nils-herrmann/clean_scirepeval huggingface dataset:
Minimizing Illumination Differences for 3D to 2D Face Recognition Using Lighting Maps

Asymmetric 3D to 2D face recognition has gained attention from the research community since the real-world application of 3D to 3D recognition is limited by the unavailability of inexpensive 3D data acquisition equipment. A 3D to 2D face recognition system explicitly relies on 3D facial data to account for uncontrolled image conditions related to head pose or illumination. We build upon such a system, which matches relit gallery textures with pose-normalized probe images, using the gallery facial meshes. The relighting process, however, is based on an assumption of indoor lighting conditions and limits recognition performance on outdoor images. In this paper, we propose a novel method for minimizing illumination difference by unlighting a 3D face texture via albedo estimation using lighting maps. The algorithm is evaluated on challenging databases (UHDB30, UHDB11, FRGC v2.0) with drastic lighting and pose variations. The experimental results demonstrate the robustness of our method for estimating the albedo from both indoor and outdoor captured images, and the effectiveness and efficiency for illumination normalization in face recognition.
Towards cardiovascular risk stratification using imaging data

In spite of the advancement and proliferation of cardiovascular imaging data, the rate of deaths due to unpredicted heart attack remains high. Thus, it becomes imperative to develop novel computational tools to mine quantitative parameters from imaging data for early detection and diagnosis of asymptomatic cardiovascular disease. In this paper, we present our progress towards developing a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. Specifically, we present computational methods developed for the detection of coronary calcification and segmentation of thoracic aorta in non-contrast cardiac computed tomography, and detection of neovessels in plaques in intravascular ultrasound imaging data.
Frames-Based Denoising in 3D Confocal Microscopy Imaging

In this paper, we propose a novel denoising method for 3D confocal microscopy data based on robust edge detection. Our approach relies on the construction of a non-separable frame system in 3D that incorporates the Sobel operator in dual spatial directions. This multidirectional set of digital filters is capable of robustly detecting edge information by ensemble thresholding of the filtered data. We demonstrate the application of our method to both synthetic and real confocal microscopy data by comparing it to denoising methods based on separable 3D wavelets and 3D median filtering, and report very encouraging results
Improved face recognition using super-resolution

Face recognition is a challenging task, especially when low-resolution images or image sequences are used. A decrease in image resolution typically results in loss of facial component details leading to a decrease in recognition rates. In this paper, we propose a new method for super-resolution by first learning the high-frequency components in the facial data that can be added to a low-resolution input image to create a super-resolved image. Our method is different from conventional methods as we estimate the high-frequency components, that are not used in other methods, to reconstruct a higher-resolution image, rather than studying the direct relationship between the high- and low-resolution images. Quantitative and qualitative results are reported for both synthetic and surveillance facial image databases.
Automatic delineation of the inner thoracic region in non-contrast CT data

The inner thoracic region consists of several important anatomical structures and an accurate delineation of this region is an essential step for various biomedical image analysis applications. In this paper, we present a fully automatic graph-based method for the delineation of the inner thoracic region in non-contrast cardiac CT data. In particular, we reformulate the problem of delineating the inner thoracic region as an optimal surface segmentation problem, the solution to which is obtained by computing the minimum-cost closed set in a node-weighted directed graph. Comparing the results obtained using our method with manual segmentations performed by an expert on non-contrast cardiac CT scans of 20 randomly selected patients indicated an overlap of 99.1 ± 0.2%.
[POSTER] Holographic iRay: Exploring Augmentation for Medical Applications

A Holographic iRay prototype focusing on medical augmented reality is presented. The prototype is built using Microsoft HoloLens and Unity engine based on a previous iRay system built for iPad. A human subject is scanned using magnetic resonance imaging and the torso surface is pre-operatively segmented for 3D registration. The registration is performed using the iterative closest point algorithm between the pre-operative torso surface and the active torso surface mesh provided by the HoloLens spatial mapping and the gaze interaction. A scanning box is visualized at the gazing point to help the user select the targeting area. The pre-operative torso surface and the sampled active vertices within the scanning box are additionally overlaid in the box as the auxiliary information for guidance. Several simple interactions are designed to control the rendering of the inner organs after the registration. The experimental results demonstrate the potential of the Holographic iRay for medical applications.
Rendering or normalization? An analysis of the 3D-aided pose-invariant face recognition

