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"abstract": "This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.",
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"content": "This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.",
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"normalizedAbstract": "This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.",
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"abstract": "Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. Outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the out performance and robustness of our framework.",
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"content": "Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. Outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the out performance and robustness of our framework.",
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"content": "Computer vision enables camera data to be utilized in user interfaces to analyze the 3-D context and automatically detect the user intentions. Using cameras as an input modality provides single-handed operations in which the users' actions are recognized without interactions with the screen or keypad. In this context, we have constructed a real-time mobile application prototype where the user's position and gaze is determined in real time, a technique that enables the display of true three-dimensional objects even on a typical 2-D LCD screen. We have defined a series of interaction methods where the user's motion and camera input realistically control the viewpoint on a 3-D scene. The head movement and gaze can be used to interact with hidden objects in a natural manner just by looking at them. We provide a description of the embedded implementation at a system-level where we highlight the application development challenges and trade-offs that need to be dealt with battery powered mobile devices. The implementation includes a parallel pipeline that reduces the latencies of the application.",
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"affiliation": "University of Oulu, Oulu, Finland",
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"affiliation": "University of Oulu, Oulu, Finland",
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"title": "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
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"abstract": "Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual's PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices.",
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"content": "Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual's PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices.",
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"normalizedAbstract": "Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual's PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices.",
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"abstract": "This paper presents an implicit and continuous user verification service, called dCASTRA, for mobile devices based on walking patterns inferred from smart phone sensors. We use LSTM (Long Short Term Memory) neural networks for learning gait biometrics from raw accelerometer and gyroscope data and enable a device centric implementation of the deep learning models for faster predictions. One of the challenges in building a gait biometric model is to differentiate the sensor data pertaining to the walking activity from other activities such as sitting, standing, climbing, running and driving, etc. We design a multi-layer framework, where the initial layer relies on Google Activity Recognition Service to extract the segments corresponding to the walking activity with high confidence and feed extracted time series data to LSTM networks in the subsequent layer. The use of LSTMs eliminate the need for tedious feature engineering and further enable us to capture long-term dependencies within temporal sequences, often overlooked by existing efforts. We use Google TensorFlow to develop LSTM based gait biometrics and deploy on Android-based smart phones for real-time prediction and evaluation. We compare dCASTRA with our prior effort and with other deep network architectures such as Convolutional Neural Networks (CNNs). Our results manifest that LSTM and CNN based dCASTRA identifies users in an average 5-6 steps (using 50 Hz sensor sampling rate) with 99% detection accuracy. However, CNNs face significant training overhead as opposed to LSTMs which in turn limits its ability to be deployed in practice.",
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"content": "This paper presents an implicit and continuous user verification service, called dCASTRA, for mobile devices based on walking patterns inferred from smart phone sensors. We use LSTM (Long Short Term Memory) neural networks for learning gait biometrics from raw accelerometer and gyroscope data and enable a device centric implementation of the deep learning models for faster predictions. One of the challenges in building a gait biometric model is to differentiate the sensor data pertaining to the walking activity from other activities such as sitting, standing, climbing, running and driving, etc. We design a multi-layer framework, where the initial layer relies on Google Activity Recognition Service to extract the segments corresponding to the walking activity with high confidence and feed extracted time series data to LSTM networks in the subsequent layer. The use of LSTMs eliminate the need for tedious feature engineering and further enable us to capture long-term dependencies within temporal sequences, often overlooked by existing efforts. We use Google TensorFlow to develop LSTM based gait biometrics and deploy on Android-based smart phones for real-time prediction and evaluation. We compare dCASTRA with our prior effort and with other deep network architectures such as Convolutional Neural Networks (CNNs). Our results manifest that LSTM and CNN based dCASTRA identifies users in an average 5-6 steps (using 50 Hz sensor sampling rate) with 99% detection accuracy. However, CNNs face significant training overhead as opposed to LSTMs which in turn limits its ability to be deployed in practice.",
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"affiliation": "Linköping University, Sweden",
