nils-herrmann/clean_scirepeval huggingface dataset:
Shape Representation and Registration in Vector Implicit Spaces: Adopting a Closed-Form Solution in the Optimization Process

In this paper, a novel method to solve the shape registration problem covering both global and local deformations is proposed. The vector distance function (VDF) is used to represent source and target shapes. The problem is formulated as an energy optimization process by matching the VDFs of the source and target shapes. The minimization process results in estimating the transformation parameters for the global and local deformation cases. Gradient descent optimization handles the computation of scaling, rotation, and translation matrices used to minimize the global differences between source and target shapes. Nonrigid deformations require a large number of parameters which make the use of the gradient descent minimization a very time-consuming process. We propose to compute the local deformation parameters using a closed-form solution as a linear system of equations derived from approximating an objective function. Extensive experimental validations and comparisons performed on generalized 2D shape data demonstrate the robustness and effectiveness of the method.
Simultaneous identification and tracking of moving targets

This paper describes a framework for simultaneous identification and tracking of moving targets in random media. Video and IR thermal sensors are used to obtain the target signature. Classical Kalman filtering methods are implemented on targets with unknown trajectories. Computer vision methodologies are proposed to design a smart interceptor which identifies the targets based on shape and thermal signatures. The paper also describes a platform for basic studies in tracking of targets using vision-guided robotics. The system enables multiple object tracking and recognition.
Differential methods for nonmetric calibration of camera lens distortion

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 non-linear search techniques to find the best distortion parameters that straighten these lines. Unlike other approaches, we also provide fast, closed-form solutions to the distortion coefficients. Experiments to evaluate the performance of this approach on synthetic and real data are reported.
Passive single image-based approach for camera steering in face recognition at-a-distance applications

This paper investigates the problem of automatically steering one or more Narrow Field of View (NFOW) cameras to a target subject using only a single image from a reference NFOW camera, without the help of a Wide Field of View (WFOV) camera. To find the approximate distance of the subject from the reference camera, our algorithm uses information from facial biometrics, specifically the inter-pupil distance (IPD), and eye-to-lips distance(ELD). A trigonometric relationship is formulated to calculate the steering parameters of the other cameras. Moreover, a new robust facial feature detector is also proposed in order to estimate the required biometrics. Several experiments are reported to evaluate the proposed system.
Novel surface registration using the grid closest point (GCP) transform

A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackay's (1992) iterative closest point (ICP) algorithm. This technique solves the computational complexity associated with the ICP algorithm by applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible mis-alignment caused by motion artifacts during scanning. In the second, the algorithm is used to register 3-D segments of the human jaw obtained using the shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.
3D vertebrae segmentation using graph cuts with shape prior constraints

Osteoporosis is a bone disease characterized by a reduction in bone mass, resulting in an increased risk of fractures. To diagnose the osteoporosis accurately, bone mineral density (BMD) measurements and fracture analysis (FA) of the Vertebral bodies (VBs) are required. In this paper, we propose a robust and 3D shape based method to segment VBs in clinical computed tomography (CT) images in order to make BMD measurements and FA accurately. In this experiment, image appearance and shape information of VBs are used. In the training step, 3D shape information is obtained from a set of 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. In the segmentation step, 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.
Enhanced automatic colon segmentation for better cancer diagnosis

Colon segmentation is the first stage towards polyp detection, the main cause of colon cancer. Due to the immense importance of colon cancer diagnosis which is the second leading cause of death in the world, the segmentation phase must guarantee that no polyps are missed, especially the flat ones that are usually hard to detect. This work validates the 3D automated colon segmentation approach using the convex contour model previously proposed in literature. It also adds improvements to its pre-processing stage in order to better capture the colon walls and to enhance the results of the subsequent phases of the segmentation process. Experiments were conducted on 27 colon data sets that include 30 polyps. Moreover, 30 synthesized polyps with various shapes and sizes were placed at challenging areas of the colon's complex structure. Experiments conducted show a significant improvement in the construction of colon walls and the rate of polyp detection over that provided by the original technique.
Shape from Shading Under Various Imaging Conditions

Most of the shape from shading (SFS) algorithms have been developed under the simplifying assumptions of a Lambertian surface, an orthographic projection, and a distant light source. Due to the difficulty of the SFS problem, only a small number of algorithms have been proposed for surfaces with non-Lambertian reflectance, and among those, only very few algorithms are applicable for surfaces with specular and diffuse reflectance. In this paper we propose a unified framework that is capable of solving the SFS problem under various settings of imaging conditions i.e., Lambertian or non-Lambertian, orthographic or perspective projection, and distant or nearby light source. The proposed algorithm represents the image irradiance equation of each setting as an explicit Partial Differential Equation (PDF). In our implementation we use the Lax-Friedrichs sweeping method to solve this PDF. To demonstrate the efficiency of the proposed algorithm, several comparisons with the state of the art of the SFS literature are given.
A Fully Automatic Method to Extract the Heart Rate from Thermal Video