In spite of recent progress achieved in near-frontal face recognition, the problem of pose variations prevalent in 2D facial images captured in the wild still remains a challenging and unsolved issue. Among existing approaches of pose-invariant face recognition, 3D-aided methods have been demonstrated effective and promising. In this paper, we present an extensive evaluation of two widely adopted frameworks of 3D-aided face recognition in order to compare the state-of-the-art, identify remaining issues, and offer suggestions for future research. Specifically, we compare the pose normalization and the pose synthesis (rendering) based methods in an empirical manner. The database (UHDB31) that we use covers 21 well-controlled pose variations, half of which show a combination of yaw and pitch. Through the experiments, we present the advantages and disadvantages of these two methods to provide solid data for future research in 3D-aided pose-invariant face recognition.
Twins 3D face recognition challenge

Existing 3D face recognition algorithms have achieved high enough performances against public datasets like FRGC v2, that it is difficult to achieve further significant increases in recognition performance. However, the 3D TEC dataset is a more challenging dataset which consists of 3D scans of 107 pairs of twins that were acquired in a single session, with each subject having a scan of a neutral expression and a smiling expression. The combination of factors related to the facial similarity of identical twins and the variation in facial expression makes this a challenging dataset. We conduct experiments using state of the art face recognition algorithms and present the results. Our results indicate that 3D face recognition of identical twins in the presence of varying facial expressions is far from a solved problem, but that good performance is possible.
Automatic segmentation of the left ventricle from dual contrast cardiac MR data

Manual tracing of the blood pool from short axis cine MR images is routinely used to compute ejection fraction (EF) in clinical practice. The manual segmentation process is cumbersome, time consuming, and operator dependent. In this paper, we present an algorithm for the automatic computation of the EF that is based on segmenting the left ventricle by combining the fuzzy connectedness and deformable model frameworks. Our contributions are the following: (1) we automatically estimate a seed point and sample region for the fuzzy connectedness estimates, (2) we extend the fuzzy connectedness method to use adaptive weights for the homogeneity and the gradient energy functions that are computed dynamically, and (3) we extend the hybrid segmentation framework to allow forces from dual contrast and fuzzy connectedness data integrated with shape constraints. We compared our method against manual delineation performed by experienced radiologists on the data from nine asymptomatic volunteers with very encouraging results.
Segmentation of the Thoracic Aorta in Noncontrast Cardiac CT Images

Studies have shown that aortic calcification is associated with cardiovascular disease. In this study, a method for localization, centerline extraction, and segmentation of the thoracic aorta in noncontrast cardiac-computed tomography (CT) images, toward the detection of aortic calcification, is presented. The localization of the right coronary artery ostium slice is formulated as a regression problem whose input variables are obtained from simple intensity features computed from a pyramid representation of the slice. The localization, centerline extraction, and segmentation of the aorta are formulated as optimal path detection problems. Dynamic programming is applied in the Hough space for localizing key center points in the aorta which guide the centerline tracing using a fast marching-based minimal path extraction framework. The input volume is then resampled into a stack of 2-D cross-sectional planes orthogonal to the obtained centerline. Dynamic programming is again applied for the segmentation of the aorta in each slice of the resampled volume. The obtained segmentation is finally mapped back to its original volume space. The performance of the proposed method was assessed on cardiac noncontrast CT scans and promising results were obtained.
Estimating anthropometry and pose from a single image

In this paper, we present a four-step technique for simultaneously estimating a human's anthropometric measurements (up to a scale parameter) and pose from a single image. The user initially selects a set of image points that constitute the projection of selected landmark. Using this information, along with a priori statistical information about the human body, a set of plausible segment length estimates are generated. The third step produces a set of plausible poses based on joint limit constraints using a geometric method. In the fourth step, pose and anthropometric measurements are obtained by minimizing an appropriate cost function subject to the associated constraints. The novelty of our approach is the use of anthropometric statistics to constrain the estimation process that allows the simultaneous estimation of both anthropometry and pose. We demonstrate the accuracy, advantages and limitations of our method for various classes of both synthetic and real input data.
3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being “discriminative” or “nondiscriminative” for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.
Kernel active contour

