nils-herrmann/clean_scirepeval huggingface dataset:
3D fusion of cine and late-enhanced cardiac magnetic resonance images

A procedure to fuse the information of short-axis cine and late enhanced magnetic resonance images is presented. First a coherent 3D reconstruction of the images is obtained by object-based interpolation of the information of contiguous slices in stacked short-axis cine acquisitions and by the correction of slice misalignments with the aid of a set of reference long-axis slices. Then, late enhanced stacked images are also interpolated and aligned with the anatomical information. Thus, the complementary information provided by both modalities is combined in a common frame of reference and in a nearly isotropic grid, which is not possible with existing fusion procedures. Numerical improvement is established by comparing the distances between unaligned and aligned manual segmentations of the myocardium in both modalities. Finally, a set of snapshots illustrate the improvement in the information overlap and the ability to reconstruct the gradient in the long-axis.
Screening for Cognitive Impairment by Model-Assisted Cerebral Blood Flow Estimation

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disease; a major health concern in the ageing population with an estimated prevalence of 46 million dementia cases worldwide. Early diagnosis is therefore crucial so mitigating treatments can be initiated at an early stage. Cerebral hypoperfusion has been linked with blood-brain barrier dysfunction in the early stages of AD, and screening for chronic cerebral hypoperfusion in individuals has been proposed for improving the early diagnosis of AD. However, ambulatory measurements of cerebral blood flow are not routinely carried out in the clinical setting. In this study, we combine physiological modeling with Holter blood pressure monitoring and carotid ultrasound imaging to predict 24-h cerebral blood flow (CBF) profiles in individuals. One hundred and three participants [53 with mild cognitive impairment (MCI) and 50 healthy controls] underwent model-assisted prediction of 24-h CBF. Model-predicted CBF and neuropsychological tests were features in lasso regression models for MCI diagnosis.  A CBF-enhanced classifier for diagnosing MCI performed better, area-under-the-curve (AUC) = 0.889 (95%-CI: 0.800 to 0.978), than a classifier based only on the neuropsychological test scores, AUC = 0.818 (95%-CI: 0.643 to 0.992). An additional cohort of 25 participants (11 MCI and 14 healthy) was recruited to perform model validation by arterial spin-labeling magnetic resonance imaging, and to establish a link between measured CBF that predicted by the model.  Ultrasound imaging and ambulatory blood pressure measurements enhanced with physiological modeling can improve MCI diagnosis accuracy.
An integrative approach to cerebrovascular disease healthcare: IT for cerebral aneurysms

One of the central themes in addressing vascular disease is connected with the behavior of the arterial wall; its response to stimuli, its remodeling, the growth or stabilization of lesions and the interaction with implants and drugs. For the case of cerebral aneurysms, we are presenting an effort to embed computational simulation models capable of handling such processes within an IT framework that combines imaging, modeling, genetics and clinical medicine in an integrative and comprehensive fashion. The @neurIST project aims at the development of an IT-enabled patient risk assessment and guidelines generation environment, capable of optimized decision support and treatment design. Within this framework, we present mechanobiological models of the vascular wall that account for the interaction of heamodynamics with vascular wall fiber and cell population and behavior.
Fast Multiscale Modeling of Cardiac Electrophysiology Including Purkinje System

In this paper, we present a modeling methodology to couple the cardiac conduction system to cardiac myocytes through a model of Purkinje-ventricular junctions to yield fast and realistic electrical activation of the ventricles. A patient-specific biventricular geometry is obtained from processing computed tomography scan data. A one-manifold implementation of the fast marching method based on Eikonal-type equations is used for modeling heart electrophysiology, which facilitates the multiscale 1-D-3-D coupling at very low computational costs. The method is illustrated in in-silico experiments where we analyze and compare alternative pacing strategies on the same patient-specific anatomy. We also show very good agreement between the results from the proposed approach and more detailed and comprehensive biophysical models for modeling cardiac electrophysiology. The effect of atrioventricular delay on the distribution of activation time in myocardium is studied with two experiments. Given the reasonable computational times and realistic activation sequences provided by our method, it can have an important clinical impact on the selection of optimal implantation sites of pacing leads or placement of ablation catheter's tip in the context of cardiac rhythm management therapies.
Efficient 3D Geometric and Zernike Moments Computation from Unstructured Surface Meshes