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"abstract": "Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time series classification datasets are often small. In order to address this problem, we propose a novel time series data augmentation called guided warping. While many data augmentation methods are based on random transformations, guided warping exploits the element alignment properties of Dynamic Time Warping (DTW) and shapeDTW, a high-level DTW method based on shape descriptors, to deterministically warp sample patterns. In this way, the time series are mixed by warping the features of a sample pattern to match the time steps of a reference pattern. Furthermore, we introduce a discriminative teacher in order to serve as a directed reference for the guided warping. We evaluate the method on all 85 datasets in the 2015 UCR Time Series Archive with a deep convolutional neural network (CNN) and a recurrent neural network (RNN). The code with an easy to use implementation can be found at https://github.com/uchidalab/time_series_augmentation.",
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"abstract": "The application of the TERT-IV system prototype by Tianjin University to identify two-phase flow regimes has been introduced. For the several typical flow regimes, the method of principal component analysis and artificial neural network to identify the two-phase flow regimes is presented, and that is proved to have higher recognition rate by experimental test. The research results show that the method is feasible using feature extraction and analysis data to identify two-phase flow regimes under different flow conditions, and prove that it is possible that on line monitor the transportation process of air/water two-phase flow using electrical resistance tomography (ERT) system.",
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"title": "Characteristic Analysis of Gas/Liquid Two-Phase Flow Regimes Based on Wavelet Packet Entropy",
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"abstract": "The identification of flow regime is the basis for measuring flow parameters in two-phase flow accurately. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow regime objectively. In this paper, the 208 measured data from electrical resistance tomography (ERT) are arranged into series based on section to investigate the dynamic characteristics of gas-liquid two-phase flow. Then wavelet packet entropy and wavelet packet node energy are applied to analyze the characteristic of gas-liquid two-phase flow. The results show that the diversified distribution of node energy in wavelet packet can reflect the changing in inner structure of flow regime. Meanwhile, the wavelet packet entropy, which is sensitive to the flow regime transition, can characterize the nonlinear dynamics of gas-liquid two-phase flow. They also provide an efficient supplementary to reveal the flow regime transition mechanism of gas/liquid two-phase flow.",
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{
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"content": "The identification of flow regime is the basis for measuring flow parameters in two-phase flow accurately. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow regime objectively. In this paper, the 208 measured data from electrical resistance tomography (ERT) are arranged into series based on section to investigate the dynamic characteristics of gas-liquid two-phase flow. Then wavelet packet entropy and wavelet packet node energy are applied to analyze the characteristic of gas-liquid two-phase flow. The results show that the diversified distribution of node energy in wavelet packet can reflect the changing in inner structure of flow regime. Meanwhile, the wavelet packet entropy, which is sensitive to the flow regime transition, can characterize the nonlinear dynamics of gas-liquid two-phase flow. They also provide an efficient supplementary to reveal the flow regime transition mechanism of gas/liquid two-phase flow.",
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"proceeding": {
"id": "17D45VtKisM",
"title": "2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW)",
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"article": {
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"doi": "10.1109/HiPCW.2018.8634062",
"title": "A Comparative Study of Turbulence Models for Two-Phase Coaxial Swirling Jet Flows",
"normalizedTitle": "A Comparative Study of Turbulence Models for Two-Phase Coaxial Swirling Jet Flows",
"abstract": "This study assesses different turbulence modeling approaches for simulation of two-phase coaxial annular swirling jet flows. The problem selected from literature involves an analytical inlet profile for an annular liquid sheet sandwiched between two coaxial annular gaseous jets. The liquid-gas interface is resolved using the volume-of-fluid (VOF) model with continuum surface force approximation. 3D unsteady Reynolds averaged Navier-Stokes simulations using up to 8.4 million grid cells and 64 HPC cores are conducted using the Fluent 17.2 software to obtain transient multiphase CFD data for this problem. Different turbulence models explored include the k-epsilon RNG with swirl modification, the Reynolds stress model (RSM), and RSM with scale adaptive simulations (RSM-SAS). Comparisons with the direct numerical results from literature suggest that the scale-adaptive simulation using RSM-SAS approach better predicts the onset of instability, liquid jet column collapse, jet mixing, vortex breakup, and the overall characteristics of this flow.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This study assesses different turbulence modeling approaches for simulation of two-phase coaxial annular swirling jet flows. The problem selected from literature involves an analytical inlet profile for an annular liquid sheet sandwiched between two coaxial annular gaseous jets. The liquid-gas interface is resolved using the volume-of-fluid (VOF) model with continuum surface force approximation. 3D unsteady Reynolds averaged Navier-Stokes simulations using up to 8.4 million grid cells and 64 HPC cores are conducted using the Fluent 17.2 software to obtain transient multiphase CFD data for this problem. Different turbulence models explored include the k-epsilon RNG with swirl modification, the Reynolds stress model (RSM), and RSM with scale adaptive simulations (RSM-SAS). Comparisons with the direct numerical results from literature suggest that the scale-adaptive simulation using RSM-SAS approach better predicts the onset of instability, liquid jet column collapse, jet mixing, vortex breakup, and the overall characteristics of this flow.",
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"normalizedAbstract": "This study assesses different turbulence modeling approaches for simulation of two-phase coaxial annular swirling jet flows. The problem selected from literature involves an analytical inlet profile for an annular liquid sheet sandwiched between two coaxial annular gaseous jets. The liquid-gas interface is resolved using the volume-of-fluid (VOF) model with continuum surface force approximation. 3D unsteady Reynolds averaged Navier-Stokes simulations using up to 8.4 million grid cells and 64 HPC cores are conducted using the Fluent 17.2 software to obtain transient multiphase CFD data for this problem. Different turbulence models explored include the k-epsilon RNG with swirl modification, the Reynolds stress model (RSM), and RSM with scale adaptive simulations (RSM-SAS). Comparisons with the direct numerical results from literature suggest that the scale-adaptive simulation using RSM-SAS approach better predicts the onset of instability, liquid jet column collapse, jet mixing, vortex breakup, and the overall characteristics of this flow.",