This work deals with non-invasive and non-intrusive measurements of the human facial vasculature from thermal IR to measure cardiovascular vital signs. A robust, fully automatic measurement system is developed to study infrared videos of 32 under three imaging scenarios. Vascular mapping, blood perfusion modeling, and wavelet analysis are used to calculate heart rate from 512 video frames in near real time. Multiple measurement locations are considered to analyze accuracy and sensitivity in a novel approach, revealing that the 60% of vessel segments in the forehead region are capable of producing 85% accuracy of measurement or better.
Automatic identification of lung abnormalities in chest spiral CT scans

Our aim is to develop a fully automatic computer-assisted diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. A screening program on 1000 subjects aims at quantification of the effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluation of its possible impact on improving the mortality rate of cancer patients. The paper presents an image analysis system for 3D reconstruction of the lungs and trachea, detection of lung abnormalities, identification/classification of these abnormalities with respect to specific diagnosis, and distributed visualization of the results over computer networks. We present two novel approaches for segmentation of the lung tissues from the surrounding structures in the chest cavity, and detection of abnormalities in the lungs. The segmentation algorithm is hierarchical, first isolating the background from the chest cavity, then isolating the lungs from surrounding structures (e.g., ribs, liver, and other organs). Abnormalities in the lungs are detected by analyzing the segmented lung tissues and extracting the isolated lumps that appear in various connected regions. 3D reconstructions are also generated for these abnormalities, to be used for subsequent identification/classification steps. Results on 50 subjects are shown, and have been evaluated against radiologists. Our image analysis approach has provided comparable results with respect to the experts. The approach is quite fast, and lends itself to distributed visualization over computer networks.
Object modeling using space carving

We consider the problem of computing the 3D shape of an unknown, arbitrary-shaped object. Starting with an initial surface, larger than the scene, we refine it using the object's projections taken at known but arbitrarily distributed viewpoints. These projections are used independently in a recursive manner. Thus, using more projections improves the reconstruction with a slight increase in the computation time. The technique generates an approximate voxelized representation of the object. We describe the proposed technique and outline the algorithm. Rendered images of voxel spaces recovered from synthetic and real observation images are shown.
Experiments on Sensitivity of Template Matching for Lung Nodule Detection in Low Dose CT Scans

Template matching is a common approach for detection of lung nodules from CT scans. Templates may take different shapes, size and intensity distribution. The process of nodule detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes of outlining the detected nodules and their classification (i.e., assigning pathology for each nodule) complete the CAD system for early detection of lung nodules. This paper is concerned with the template design and evaluating the effectiveness of the first step in the nodule detection process. The paper will neither address the problem of reducing false positives nor would it deal with nodule segmentation and classification. Only parametric templates are considered. Modeling the gray scale distribution for the templates is based on the prior knowledge of typical nodules extracted by radiologists. The effectiveness of the template matching is investigated by cross validation with respect to the ground truth and is described by hit rate curves indicating the probability of detection as function of shape, size and orientation, if applicable, of the templates. We used synthetic and sample real CT scan images in our experiments. It is found that template matching is more sensitive to additive noise than image blurring when tests conducted on synthetic data. On the sample CT scans small size circular and hollow-circular templates provided comparable results to human experts.
Segmentation of vertebrae using level sets with expectation maximization algorithm

In this paper, we propose a robust level sets method to segment vertebral bodies (VBs) in clinical computed tomography (CT) images. Since the VB and surrounding organs have very close gray level information and there are no strong edges in some CT images, the initialization of level sets method becomes very crucial step. If the object and background regions are not initialized correctly, the results would not be acceptable. Also, the size and place of the initial seed may give non-reproducible results. To solve these problems, we use a statistical level sets method which uses the Expectation- Maximization (EM) algorithm for the initialization and parameter estimation. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP) as a known reference. The proposed method is compared with other known alternatives.
Advanced algorithms for Bayesian classification in high dimensional spaces with applications in hyperspectral image segmentation

