nils-herrmann/clean_scirepeval huggingface dataset:
Stochastic segmentation of blood vessels from time-of-flight

In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data across the volume, while the high level process characterizes the statistical dependence among neighboring voxels. 3D Markov random field (MRF) has been employed to model the high level process, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE). Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several TOF datasets. Experimental results showed that the proposed model is capable of delineating vessels down to 3 voxel diameters.
A Statistical Model Combining Shape and Spherical Harmonics for Face Reconstruction and Recognition

We describe a face reconstruction framework based on a statistical model that combines shape (2D and height maps), appearance (albedo), and spherical harmonics projection (SHP) information. The framework takes a 2D frontal face image under arbitrary illumination as input and outputs the estimated 3D shape and appearance. Face identification is performed using the shape and albedo coefficients of the fitting process, as well as the actual shape/texture reconstructions. Results indicate decent face reconstructions and a perfect recognition rate for the frontal images of the Extended Yale Database B.
3D automated colon segmentation for efficient polyp detection

With polyps being the main cause of colorectal cancer, accurate colon segmentation is a crucial step for polyp detection in a virtual colonoscopy system. This paper presents a fully automated segmentation framework for the colon which is based on convex formulation of the active contour model. Our approach is tested on 7 sets where the results are further validated for polyp detection. Results show the efficiency of the framework with an overall accuracy of 99%, and high sensitivity of polyp detection.
A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets

A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule “head.” The embedding process takes into consideration the image intensity as well as prior shape information. A nonparametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.
Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images

Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.
Facial action units detection under pose variations using deep regions learning

A set of facial Action Units (AUs) is activated due to the movement of facial muscles in response to a person's internal emotion. The activated facial action units appear around sparse regions on the face e.g., the mouth and eyes. This group occurrence of AUs reveals the semantic relationships among them. These relationships should be considered in the detection process of AUs. Therefore, we propose an approach that learns these semantic relationships using a multi-label deep learning architecture. The sparse patches of the AUs are used in the proposed approach instead of the whole facial region. To handle these sparse patches, we propose a region-based network instead of the well-known convolutional network and the locally connected network. Moreover, unlike the current approaches, which define facial regions using a uniform grid, the proposed region-based architecture, define patches around facial landmarks. This overcomes the region displacement problem of the uniform grid. Usually, the AUs classification suffers from the high skewness factor problem. To overcome this problem, we use a weighted loss function. We conduct our experiments on two standard benchmarks: BP4D and FERA17. f1 score, the area under the ROC (AROC) and the area under the precision-recall curve (APR) are used as performance measures. Compared to the standard convolutional layer, the proposed patch-based layer is more effective in capturing the required structural features of the face and learns the correlations among AUs under pose variations. Also, it has been shown that the proposed approach outperforms the state-of-the-art methods.
A general-purpose platform for 3-D reconstruction from sequence of images

A number of approaches have been proposed in the literature for reconstruction of 3-D objects from sequence of images. Yet, very few studies have been reported on the quantification/validation of the accuracy of these reconstructions. In addition, no design has been reported for a generic vision platform that can allow various modality imaging. The purpose of this paper is two folds: 1) propose a vision platform that lend itself for acquisition of calibrated sequence of images, and concurrently obtain a direct 3-D reconstruction by laser scanning; and 2) develop and implement different approaches for 3-D reconstructions from sequence of images. 3-D reconstructions will be evaluated against the 3-D scanning generated from a laser scanner. Validation of the reconstructions is made by pairwise comparison with the 3-D scanning results. Preliminary studies with the proposed vision platform show a good promise for its use in the validation of various 3-D reconstructions, registration and data fusion.
A Variational Approach for Shapes Registration Using Vector Maps

The main focus of this paper is the shape representation and registration using vector level set functions. This powerful representation is more flexible than conventional signed distance level sets since it enables us to control the shapes registration process by using more complicated transforms. Based on this model, a variational frame work is proposed for the rigid and non-rigid registration of shapes which can be extended to the higher dimensional case in a straightforward manner and also does not need any point correspondences. The optimization criterion presented can handle efficiently both the rigid and the non-rigid operations together. Experimental results in 2D for real shapes registration are discussed to show the efficiency of the approach with small and large global deformations of shapes.
Coding of 3D objects using surface signatures