"fno": "08634062",
"keywords": [
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"authors": [
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"affiliation": "Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, India",
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"affiliation": "Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, India",
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"abstract": "Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.",
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"affiliation": "George Mason University",
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"affiliation": "George Mason University",
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"affiliation": "George Mason University",
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"content": "A new set of orthogonal moment functions named as GF moments (GFMs) was proposed in this paper. The kernel functions of GFMs is GF-system, which is a class of complete orthogonal spline function set of degree k(k=0,1,2,?). The implementation of GFMs does not involve any numerical approximation and has a rather low computation complexity, since the basis set has the advantages of lower order. These properties make GFMs superior to the traditional polynomial moments such as Legendre moments and Zernike moments, in terms of the image reconstruction. Our simulation results also show that GFMs have a better feature representation capability.",
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"abstract": "In this paper we introduce a method for fast computation of Krawtchouk moments by using of cascaded digital filters. The proposed method based on the outputs of cascaded digital filters operating as accumulators are combined with a simplified Krawtchouk polynomials to form Krawtchouk moments depends on the formulation of Krawtchouk polynomials in terms of binomial functions. The theoretical framework of the proposed method is validated by an experiment on the computation of the Krawtchouk moments then the results are compared to method recursive. Experimental results show that both the proposed algorithm to compute Krawtchouk moments perform better than recursive method in term of computation speed. Finally, the performances of the computational Krawtchouk moments by proposed method in describing images were measured in terms of the image reconstruction by matrix representation.",
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"abstract": "Blendshape based animation is a technique commonly used to animate the face. To generate realistic animation for different faces, creating appropriate blendshapes for each different face is essential. There have been many attempts to find the production-level blend shapes. However, many existing methods mostly require a professional artist's intuition with manual intervention. In this paper, we present a novel approach to automatically generate individually optimized blendshapes from real-time captured facial expressions. The proposed method generates the blendshape from the captured face with two methods: linear regression and an autoencoder. Among results from two methods, we select the trained result that is more similar to the original face. The adopted blendshape could be used to animate the original face more naturally. In addition, the generated blendshape is utilized to retarget the original face animation to another face while preserving the original face's animation characteristic. Comparison of results by animating the face on the screen show linear regression is suitable for retargeting the facial expressions without using the complicated neural networks.",
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"content": "Blendshape based animation is a technique commonly used to animate the face. To generate realistic animation for different faces, creating appropriate blendshapes for each different face is essential. There have been many attempts to find the production-level blend shapes. However, many existing methods mostly require a professional artist's intuition with manual intervention. In this paper, we present a novel approach to automatically generate individually optimized blendshapes from real-time captured facial expressions. The proposed method generates the blendshape from the captured face with two methods: linear regression and an autoencoder. Among results from two methods, we select the trained result that is more similar to the original face. The adopted blendshape could be used to animate the original face more naturally. In addition, the generated blendshape is utilized to retarget the original face animation to another face while preserving the original face's animation characteristic. Comparison of results by animating the face on the screen show linear regression is suitable for retargeting the facial expressions without using the complicated neural networks.",
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"abstract": "Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.",
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"abstract": "Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by (1) establishing a DensePose model for the new animal which is also geometrically aligned to humans (2) introducing a multi-head R-CNN architecture that facilitates transfer of multiple recognition tasks between classes, (3) finding which combination of known classes can be transferred most effectively to the new animal and (4) using self-calibrated uncertainty heads to generate pseudo-labels graded by quality for training a model for this class. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach, showing excellent transfer learning performance.",
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"abstract": "The distance transform (DT) and the medial axis transform (MAT) are two image computation tools used to extract information about the shape and position of foreground pixels relative to each other. Extensively applications of these two transforms are used in the fields of computer vision and image processing, such as expanding/shrinking, thinning, computing the shape factor, etc. There are many different DTs based on different distance metrics. Finding the DT with respect to the Euclidean distance metric is easier to use, but rather time-consuming, so many approximate Euclidean DTs (EDTs) are also widely used in the computer vision and image processing fields. The chessboard DT (CDT) is one kind of DT, which converts an image based on the chessboard distance metric. Traditionally, the MAT and the CDT have usually been viewed as two completely different image computation problems. In this paper, we first point out that the processes to find the CDT and the MAT are almost identical, i.e. the two transforms are interchangeable through the proposed algorithms, so that a MAT can be found by utilizing a CDT algorithm and vice versa.",