Level sets and graph cuts are two state-of-the-art image segmentation methods in use today. The two methods are apparently different from each other not only because they originate from different theory foundations but also because they employ image information in different ways — level sets typically use image information in a point-wise way, whereas graph cuts use image information in a pairwise way. In this paper, we derive an equivalence relationship between the two methods through kernel technology. In particular, we show that the kernelization of the Chan-Vese (CV) functional — a functional widely used in the level set community — is exactly the energy optimized in the average association — a well-known graph cut criterion. We refer to the level sets method using the kernelized version of the CV functional as kernel active contour. The kernel active contour has computational complexity O(n2) due to the involved kernel technology. We propose a fast implementation for kernel active contour with computational complexity only O(n) using random projection. The kernel active contour is evaluated on synthetic and real images and compared with several existing level set and graph cut methods for image segmentation.
Towards automatic analysis of DNA microarrays

In this paper we present a computational framework that provides the automatic analysis of spotted DNA microarray image data. The challenges are in providing an accurate representation of microarray hybridization observations while minimizing user interaction. To obtain this, we need to segment the observation data and subsequent correction for true hybridization level measurements must be accomplished against the backdrop of signal noise, background signal variation, and spatial non-uniformity in the array layout. With the requirements of automation and accuracy, an approach based on data-driven denoising, array addressing, background estimation, and spot segmentation was developed We proceeded to validate our approach on synthetic data as well as the publicly available raw and analyzed microarray data from the published Stanford yeast cell cycle analysis project. Spot mean and total intensities were examined as well as spot background estimates. By minimizing the user role, a main bottleneck in microarray data analysis is removed, allowing for more immediate analysis of large observation data sets. Our implementation has proven to be relatively fast, and the results of our approach have been encouraging.
Image-Based Frame Gating of IVUS Pullbacks: A Surrogate for ECG

Intravascular ultrasound (IVUS) is a catheter-based modality which is used to produce high-resolution, cross-sectional images of the interior of blood vessels. By capturing 2-D IVUS images continually while translating the catheter, a volumetric image of a vessel may be digitally reconstructed. To improve the quality of these volumes, electrocardiogram (ECG)-based frame gating is often applied to alleviate motion artifacts caused by the beating heart. However, there are several issues surrounding the use of ECG signals which make their use for this purpose potentially suboptimal. We introduce a method which gates pullback sequences by examining the imaging data alone, without requiring synchronous ECG, and guarantees that frames will be collected at those points in time when the heart is maximally motionless (i.e., regardless of the fraction of cardiac phase associated with those points). We compare the results of our method and of ECG on pullbacks captured in vivo in swine.
Elastically Adaptive Deformable Models

We present a technique for the automatic adaptation of a deformable model's elastic parameters within a Kalman filter framework for shape estimation applications. The novelty of the technique is that the model's elastic parameters are not constant, but spatio-temporally varying. The variation of the elastic parameters depends on the distance of the model from the data and the rate of change of this distance. Each pass of the algorithm uses physics-based modeling techniques to iteratively adjust both the geometric and the elastic degrees of freedom of the model in response to forces that are computed from the discrepancy between the model and the data. By augmenting the state equations of an extended Kalman filter to incorporate these additional variables, we are able to significantly improve the quality of the shape estimation. Therefore, the model's elastic parameters are always initialized to the same value and they are subsequently modified depending on the data and the noise distribution. We present results demonstrating the effectiveness of our method for both two-dimensional and three-dimensional data.
A simplified error model for height estimation using a single camera

Intelligent solutions for video surveillance require reliable object features for robust tracking. Surveillance cameras are generally low resolution and hence impose restrictions on the ability to identify sufficient features and descriptors for effective tracking. In such applications, reliable estimate of the object height from a single uncalibrated camera can prove to be a very useful feature. Several approaches have been developed to obtain height information of an object from image sequences, but are limited in their utility due to errors arising from varying image and object properties. In this paper, we present a simplified error model dependent on the location of the object in the image and the estimated camera height. Further, the developed model is used to obtain reliable height estimates of objects in different image sequences and the result is compared to estimates obtained without the use of the model. Significant reduction in error associated with the measurement is observed while making the estimate independent of camera and object variations. We then demonstrate its utility in object tracking.
Neck localization and geometry quantification of intracranial aneurysms

We present an approach for accurate localization of the neck of intracranial aneurysms and quantification of their geometry that is useful for their treatment through endovascular embolization. In particular, we first obtain a vessel segmentation using a topology-preserving level set method and extract the surface of the segmented vessel. We then separate the aneurysm from the parent vessels and localize its neck by formulating the aneurysm segmentation problem as an s-t minimum cut problem. Finally, we estimate clinically relevant geometric parameters of the aneurysm. The results indicate that there is good agreement between the measurements obtained by the proposed approach and two independent manual sets of measurements obtained by two experienced interventional neuroradiologists.
m-HDAF multiresolution deformable models