This paper presents a new concept for 3D coding of freeform surfaces. The proposed coding technique uses the surface signature representation scheme. This representation scheme captures the 3D curvature information of any freeform surface and encodes it into a 2D image corresponding to a certain point on the surface. This image is unique for this point and is independent of the object translation or orientation in space. For this reason this image is called "surface point signature" (SPS). Using SPS in 3D coding has many applications in 3D compression, 3D pose estimation and in 3D object recognition.
Dynamic Weighting of Facial Features for Automatic Pose-Invariant Face Recognition

This paper proposes an automatic pose-invariant face recognition system. In our approach, we consider the texture information around the facial features to compute the similarity measure between the probe and gallery images. The weight of each facial feature is dynamically estimated based on its robustness to the pose of the captured image. An approach to extract the 9 facial features used to initialize the Active shape model is proposed. The approach is not dependent on the texture around the facial feature only but incorporates the information obtained about the facial feature relations. Our face recognition system is tested on common datasets in pose evaluation CMU-PIE and FERET. The results show out-performance of the state of the art automatic face recognition systems.
Image content-based active sensor planning for a mobile trinocular active vision system

In this paper, we present a sensor planning approach for a mobile trinocular active vision system. At the stationary state (i.e., no motion) the sensor planning system calculates the generalized cameras' parameters (i.e., translational distance from the center, zoom, focus and vergence) using deterministic geometric specifications of both the sensors and the objects in their field of view. Some of these geometric parameters are difficult to be predetermined for the mobile system operation. In this paper, a new sensor planning approach, based on processing the content of the captured images, is proposed. The approach uses a combination of a closed-form solution for the translation between the three cameras, the vergence angle of the cameras as well as zoom and focus settings with the results of the correspondences between the acquired images and a predefined target object(s) obtained using the SIFT algorithm. We demonstrate the accuracy of the new approach using practical experiments.
CSIFT: A SIFT Descriptor with Color Invariant Characteristics

SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.
Heat Kernels for Non-Rigid Shape Retrieval: Sparse Representation and Efficient Classification

One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.
Adaptive object identification and recognition using neural networks and surface signatures

The paper introduces an adaptive technique for 3D object identification and recognition in 3D scanned scenes. This technique uses neural learning of the 3D free-form surface representation of the object in study. This representation scheme captures the 3D curvature information of any free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image represents a "surface signature" because it is unique for this point and is independent of the object translation or orientation in space.
Local estimation of Gaussian-based edge enhancement filters using Fourier analysis

An empirical measure for the selection of the edge-enhancement Gaussian filter is developed. The Gaussian filter is specified by its standard deviation sigma ; the filter's spatial support is a function of sigma . An estimation procedure for sigma using Fourier analysis is described. The measure is easy to implement and is based totally on the image at hand. Experimental results suggest that this measure can be used as an aid in deciding the Gaussian filter's spatial support, which is needed to enhance the edges. Other equivalent bandwidth definitions can be used to obtain a measure of the frequency spread in the smoothed image (e.g., the mean-square bandwidth).<<ETX>>
Model-based shape recovery from single images of general and unknown lighting

We present a new statistical shape-from-shading framework for images of unknown illumination. The object (e.g., face) to be reconstructed is described by a parametric model. To deal with arbitrary illumination, the framework makes use of recent results that general lighting can be expressed using low-order spherical harmonics for convex Lambertian objects. The classical shape-from-shading equation is modified according to this framework. Results show accurate shape recovery with respect to ground truth data.
Shape Representation and Registration using Vector Distance Functions

This paper introduces a new method for shape registration 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. The optimization criterion can handle efficiently the estimation of the global registration parameters. A closed form solution is provided to handle an incremental free form deformation model for covering the local deformations. This is an advantage over the gradient descent optimization which is biased towards the initialization and is more time consuming. Results of real shapes registration will be demonstrated to show the efficiency of the proposed approach with small and large global/local deformations.
3-D reconstruction of the human jaw using space carving

