nils-herrmann/clean_scirepeval huggingface dataset:
Expressive Maps for 3D Facial Expression Recognition

We present a semi-automatic 3D Facial Expression Recognition system based on geometric facial information. In this approach, the 3D facial meshes are first fitted to an Annotated Face Model (AFM). Then, the Expressive Maps are computed, which indicate the parts of the face that are most expressive according to a particular geometric feature (e.g., vertex coordinates, normals, and local curvature). The Expressive Maps provide a way to analyze the geometric features in terms of their discriminative information and their distribution along the face and allow the reduction of the dimensionality of the input space to 2:5% of the original size. Using the selected features a simple linear classifier was trained and yielded a very competitive average recognition rate of 90:4% when evaluated using ten-fold cross validation on the publicly available BU-3DFE database.
Evaluation of 3D Face Recognition in the presence of facial expressions: an Annotated Deformable Model approach

From a user’s perspective, face recognition is one of the most desirable biometrics, due to its non-intrusive nature; however, variables such as face expression tend to severely affect recognition rates. We have applied to this problem our previous work on elastically adaptive deformable models to obtain parametric representations of the geometry of selected localized face areas using an annotated face model. We then use wavelet analysis to extract a compact biometric signature, thus allowing us to perform rapid comparisons on either a global or a per area basis. To evaluate the performance of our algorithm, we have conducted experiments using data from the Face Recognition Grand Challenge data corpus, the largest and most established data corpus for face recognition currently available. Our results indicate that our algorithm exhibits high levels of accuracy and robustness, and is not gender biased. In addition, it is minimally affected by facial expressions.
Image segmentation based on fuzzy connectedness using dynamic weights

Traditional segmentation techniques do not quite meet the challenges posed by inherently fuzzy medical images. Image segmentation based on fuzzy connectedness addresses this problem by attempting to capture both closeness, based on characteristic intensity, and "hanging togetherness," based on intensity homogeneity, of image elements to the target object. This paper presents a modification and extension of previously published image segmentation algorithms based on fuzzy connectedness, which is computed as a linear combination of an object-feature-based and a homogeneity-based component using fixed weights. We provide a method, called fuzzy connectedness using dynamic weights (DyW), to introduce directional sensitivity to the homogeneity-based component and to dynamically adjust the linear weights in the functional form of fuzzy connectedness. Dynamic computation of the weights relieves the user of the exhaustive search process to find the best combination of weights suited to a particular application. This is critical in applications such as analysis of cardiac cine magnetic resonance (MR) images, where the optimal combination of affinity component weights can vary for each slice, each phase, and each subject, in spite of data being acquired from the same MR scanner with identical protocols. We present selected results of applying DyW to segment phantom images and actual MR, computed tomography, and infrared data. The accuracy of DyW is assessed by comparing it to two different formulations of fuzzy connectedness. Our method consistently achieves accuracy of more than 99.15% for a range of image complexities: contrast 5%-65%, noise-to-contrast ratio of 6%-18%, and bias field of four types with maximum gain factor of up to 10%.
UR3D-C: Linear dimensionality reduction for efficient 3D face recognition

We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.
Automatic segmentation of the diaphragm in non-contrast CT images

The diaphragm is a thin double-domed muscle that separates the thoracic and abdominal cavities. An accurate delineation of the diaphragm surface will be useful in providing a good region of interest for segmentation problems pertaining to the thoracic and abdominal cavities. In this paper, we present a fully automatic 3D graph-based method for the segmentation of the diaphragm in non-contrast CT data. In particular, we reformulate the diaphragm segmentation problem as an optimal surface segmentation problem in a volumetric graph. Comparison of the results obtained using our method with manual segmentations performed by an expert on non-contrast cardiac CT scans of 7 randomly selected patients indicated an overlap of 94.20 ± 0.01%.
Joint prototype and metric learning for set-to-set matching: Application to biometrics