This paper introduces and evaluates a fast exact algorithm and a series of faster approximate algorithms for the computation of 3D geometric moments from an unstructured surface mesh of triangles. Being based on the object surface reduces the computational complexity of these algorithms with respect to volumetric grid-based algorithms. In contrast, it can only be applied for the computation of geometric moments of homogeneous objects. This advantage and restriction is shared with other proposed algorithms based on the object boundary. The proposed exact algorithm reduces the computational complexity for computing geometric moments up to order N with respect to previously proposed exact algorithms, from N9 to N6. The approximate series algorithm appears as a power series on the rate between triangle size and object size, which can be truncated at any desired degree. The higher the number and quality of the triangles, the better the approximation. This approximate algorithm reduces the computational complexity to N3. In addition, the paper introduces a fast algorithm for the computation of 3D Zernike moments from the computed geometric moments, with a computational complexity N4, while the previously proposed algorithm is of order N6. The error introduced by the proposed approximate algorithms is evaluated in different shapes and the cost-benefit ratio in terms of error, and computational time is analyzed for different moment orders.
Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to statistical shape models (SSMs) which capture statistical information about shapes across a population, but offers several advantages over SSMs. First, SDMs can be constructed directly from images such as three-dimensional (3-D) magnetic resonance (MR) or computer tomography volumes without the need for segmentation which is usually a prerequisite for the construction of SSMs. Instead, a nonrigid registration algorithm based on free-form deformations and normalized mutual information is used to compute the deformations required to establish dense correspondences between the reference subject and the subjects in the population class under investigation. Second, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3-D nature of the underlying anatomy into account by analysing dense 3-D deformation fields rather than only information about the surface shape of anatomical structures. We show results for the construction of anatomical models of the brain from the MR images of 25 different subjects. The correspondences obtained by the nonrigid registration are evaluated using anatomical landmark locations and show an average error of 1.40 mm at these anatomical landmark positions. We also demonstrate that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.
Multihop Packet Delay Bound Violation Modeling for Resource Allocation in Video Streaming Over Mesh Networks

Resource allocation plays a critical role in multisession video streaming over mesh networks to maximize the overall video presentation quality under transmission delay and network resource constraints. A critical component in efficient resource allocation is to analyze and model the multihop queuing behavior along the transmission path, estimate the packet loss ratio due to delay bound violation, and predict the amount of video quality degradation after multihop video transmission. In this work, we develop a multihop packet delay bound violation model to predict the packet loss probability and end-to-end distortion for video streaming over multihop networks. To this end, we extract salient features to characterize the input source and network conditions of links along the transmission path and construct a learning-based model using artificial neural network (ANN). Based on this model, we then formulate the resource allocation into a nonconvex optimization problem which aims to minimize the overall video distortion while maintaining fairness between sessions. We solve this optimization problem using Lagrangian duality methods. Extensive experimental results demonstrate that, with this widely-used offline-training-online-estimation mechanism, the proposed model is potentially applicable to almost all network conditions and can provide fairly accurate estimation results as compared with other models with a given sample data set. The proposed optimization algorithm achieves more efficient resource allocation than existing schemes.
Semi-supervised learning for robust car windshield tracking and monitoring in live traffic videos

This paper deals with the problem of car-windshield tracking in live traffic video. To avoid a comprehensive labeled dataset that covers most appearance variations, we aim to appropriately involve unlabeled examples and efficiently update the discriminative model in an online semi-supervised setting. Our approach follows the state-of-the- art “learning by detection” approach, yet different from it in the following aspects. First, instead of assigning hard labels to new added examples, we leave them unlabeled. Second, we focus on exploring the intrinsic manifold structure of data marginal distribution and studying its role in kernel function optimization. The proposed online semi-supervised learning framework involves a 3D mean-shift optimization for windshield localization and is followed by a block-based decision for co-driver detection. The experimental results demonstrate the effectiveness of the proposed method.
Accumulative visual information in wireless video sensor network: definition and analysis

