nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Segmentation of Abdominal Fat from CT Data

Abdominal visceral fat accumulation is one of the most important cardiovascular risk factors. Currently, computed tomography and magnetic resonance images are manually segmented to quantify abdominal fat distribution. The manual delineation of subcutaneous and visceral fat is labor intensive, time consuming, and subject to inter- and intra- observer variability. An automatic segmentation method would eliminate intra- and inter-observer variability and provide more consistent results. In this paper, we present a hierarchical, multi-class, multi-feature, fuzzy affinity-based computational framework for tissue segmentation in medical images. We have applied this framework for automatic segmentation of abdominal fat. An evaluation of the accuracy of our method indicates bias and limits of agreement comparable to the inter-observer variability inherent in manual segmentation.
Illumination alignment using lighting ratio: Application to 3D-2D face recognition

Illumination alignment is accomplished by relighting or unlighting methods that compensate the appearance differences due to variations in illumination conditions between a pair of face images or among a set of face images. In this paper, we propose a lighting ratio based method that avoids parameter tuning process by using an image-specific low-pass filtering. In addition, it includes an optimization step that brings the images into a similar energy level. The proposed method is simple and does not require any prior knowledge of the number, position, type of lighting sources or the 3D face geometry. Experimental results indicate that our method is effective and efficient for illumination-robust face recognition.
Intraclass Retrieval of Nonrigid 3D Objects: Application to Face Recognition

As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at to 10-3 false accept rate. The latest results of our work can be found at
Noninvasive automatic breast volume estimation for post-mastectomy breast reconstructive surgery

Automatic computation of breast volume data is required in post-mastectomy breast reconstructive surgery, where it is very useful to have an estimate of the volume of the tissue to be extracted in advance of the operation. Such an automated process is essential when conducting studies on large patient databases where manual volume estimation is both tedious and subjective. In this paper, we present a noninvasive automatic breast volume estimation method. It employs 3D scanned data of the torso. First, a surface underneath the breast that resembles a 'breast-less' torso is automatically constructed, and then the volume of the breast is estimated as a function of the difference between the new surface and the actual breast. We have performed a number of experiments on both synthetic and real data with very encouraging results.
Regression-based metric learning

Existing distance metric learning methods define an objective function and seek a distance metric (or equivalently a projection) that minimizes it. In this paper, we propose a different approach that illustrates how to formulate distance metric learning as a regression problem. First, the objective function is minimized to learn target representations. Then, a regression method is employed to learn a projection that maps the input to the target representations. This global projection function is the single output of the proposed algorithm. Our contribution is a different perspective on how to train a distance metric learning algorithm. The advantages are: (i) this approach has the potential to simplify the optimization process; and (ii) it allows researchers to leverage the power of existing regression methods and those to be invented. Experimental results on several publicly available datasets illustrate that the proposed framework can learn a distance metric with discriminative properties.
Show me your body: Gender classification from still images

In this work, we investigate the problem of predicting gender from still images using human metrology. Since the values of the anthropometric measurements are difficult to be estimated accurately from state-of-the-art computer vision algorithms, ratios of anthropometric measurements were used as features. Additionally, since several measurements will not be available at test time in a real-life scenario, we opted for the Learning Using Privileged Information (LUPI) paradigm. During training, we used as features, ratios from all the available anthropometric measurements, whereas at test time only ratios of measurable (i.e., observable) quantities were used. We show that by using the LUPI framework, the estimation of soft biometric characteristics such as gender is possible. Gender classification from human metrology is also tested on real images with promising results.
Active privileged learning of human activities from weakly labeled samples

In many human activity recognition systems the size of the unlabeled training data may be significantly large due to expensive human effort required for data annotation. Moreover, the insufficient data collection process from heterogenous sources may cause dissimilarities between training and testing data. To address these limitations, a novel probabilistic approach that combines learning using privileged information (LUPI) and active learning is proposed. A pool-based privileged active learning approach is presented for semi-supervising learning of human activities from multimodal labeled and unlabeled data. Both uncertainty and distance from the decision boundary are used as query inference strategies for selecting an unlabeled observation and querying its label. Experimental results in four publicly available datasets demonstrate that the proposed method can identify complex human activities with high accuracy.
Vision-based animation of digital humans

The paper presents a system for animating customized virtual humans using motion parameters estimated from multi-view image sequences. The advantage of the method is that the subject does not have to wear markers or other devices. The authors offer a detailed analysis of the singularities that might arise during tracking humans using multiple cameras and provide an analysis of the criteria that allow the ordering of the cameras that provide the most information for tracking. They demonstrate their system using image sequences requiring the recovery of complex three-dimensional motions in the presence of significant occlusion, and present a quantitative analysis of the accuracy of the estimated motion parameters.
Pose invariant facial component-landmark detection