In this paper, we construct a new class of deformable models using new orthogonal wavelets, named modified Hermite distributed approximating functional (m-HDAF) wavelets. The scaling functions of this new family are symmetric and the corresponding wavelets optimize their smoothness for a given number of vanishing moments. In addition, we embed these multiresolution deformable models to the physics-based deformable model framework and use them for fitting 2D and 3D data. We have performed a number of experiments with both synthetic and real data with very encouraging results.
Can We Do Better in Unimodal Biometric Systems? A Rank-Based Score Normalization Framework

Biometric systems use score normalization techniques and fusion rules to improve recognition performance. The large amount of research on score fusion for multimodal systems raises an important question: can we utilize the available information from unimodal systems more effectively? In this paper, we present a rank-based score normalization framework that addresses this problem. Specifically, our approach consists of three algorithms: 1) partition the matching scores into subsets and normalize each subset independently; 2) utilize the gallery versus gallery matching scores matrix (i.e., gallery-based information); and 3) dynamically augment the gallery in an online fashion. We invoke the theory of stochastic dominance along with results of prior research to demonstrate when and why our approach yields increased performance. Our framework: 1) can be used in conjunction with any score normalization technique and any fusion rule; 2) is amenable to parallel programming; and 3) is suitable for both verification and open-set identification. To assess the performance of our framework, we use the UHDB11 and FRGC v2 face datasets. Specifically, the statistical hypothesis tests performed illustrate that the performance of our framework improves as we increase the number of samples per subject. Furthermore, the corresponding statistical analysis demonstrates that increased separation between match and nonmatch scores is obtained for each probe. Besides the benefits and limitations highlighted by our experimental evaluation, results under optimal and pessimal conditions are also presented to offer better insights.
Multimodal face recognition: combination of geometry with physiological information

It is becoming increasingly important to be able to credential and identify authorized personnel at key points of entry. Such identity management systems commonly employ biometric identifiers. In this paper, we present a novel multimodal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. Data from multiple cameras is used to construct a three-dimensional mesh representing the face and a facial thermal texture map. An annotated face model with explicit two-dimensional parameterization (UV) is then fitted to this data to construct: 1) a three-channel UV deformation image encoding geometry, and 2) a one-channel UV vasculature image encoding facial vasculature. Recognition is accomplished by comparing: 1) the parametric deformation images, 2) the parametric vasculature images, and 3) the visible spectrum texture maps. The novelty of our work lies in the use of deformation images and physiological information as means for comparison. We have performed extensive tests on the Face Recognition Grand Challenge v1.0 dataset and on our own multimodal database with very encouraging results.
Robust interactive image segmentation via iterative refinement

Image segmentation with user inputs gets more and more popular in recent years and always performs better compared with automatic methods. However, existing interactive image segmentation methods still might fail if the image contains messy textures, or the user inputs are sparse or at inappropriate locations. In this paper, we propose a novel iterative refinement framework which leads to robust segmentation performance even with sparse and improper input strokes. Specifically, a geodesic distance based energy is introduced and combined with convex active contour model, and an iterative seeds refinement technique is put forward to handle the sparse input problem. Extensive experiments using real world images, and segmentation benchmark dataset show that our proposed method has superior performance compared with representative state-of-the-art methods.
Site selection for the Tianlai experiment proceedings of the XXXIst URSI general assembly and scientific symposium to be held in Beijing, China (CIE), August 17–23, 2014

The Tianlai project plans to build a compact cylinder array to map the 21cm emission from redshift 0 to 3, corresponding to frequency 350-1420MHz. The full array is designed to have a size of about 100m-100m in dimension. A site with minimum radio frequency interference (RFI) and convenience of logisitics support is desired. This project is also constrained by its low budget, so attention have to be paid to reduce the cost for the construction and operation. We have conducted site surveys in many places in China, obtained radio spectrum measurement at more than a hundred potential site locations. This paper describes the site selection and survey method. We have tentatively selected a site in Hongliuxia, Balikun County, Xinjiang Autonomous Region, which have very low RFI and is potentially good for other low frequency radio experiments.