In this paper, we focus on the problem of image set classification. Since existing methods utilize all available samples to model each image set, the corresponding time and storage requirements are high. Such methods are also susceptible to outliers. To address these challenges, we propose a method that jointly learns prototypes and a Mahalanobis distance. The prototypes learned represent the gallery image sets using fewer samples, while the classification accuracy is maintained or improved. The distance learned ensures that the notion of similarity between sets of images is reflected more accurately. Specifically, each gallery set is modeled as a hull spanned by the learned prototypes. The prototypes and distance metric are alternately updated using an iterative scheme. Experimental results using the YouTube Face, ETH-80, and Cambridge Hand Gesture datasets illustrate the improvements obtained.
3D-aided profile-based face recognition

The silhouette of the face profile is a well-known biometric that is already in use in Face Recognition research. One of the challenges for successful employment of this biometric is the sensitivity of its geometry to face rotation. In this paper, we introduce a new method that improves robustness to rotation. We achieve this by exploring the feature space of profiles under various rotations with the aid of a 3D face model. Based on fiducial points on the profile silhouette, we extract a set of rotation-, translation- and scale-invariant features which are used to design and train a hierarchical pose-identity classifier. In our experiments the classifier is used for the identification of a driver using his/her side-view image. We present our results on a publicly available database.
Facial 3D model registration under occlusions with sensiblepoints-based reinforced hypothesis refinement

Registering a 3D facial model to a 2D image under occlusion is difficult. First, not all of the detected facial landmarks are accurate under occlusions. Second, the number of reliable landmarks may not be enough to constrain the problem. We propose a method to synthesize additional points (Sensible Points) to create pose hypotheses. The visual clues extracted from the fiducial points, non-fiducial points, and facial contour are jointly employed to verify the hypotheses. We define a reward function to measure whether the projected dense 3D model is well-aligned with the confidence maps generated by two fully convolutional networks, and use the function to train recurrent policy networks to move the Sensible Points. The same reward function is employed in testing to select the best hypothesis from a candidate pool of hypotheses. Experimentation demonstrates that the proposed approach is very promising in solving the facial model registration problem under occlusion.
Rank-based score normalization for multi-biometric score fusion

The matching score distributions produced by different biometric modalities are heterogeneous. The same is true for the matching score distributions obtained for different probes. Both of these problems can be addressed by score normalization methods that standardize the corresponding distributions. In our previous work we demonstrated that, in the case of multi-sample galleries, the matching score distributions are also heterogeneous between different subsets of matching scores obtained for the same probe. In this paper, we use this result to propose a rank-based score normalization framework for multi-biometric score fusion. Specifically, in addition to normalizing the matching scores produced for each biometric modality independently, we propose to further join them to form a single set. This set is then partitioned to subsets using a rank-based scheme. The theory of stochastic dominance demonstrates that the rank-based scheme imposes the distributions of the subsets to be ordered. Hence, by normalizing the matching scores of each subset independently, better normalized scores are produced. The normalized scores can be fused using any fusion rule. Experimental results using face and iris data from the CASIA-Iris-Distance database demonstrate the improvements obtained.
Segmentation of zebrafish embryonic images using a geometric atlas deformation

Transgenic zebrafish expressing fluorescent proteins in specific tissues or organs are promising models for studies of normal developmental processes, or perturbations of these. However, for widespread use, reliable quantification of the observed effects is necessary. Therefore, accurate and automatic analysis of images obtained by fluorescent microscopy and depicting zebrafish embryos becomes crucial. In this paper, a segmentation approach for such images is proposed. The segmentation is achieved by fitting a species-specific 2D atlas to the zebrafish data depicted in the images. Experiments performed in a set of 50 images have provided promising results.
Towards robust structure-based enhancement and horizon picking in 3-D seismic data