Facial landmark detection has proved to be a very challenging task in biometrics due to the numerous sources of variation. In this work, we present an algorithm for robust detection of facial component-landmarks. Specifically, we address the variation due to extreme pose and illumination. To achieve robust detection for extreme poses, we use a set of independent pose and landmark specific detectors. Each component-landmark detector is applied independently and the information obtained is used to make inferences about the layout of multiple components. In addition, we incorporate a multi-view representation based on an aspect graph approach. The performance of our algorithm is assessed using data from a publicly available database. The failure rate of our method is lower than that of commercially available software.
Denoising for 3-D Photon-Limited Imaging Data Using Nonseparable Filterbanks

In this paper, we present a novel frame-based denoising algorithm for photon-limited 3D images. We first construct a new 3D nonseparable filterbank by adding elements to an existing frame in a structurally stable way. In contrast with the traditional 3D separable wavelet system, the new filterbank is capable of using edge information in multiple directions. We then propose a data-adaptive hysteresis thresholding algorithm based on this new 3D nonseparable filterbank. In addition, we develop a new validation strategy for denoising of photon-limited images containing sparse structures, such as neurons (the structure of interest is less than 5% of total volume). The validation method, based on tubular neighborhoods around the structure, is used to determine the optimal threshold of the proposed denoising algorithm. We compare our method with other state-of-the-art methods and report very encouraging results on applications utilizing both synthetic and real data.
UHDB31: A Dataset for Better Understanding Face Recognition Across Pose and Illumination Variation

Face datasets are a fundamental tool to analyze the performance of face recognition algorithms. However, the accuracy achieved on current benchmark datasets is saturated. Although multiple face datasets have been published recently, they only focus on the number of samples and lack diversity on facial appearance factors, such as pose and illumination. In addition, while 3D data have been demonstrated improved face recognition accuracy by a significant margin, only a few 3D face datasets provide high quality 2D and 3D data. In this paper, we introduce a new and challenging dataset, called UHDB31, which not only allows direct measurement of the influence of pose, illumination, and resolution on face recognition but also facilitates different experimental configurations with both 2D and 3D data. We conduct a series of experiments with various face recognition algorithms and point out how far they are from solving the face recognition problem under pose, illumination, and resolution variation. The dataset is publicly available and free for research use1.
Cascaded regression for CT slice localization

Automated computational tools are needed to estimate the position of a slice of interest within a contiguous stack of slices. Such estimation is useful to retrieve relevant slices from a volume of slices in clinical analysis or it can be used as an initialization step to other post-processing and image analysis techniques. In this paper, we present a novel method to determine the location of a slice of interest within a given volume by formulating it as a regression problem. The input variables for the regression are obtained from simple intensity features computed from a pyramid representation of the slice. We assess the performance of the proposed method by comparing the estimated positions of slices of interest in CT data with manual annotations. Our method was validated on a dataset of 45 volumes and promising results were obtained for 5 different target slices, the average error being 2 slices.
iRay: Mobile AR Using Structure Sensor

Using depth information has become more popular in recent years, as it adds a new dimension to 2D camera views. We have developed a novel mobile application called iRay, which uses depth information to achieve highly accurate markerless registration on a mobile device for medical use. We use a Structure Sensor to capture depth information that is portable to iPad. Its SDK also provides SLAM data to track pose. ICP is applied to achieve highly accurate registration between the 3D surface of a human torso and a pre-scanned torso model. The experiments demonstrate our results under motion blur, partial occlusion, and small movements. This application has the potential to be used for medical education and intervention.
Towards fast 3D ear recognition for real-life biometric applications

Three-dimensional data are increasingly being used for biometric purposes as they offer resilience to problems common in two-dimensional data. They have been successfully applied to face recognition and more recently to ear recognition. However, real-life biometric applications require algorithms that are both robust and efficient so that they scale well with the size of the databases. A novel ear recognition method is presented that uses a generic annotated ear model to register and fit each ear dataset. Then a compact biometric signature is extracted that retains 3D information. The proposed method is evaluated using the largest publicly available 3D ear database appended with our own database, resulting in a database containing data from multiple 3D sensor types. Using this database it is shown that the proposed method is not only robust, accurate and sensor invariant but also extremely efficient, thus making it suitable for real-life biometric applications.
Segmentation of neurons based on one-class classification

In this paper, we propose a novel one-class classification method to segment neurons. First, a new criterion to select a training set consisting of background voxels is proposed. Then, a discriminant function is learned from the training set that allows determining how similar an unlabeled voxel is to the voxels in the background class. Finally, foreground voxels are assigned as those unlabeled voxels that are not classified as background. Our method was qualitatively and quantitatively evaluated on several dataset to demonstrate its ability to accurately and robustly segment neurons.
Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition