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning and surgical simulation. Dentistry requires an accurate 3D representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system constructs a 3D model of the patient's dental occlusion using an intra-oral video camera. The space carving algorithm is used to reconstruct the shape of the human jaw. This algorithm provides more flexibility and eliminates several constraints imposed by other approaches like stereo. The system performance is investigated, and the results show acceptable reconstruction accuracy.
MultiStencils Fast Marching Methods: A Highly Accurate Solution to the Eikonal Equation on Cartesian Domains

A wide range of computer vision applications require an accurate solution of a particular Hamilton-Jacobi (HJ) equation known as the Eikonal equation. In this paper, we propose an improved version of the fast marching method (FMM) that is highly accurate for both 2D and 3D Cartesian domains. The new method is called multistencils 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 upwind condition. The stencils are centered at each grid point and cover its entire nearest neighbors. In 2D space, two stencils cover 8-neighbors of the point, whereas in 3D space, six 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 higher order finite difference schemes. The accuracy of the proposed method over the state-of-the-art FMM-based techniques has been demonstrated through comprehensive numerical experiments.
A new symmetric shape from shading algorithm with an application to 3-D face reconstruction

This paper presents a new symmetric shape from shading (SFS) algorithm where the self-ratio image irradiance equation proposed by Zhao and Chellappa is formulated as a partial differential equation (PDE) with a Dirichlet boundary condition. This PDE is solved using the Lax-Friedrichs sweeping method. The potential of the proposed symmetric SFS algorithm is demonstrated by several experiments on synthetic and real data. As an application, the proposed algorithm is used for 3-D face reconstruction.
On clinical validation of Fly-Over visualization technique for virtual colonoscopy

In this paper, we aim to provide the further evaluation on the recently proposed visualization technique known as Virtual Fly-Over for virtual colonoscopy by using clinical CT colonography datasets. The proposed method mainly consists of two steps: splitting the entire colon anatomy into exactly two halves, and assigning a virtual camera to each half to perform fly-over navigation. We have quantitatively validated the effectiveness of the proposed technique by using 22 real CT colonography datasets. Experimental results have shown that the average surface visibility coverage is 97.49±1.02%.
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance

Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable. In this paper, we propose an analytic formulation for low-dimensional subspace construction in which shading cues lie while preserving the natural structure of an image sample. Using the frequency-space representation of the image irradiance equation, the process of finding such subspace is cast as establishing a relation between its principal components and that of a deterministic set of basis functions, termed as irradiance harmonics. Representing images as matrices further lessen the number of parameters to be estimated to define a bilinear projection which maps the image sample to a lower-dimensional bilinear subspace. Results show significant impact on dimensionality reduction with minimal loss of information as well as robustness against noise.
Segmentation of trabecular bones from Vertebral bodies in volumetric CT spine images

We present a 3D segmentation technique of trabecular (cancellous) bones in CT images of Vertebral bodies (VBs). In order to be used for Bone Mineral Density (BMD) measurements, the cortical and trabecular bones are subsequently segmented using graph cuts method and local volume growing methods separately. In the final step, we measure our segmentation accuracy for each method. Validity was analyzed using ground truths of data sets and the European Spine Phantom (ESP). Preliminary results are very encouraging and a reproducibility of the results was achieved for 16 data sets. The average segmentation error is below 2.0% for both methods.
Can lens distortion be calibrated from an image of a smooth, textureless Lambertian surface?

This paper addresses the problem of lens distortion calibration from a single image of a smooth, textureless surface. 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 lens distortion. The proposed method is easy to use and requires no specific feature extraction from images. Experiments on simulations and real data are reported and compared to well-known techniques.
Modified fuzzy c-mean in medical image segmentation

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov Random Field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.
Frequency-Domain Analysis of the Human Brain for Studies of Autism

Geometric analysis of 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 reduces the pose differences between the CC's models. Then the complex Fourier descriptor analysis is used to extract a feature vector of each CC model. This feature is used to build a criteria of discrimination between the normal and autistic subjects. 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 MRI data sets demonstrates the potential of the proposed approach. Discrimination results will be demonstrated as well to show the efficiency of the discrimination technique.