We present a novel structure-enhancing adaptive filter guided by features derived from the gradient structure tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for initializing an automatic horizon picking algorithm. In addition, our algorithm takes seismic attributes into consideration to reduce the possibilities of false horizon generation and fault crossing. Comparative experimental results are presented to highlight the potential of our approach.
Towards fitting a 3D dense facial model to a 2D image: A landmark-free approach

Head pose estimation helps to align a 3D face model to a 2D image, which is critical to research requiring dense 2D-to-2D or 3D-to-2D correspondence. Traditional pose estimation relies strongly on the accuracy of landmarks, so it is sensitive to missing or incorrect landmarks. In this paper, we propose a landmark-free approach to estimate the pose projection matrix. The method can be used to estimate this matrix in unconstrained scenarios and we demonstrate its effectiveness through multiple head pose estimation experiments.
Comparative evaluation of wavelet-based super-resolution from video for face recognition at a distance

Face recognition is a challenging problem, especially when low resolution images or image sequences are used for the task. Many methods have been proposed that can combine multiple low resolution images to realize a higher resolution or super-resolved image. Nonetheless, their utility and limitations for use in face recognition are not well understood. In this paper, we present a quantitative and comparative evaluation of wavelet transform based methods for image super-resolution. We evaluate different basis functions, varying levels of decomposition, and multiple methods for coefficient fusion to maximize the benefit of the super-resolved image for the task of face recognition. We have used a Discrete Wavelet Transform and the shift-invariant Dual-Tree Complex Wavelet Transform. Results are reported across both manually generated datasets and data from a surveillance system.
Addressing the illumination challenge in two-dimensional face recognition: a survey

Uncontrolled illumination is one of the most widely researched and most encountered face recognition challenges in recent years. In this study, the authors propose the division of algorithms into two categories: (i) relighting and (ii) unlighting. Relighting methods try to match the probe's illumination conditions using a subset of representative gallery images, while unlighting methods seek to suppress the variations. A total of 64 state-of-the-art methods are summarised and categorised in each of the groups. To make this work concise and easy to follow, they restricted themselves to selected conferences/journals and they limited the number of approaches reviewed. Also, eight past state-of-the-art approaches are used in both identification and verification experiments. However, only significant reported results from all methods were compared and organised in tables. The author's main objective is not to provide an exhaustive analysis of each category, but to present a collection of papers that can be useful in identifying research directions. Results indicate that unlighting methods are a better and a practical solution to address illumination challenges.
Performance Evaluation of Abdominal Fat Burden Quantification in CT

Abdominal fat accumulation is an important cardiovascular risk factor. In clinical practice, delineation of subcutaneous and visceral fat is performed manually by an expert. This procedure is labor intensive, time consuming, and subject to inter- and intra-observer variability. In this paper, we present an extension of our previous work on automatic fat burden quantification and classification. Our improved method automatically differentiates abdominal fat into subcutaneous and visceral fat components and removes equipment-related artifacts. We evaluated the performance of our method using data from 40 subjects with very encouraging results
Towards 3D-aided profile-based face recognition

In this paper, we present a fully automatic system for face recognition based on a silhouette of the face profile. Previous research has demonstrated the high discriminative potential of this biometric. However, for the successful employment of this characteristic one is confronted with many challenges, such as the sensitivity of a profile's geometry to face rotation and the difficulty of accurate profile extraction from images. We propose to explore the feature space of profiles under various rotations with the aid of a 3D face model. In the enrollment mode, 3D data of subjects are acquired and used to create profiles under different rotations. The features extracted from these profiles are used to train a classifier. In the identification mode, the profiles are extracted from side view images using a modified Active Shape Model approach. We validate the accuracy of the extractor and the robustness of classification algorithms using data from a publicly available database.
Localization and Segmentation of Left Ventricle in Cardiac Cine-MR Images