The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.
Image-Based Gating of Intravascular Ultrasound Pullback Sequences

Intravascular ultrasound (IVUS) sequences recorded in vivo are subject to a wide array of motion artifacts as the majority of these studies are performed within the coronary arteries of a beating heart. To eliminate these artifacts, an electrocardiogram (ECG) signal is typically used to gate (collect) those frames recorded at the points in time associated with a particular fraction of the cardiac cycle. However, this technique may be suboptimal for a number of reasons, among which is the difficulty of determining the optimal fraction at which to gate. This value is generally nonobvious. To circumvent this problem, we introduce a frame-gating method for IVUS pullbacks that mimics ECG (i.e., in the sense that it selects only one frame per cardiac cycle), but will automatically choose the fraction of the cycle that renders the most stable gated frame set. Stability here is gauged by measuring interframe similarity. Our method operates exclusively on the imagery data and does not require ECG or any form of image segmentation or other high-level image analysis. To validate our algorithm, we compare its behavior versus true ECG gating.
Automated segmentation of thoracic aorta in non-contrast CT images

Aortic calcification has been shown to be related to cardiovascular disease. In this paper, we present a novel method for localization and segmentation of thoracic aorta in non- contrast CT images using dynamic programming concepts to detect and quantify aortic calcium. The localization and segmentation of the aorta are formulated as optimal path detection problems, which are solved using dynamic programming principles. We apply these methods on Hough space for aorta localization and a transformed polar coordinate space for aorta segmentation. We evaluate the proposed approach by comparing it with the manual annotations in terms of aorta location, boundary distance, and volume overlap.
Fine-grained categorization of fish motion patterns in underwater videos

Marine biologists commonly use underwater videos for their research. Their video analysis, however, is typically based on visual inspection. This incurs prohibitively large user costs, and severely limits the scope of biological studies. There is a need for developing vision algorithms that can address specific needs of marine biologists, such as fine-grained categorization of fish motion patterns. This is a difficult problem, because of very small inter-class and large intra-class differences between fish motion patterns. Our approach consists of three steps. First, we apply our new fish detector to identify and localize fish occurrences in each frame, under partial occlusion, and amidst dynamic texture patterns formed by whirls of sand on the sea bed. Then, we conduct tracking-by-detection. Given the similarity between fish detections, defined in terms of fish appearance and motion properties, we formulate fish tracking as transitively linking similar detections between every two consecutive frames, so as to maintain their unique track IDs. Finally, we extract histograms of fish displacements along the estimated tracks. The histograms are classified by the Random Forest technique to recognize distinct classes of fish motion patterns. Evaluation on challenging underwater videos demonstrates that our approach outperforms the state-of-the-art techniques.
Fuzzy-Cuts: A knowledge-driven graph-based method for 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 result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present fuzzy-cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the maximum a posteriori (MAP) estimation of a Markov random field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the object's location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography (CT) data.
Multi-view 3D face reconstruction with deep recurrent neural networks

Image-based 3D face reconstruction has great potential in different areas, such as facial recognition, facial analysis, and facial animation. Due to the variations in image quality, single-image-based 3D face reconstruction might not be sufficient to accurately reconstruct a 3D face. To overcome this limitation, multi-view 3D face reconstruction uses multiple images of the same subject and aggregates complementary information for better accuracy. Though theoretically appealing, there are multiple challenges in practice. Among these challenges, the most significant is that it is difficult to establish coherent and accurate correspondence among a set of images, especially when these images are captured in different conditions. In this paper, we propose a method, Deep Recurrent 3D FAce Reconstruction (DRFAR), to solve the task ofmulti-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep con-volutional neural network (DCNN) and a recurrent neural network (RNN). The DCNN disentangles the facial identity and the facial expression components for each single image independently, while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information, or the identity signal, from the whole set of images to predict the facial identity parameter, which is robust to variations in image quality and is consistent over the whole set of images. Through extensive experiments, we evaluate our proposed method and demonstrate its superiority over existing methods.
Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality
Improved automatic centerline tracing for dendritic structures

Centerline tracing of dendritic structures in confocal images of neurons is an essential tool for the construction of a geometric representation of a neuronal network. In this paper, we propose a novel algorithm (ORION 2) for centerline extraction that is both highly accurate and computationally efficient. The main novelty of the proposed method is the use of a small set of Multiscale Isotropic Laplacian filters for a fast and efficient binarization of the dendritic structure.The performance of this algorithm, which is validated using datasets from DIADEM is shown to be very competitive against other state-of-the-art algorithms.