order features were extracted from the images. Among the statistical classification techniques are the Bayes approach and the K-nearest neighbor (K-NN). For the neural network approach, we used the multilayer feedforward and the functional link network. All of these techniques provide adequate results with the neural methods performing with higher accuracy reaching above 97% classification.