Accurate delineation of the left ventricular myocardial boundaries on cardiac cine magnetic resonance (MR) images is essential for volumetric and functional cardiac analysis. Automated myocardial contour delineation often suffers from misalignment of slices, nonuniform coil sensitivity, blood-flow-related inter- and intraslice intensity inhomogeneities, blurring due to motion, partial voluming, and a need to circumscribe the papillary muscles and the trabeculae. In this paper, we propose a novel method for data-driven localization and segmentation of the left ventricle in the cine-MR images toward automated computation of ejection fraction (EF). Our hybrid segmentation method combines intensity- and texture-based fuzzy affinity maps obtained from a novel multiclass, multifeature fuzzy connectedness method with dynamic-programming-based boundary detection to delineate the myocardial contours. Bland-Altman analysis indicates that the mean biases of the end-diastolic volume, end-systolic volume, and EF estimates of our method are comparable to the interobserver variability when compared with the annotations from two experts.
Predicting privileged information for height estimation

In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., ϵ-SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the ϵ-SVR+ algorithm and report results for different genders and quartiles of humans.
A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.
Accurate Landmarking of Three-Dimensional Facial Data in the Presence of Facial Expressions and Occlusions Using a Three-Dimensional Statistical Facial Feature Model

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.
Automated characterization of gene expression patterns with an atlas of the mouse brain

A spatio-temporal map of gene activity in the brain would be an important contribution to the understanding of brain development, disease, and function. Such a resource is now possible using high-throughput in situ hybridization, a method for transcriptome-wide acquisition of cellular resolution gene expression patterns in serial tissue sections. However, querying an enormous quantity of image data requires computational methods for describing and organizing gene expression patterns in a consistent manner. In addressing this, we have developed procedures for automated annotation of gene expression patterns in the postnatal mouse brain.
Pose-robust face signature for multi-view face recognition

Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue. We propose a novel framework for multi-view face recognition based on extracting and matching pose-robust face signatures from 2D images. Specifically, we propose an efficient method for monocular 3D face reconstruction, which is used to lift the 2D facial appearance to a canonical texture space and estimate the self-occlusion. On the lifted facial texture we then extract various local features, which are further enhanced by the occlusion encodings computed on the self-occlusion mask, resulting in a pose-robust face signature, a novel feature representation of the original 2D facial image. Extensive experiments on two public datasets demonstrate that our method not only simplifies the matching of multi-view 2D facial images by circumventing the requirement for pose-adaptive classifiers, but also achieves superior performance.
An Explicit Shape-Constrained MRF-Based Contour Evolution Method for 2-D Medical Image Segmentation

Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired segmentation result. While segmenting organs in medical images, which is the topic of this paper, a significant amount of prior knowledge about the shape, appearance, and location of the organs is available that can be used to constrain the solution space of the segmentation problem. Among the various types of prior information, the incorporation of prior information about shape, in particular, is very challenging. In this paper, we present an explicit shape-constrained MAP-MRF-based contour evolution method for the segmentation of organs in 2-D medical images. Specifically, we represent the segmentation contour explicitly as a chain of control points. We then cast the segmentation problem as a contour evolution problem, wherein the evolution of the contour is performed by iteratively solving a MAP-MRF labeling problem. The evolution of the contour is governed by three types of prior information, namely: (i) appearance prior, (ii) boundary-edgeness prior, and (iii) shape prior, each of which is incorporated as clique potentials into the MAP-MRF problem. We use the master-slave dual decomposition framework to solve the MAP-MRF labeling problem in each iteration. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography data.
A framework for building multi-tissue atlas of zebrafish embryo

This paper presents a new framework for building a representative atlas of multiple tissue types for zebrafish embryos. In our framework, multiple normalization strategies were considered and evaluated. Moreover, to keep the resulting atlas representative, outlier samples were discovered and removed automatically. Our quality measure for multiple tissue channels was used for selecting the best resulting atlas and for driving the normalization process (to reduce the number of experiments). We applied our framework to computing a 2D atlas of five tissue types (vasculature, sensory neuronal precursors, somites, motor neurons, and yolk) from a population of 40 zebrafish embryos.