A wireless video sensor network (WVSN) is a system of spatially distributed video sensors which gather and transmit video information over a wireless ad hoc network. To measure, control and optimize the system performance, the following key research problems need to be addressed: 1) how to measure the amount of visual information collected by the video sensor within its operational lifetime; 2) how to quantitatively compare the information sensing efficiency between two video sensors; 3) how to measure the information sensing efficiency of the whole video sensor network; 4) how to control and optimize the information sensing efficiency of the video sensor network. In this work, we introduce the concept of accumulative visual information (AVT), and use it as a measure for the amount of visual information collected the WVSN. Based on the AVI measure and the power-rate-distortion analysis model developed in our previous work, we optimize the efficiency of the WVSN system.
End-to-end video quality analysis and modeling for video streaming over IP network

We derive an analytical end-to-end video quality prediction and control model for video streaming over an IP network. This model maps the quality of service (QoS) parameters defined at the connection level to actual video presentation quality at the receiver end. Specifically, we provide an analysis on the effects of packet loss on the decoded video quality and relate the packet loss ratio, one of the most important QoS parameters at the connection level, to the actual video quality. The model also characterizes the rate-distortion (R-D) behavior of the encoder at the video sequence level, predicting the average encoding quality for a given channel bandwidth. Our experimental results demonstrate the accuracy of the proposed video quality model.
SVM-Based Multi-Hop Video Packet Delay Bound Violation Modeling over Mesh Networks

It is crucial to develop feasible methodologies and techniques for accurately analyzing, predicting end-to-end delay performance over multi-hop network communication paths. In this work, we develop a multi-hop packet delay bound violation model to predict the packet loss probability and end-to-end video distortion for video streaming over multi-hop networks. To this end, we extract salient features to characterize the input source and network conditions of links along the transmission path and construct a statistical learning-based model using Support Vector Machines (SVM) for nonlinear regression. Our extensive simulation results with NS-2 demonstrate that the proposed model achieves accurate and reliable prediction of the packet delay bound violation probability and end-to-end video distortion under various network settings and transmission conditions. Compared with genetic neural network (GNN) prediction model supposed in our previous research, It shows that SVM has a higher accuracy and greater generalization ability of prediction than GNN, which proved the validity and practicality of the model.
Vision-based UAV flight control and obstacle avoidance

In this work, we explore various ideas and approaches to deal with the inherent uncertainty and image noise in motion analysis, and develop a low-complexity, accurate and reliable scheme to estimate the motion fields from UAV navigation videos. The motion field information allows us to accurately estimate ego-motion parameters of the UAV and refine (or correct) the motion measurements from other sensors. Based on the motion field information, we also compute the range map for objects in the scene. Once we have accurate knowledge about the vehicle motion and its navigation environment (range map), control and guidance laws can be designed to navigate the UAV between way points and avoid obstacles
Recognizing Falls from Silhouettes

A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind
Adaptive Silouette Extraction and Human Tracking in Complex and Dynamic Environments

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction and human tracking algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In this work, we propose to address the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extract features from image regions, accumulate the feature information over time, fuse the high-level knowledge with low-level features, and build a time-varying background model. We develop a fuzzy decision process to detach foreground moving objects from the human body. Our experimental results demonstrate that the algorithm is very efficient and robust.
Fast Deep Neural Networks With Knowledge Guided Training and Predicted Regions of Interests for Real-Time Video Object Detection

It has been recognized that deeper and wider neural networks are continuously advancing the state-of-the-art performance of various computer vision and machine learning tasks. However, they often require large sets of labeled data for effective training and suffer from extremely high computational complexity, preventing them from being deployed in real-time systems, for example vehicle object detection from vehicle cameras for assisted driving. In this paper, we aim to develop a fast deep neural network for real-time video object detection by exploring the ideas of knowledge-guided training and predicted regions of interest. Specifically, we will develop a new framework for training deep neural networks on datasets with limited labeled samples using cross-network knowledge projection which is able to improve the network performance while reducing the overall computational complexity significantly. A large pre-trained teacher network is used to observe samples from the training data. A projection matrix is learned to project this teacher-level knowledge and its visual representations from an intermediate layer of the teacher network to an intermediate layer of a thinner and faster student network to guide and regulate the training process. To further speed up the network, we propose to train a low-complexity object detection using traditional machine learning methods, such as support vector machine. Using this low-complexity object detector, we identify the regions of interest that contain the target objects with high confidence. We obtain a mathematical formula to estimate the regions of interest to save the computation for each convolution layer. Our experimental results on vehicle detection from videos demonstrated that the proposed method is able to speed up the network by up to 16 times while maintaining the object detection performance.
Supplementary Virtual Keypoints of Weight-Based Correspondences for Occluded Object Tracking