This paper proposes advanced algorithms for building different components of the Bayesian classification setup, with focus in high dimensional spaces. The mean field (MF) theory is used with support vector machines (SVM) to overcome the curse of dimensionality problem associated with the estimation of class conditional probabilities in high dimensional spaces. The Markov random fields (MRF) model is used to implement the contextual interaction of classes in an image. A new algorithm which uses SVM, in a regression prospective, is proposed for estimating the parameters of the MRF model. An iterative setup for the Bayesian image segmentation setup is proposed which maximizes the overall likelihoods of the defined classes in the image. Experimental results using a real hyperspectral remote sensing image illustrate the outstanding performance of the proposed algorithms.
A fuzzy framework with prior information unifying registration, segmentation and bias field correction of brain MRI

This paper introduces a fuzzy framework for the simultaneous segmentation and registration in addition to bias field correction of MRI datasets. The framework utilizes prior information which may be available about the tissues' mean intensities and tissues' distribution through the datasets. Moreover it works on the given, original image intensities without any logarithmic transformation and thus produces more accurate results and faster performance. The algorithm is evaluated using simulated and real brain MRI data. The results show that the algorithm has indeed improved the segmentation accuracy.
A New Shape-Based Segmentation Approach for the DCE-MRI Kidney Images

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) is considered. The algorithm is based on segmentation to isolate the kidney from the surrounding anatomical structures via a shape-based segmentation approach using level sets. So the main focus of this paper is the shape based segmentation. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporate the image information with the shape prior in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. The perfusion curves that show the transportation of the contrast agent into the tissue are obtained from segmented kidneys and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction.
Analytical method for MGRF Potts model parameter estimation

This paper proposes a new analytical method for estimating parameters of a homogeneous isotropic Potts model with an asymmetric Gibbs potential function. The model is generalized by including both pairwise and triple cliques. The maximum likelihood estimates of the cliques potentials are obtained by a further elaboration of the approximate analytical estimator proposed in. Experiments with synthetic textures have shown that our potential estimates are more accurate and practicable than their counterparts obtained with classical methods.
Cooperative stereo: combining edge- and area-based stereo

For 3D reconstruction of objects, edge-based stereo techniques use geometric constraints of edges extracted from stereo images taken by a fixed, known camera configuration. The universal assumption made by most edge-based stereo algorithms is that the extracted edges are fixed, corresponding to the same feature in the 3D space. However, many objects in practice contain both fixed and smooth edges, known as occluding contours. Occluding contour edges are attributed to depth discontinuities and are view-dependent, resulting in poor reconstruction. We propose a method that combines reconstruction from an edge-based technique with a refinement process derived from area-based stereo. Using geometric constraints of straight-line segments, this edge-based technique extracts and reconstructs 3D line segments. Tracking the 3D segments by reprojecting them into images at new views, we acquire new matches with area-based techniques. These matches, no longer on the occluding contour, are used to refine the original 3D points.
On the Extraction of Curve Skeletons using Gradient Vector Flow

In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.
A Global-to-Local 2D Shape Registration in Implicit Spaces using Level Sets

This work deals with the problem of shape registration. Broadly speaking, the problem is that of establishing point-wise correspondences in two different shapes of arbitrary dimension and topology. This problem is a fundamental component in numerous image and vision applications. We propose a variational framework for a dense global-to-local 2D shape registration. Affine transformations are accounted for using vector distance functions. Based on this representation, a dissimilarity measure between the two shapes is minimized to recover the global matching parameters. The local coordinate transformation between the two shapes is explicitly estimated by solving a regularized non-linear PDE-based motion model. Various experimental results are presented and discussed to show the potential of the proposed framework with a finite element (FE)-based validation of its performance.
Accurate Tracking of Monotonically Advancing Fronts

A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.
Face recognition at-a-distance using texture and sparse-stereo reconstruction

We propose a framework for face recognition at a distance based on texture and sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. 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. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The local patches around the landmark points are also well-suited for Gabor-based and LBP-based 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 shape-based segmentation approach: an improved technique using level sets

We propose a novel approach for shape-based segmentation based on a specially designed level set function format. This format permits us to better control the process of object 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 partial differential equations (PDE's). Also it is suitable for multidimensional data and computationally efficient. Results of extracting the 2D star fish and the brain ventricles in 3D demonstrate the efficiency of the approach
Robust skeletonization using the fast marching method

We have recently developed a level set based-framework for computing medial curves or curve skeletons CS for arbitrary 2D shapes as well as tubular and articulated 3D objects. The proposed framework is robust, fully automatic, computationally efficient, and produces curve skeletons that are connected, centered, thin, and less sensitive to boundary noise. In this paper, we introduce two improvements to the framework in order to enhance its performance in terms of stability and topology preserving.