In this paper, we propose a short-term tracking method, which is more robust than conventional methods for single-target tracking under occlusions. On the one hand, our method uses the features-supplemental points and optical flows for static-adaptive targets based on the online tracking algorithm, which clusters the static-adaptive correspondences for deformable object tracking. On the other hand, our method combines the locally matched target bounding box and the one from optical flows tracking using novel fuzzy weights, which are updated based on the changes in keypoints. In the proposed weighted-based keypoints matching tracker, we employ fast keypoints detection and partial descriptors, which makes it run in real time. We demonstrate the effectiveness of our method extensively on benchmark data sets that contain scenes of object deformation and occlusions. The results show that our algorithm performs more favorably against the competing tracking methods.
Wavelet image coding using blockwise binary classification and trellis coded quantization

With blockwise binary classification and data partitioning, we convert the image subbands to the type of source data for which the trellis coded quantization (TCQ) has the best quantization performance. Compared to the arithmetic coded TCQ (ACTCQ) and other TCQ-based coding schemes, the proposed algorithm significantly reduces the computational complexity. However, it performs competitively with the best available coding algorithms reported in the literature with regard to the rate-distortion performance.
A linear source model and a unified rate control algorithm for DCT video coding

We show that, in any typical transform coding system, there is always a linear relationship between the coding bit rate R and the percentage of zeros among the quantized transform coefficients, denoted by /spl rho/. Based on Shannon's source coding theorem, a theoretical justification is provided for this linear source model. The physical meaning of the model parameter is also discussed. We show that it is directly related to the image content and is a measure of picture complexity. In video coding, we propose an adaptive estimation scheme to estimate this model parameter. Based on the linear source model and the adaptive estimation scheme, a unified rate control algorithm is proposed for various standard video coding systems, such as MPEG-2, H.263, and MPEG-4. Our extensive simulation results show that the proposed rate control outperforms other algorithms reported in the literature by providing much more accurate and robust rate control.
On the computation of the ego-motion and distance to obstacles for a micro air vehicle

In this paper, we have considered the problem of velocity and range estimation for an UAV using a camera and the knowledge of linear speed through a GPS device. In our earlier work (2006), a method for decomposition of a scene into structure blocks, and finding correspondences between such blocks in successive frames was developed. The result of this low-level image processing is (a) a set of structure blocks; (b) the motion of each structure block; and (c) a reliability index between zero and one denoting the confidence of the solution in (b). Here, we show that by solving a constrained optimization problem set up using the results of the image processing, one can obtain the linear and angular velocity of the camera motion, provided the speed of the linear motion is known. Once the velocity parameters is computed, we show how the range to objects in the field of view can be computed
Lossless image compression using super-spatial prediction of structural components

We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structural components, such as edges, patterns, and textures. Existing image compression schemes attempt to predict image data using its spatial neighborhood. In this work, we develop an efficient image compression scheme based on super-spatial prediction of structural units. This so-called super-spatial prediction breaks this neighborhood constraint, attempting to find an optimal prediction of structural components within the whole image domain. We consider only lossless image compression. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art image compression methods.
Energy-efficient integrated camera and sensor system design for wildlife activity monitoring

We introduce our recent research effort on energyefficient portable video communication system design for wildlife activity monitoring. The capability of seeing what an animal sees in the field is very important for wildlife activity monitoring and research. We have designed an integrated video and sensor system, called DeerCam and mount it on animals so as to collect important video and sensor data about their activities in the field. We will explain the major design challenges and research issues in this project.