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"title": "Shape segmentation using medial point clouds with applications to dental cast analysis",
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"content": "Most of the recent literature on image super-resolution (SR) assumes the availability of training data in the form of paired low resolution (LR) and high resolution (HR) images or the knowledge of the downgrading operator (usually bicubic downscaling). While the proposed methods perform well on standard benchmarks, they often fail to produce convincing results in real-world settings. This is because real-world images can be subject to corruptions such as sensor noise, which are severely altered by bicubic downscaling. Therefore, the models never see a real-world image during training, which limits their generalization capabilities. Moreover, it is cumbersome to collect paired LR and HR images in the same source domain. To address this problem, we propose DSGAN to introduce natural image characteristics in bicubically downscaled images. It can be trained in an unsupervised fashion on HR images, thereby generating LR images with the same characteristics as the original images. We then use the generated data to train a SR model, which greatly improves its performance on real-world images. Furthermore, we propose to separate the low and high image frequencies and treat them differently during training. Since the low frequencies are preserved by downsampling operations, we only require adversarial training to modify the high frequencies. This idea is applied to our DSGAN model as well as the SR model. We demonstrate the effectiveness of our method in several experiments through quantitative and qualitative analysis. Our solution is the winner of the AIM Challenge on Real World SR at ICCV 2019.",
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"normalizedAbstract": "Most of the recent literature on image super-resolution (SR) assumes the availability of training data in the form of paired low resolution (LR) and high resolution (HR) images or the knowledge of the downgrading operator (usually bicubic downscaling). While the proposed methods perform well on standard benchmarks, they often fail to produce convincing results in real-world settings. This is because real-world images can be subject to corruptions such as sensor noise, which are severely altered by bicubic downscaling. Therefore, the models never see a real-world image during training, which limits their generalization capabilities. Moreover, it is cumbersome to collect paired LR and HR images in the same source domain. To address this problem, we propose DSGAN to introduce natural image characteristics in bicubically downscaled images. It can be trained in an unsupervised fashion on HR images, thereby generating LR images with the same characteristics as the original images. We then use the generated data to train a SR model, which greatly improves its performance on real-world images. Furthermore, we propose to separate the low and high image frequencies and treat them differently during training. Since the low frequencies are preserved by downsampling operations, we only require adversarial training to modify the high frequencies. This idea is applied to our DSGAN model as well as the SR model. We demonstrate the effectiveness of our method in several experiments through quantitative and qualitative analysis. Our solution is the winner of the AIM Challenge on Real World SR at ICCV 2019.",
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"Image Resolution",
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"affiliation": "ETH Zürich",
"fullName": "Shuhang Gu",
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"surname": "Gu",
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{
"affiliation": "ETH Zürich",
"fullName": "Radu Timofte",
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"title": "Exemplar Guided Face Image Super-Resolution Without Facial Landmarks",
"normalizedTitle": "Exemplar Guided Face Image Super-Resolution Without Facial Landmarks",
"abstract": "Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.",
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"normalizedAbstract": "Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.",
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"affiliation": "D-ITET, ETH Zurich",
"fullName": "Berk Dogan",
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"affiliation": "D-ITET, ETH Zurich",
"fullName": "Shuhang Gu",
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"title": "2020 IEEE Pacific Visualization Symposium (PacificVis)",
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"title": "SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization",
"normalizedTitle": "SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization",
"abstract": "We present SSR-VFD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of three-dimensional vector field data (VFD). SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones. The core of SSR-VFD lies in the use of three separate neural nets that take the three components of a low-resolution vector field as input and jointly output a synthesized high-resolution vector field. To capture spatial coherence, we take into account magnitude and angle losses in network optimization. Our method can work in the in situ scenario where VFD are down-sampled at simulation time for storage saving and these reduced VFD are upsampled back to their original resolution during postprocessing. To demonstrate the effectiveness of SSR-VFD, we show quantitative and qualitative results with several vector field data sets of different characteristics and compare our method against volume upscaling using bicubic interpolation, and two solutions based on CNN and GAN, respectively.",
"abstracts": [
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"content": "We present SSR-VFD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of three-dimensional vector field data (VFD). SSR-VFD is the first work that advocates a machine learning approach to generate high-resolution vector fields from low-resolution ones. The core of SSR-VFD lies in the use of three separate neural nets that take the three components of a low-resolution vector field as input and jointly output a synthesized high-resolution vector field. To capture spatial coherence, we take into account magnitude and angle losses in network optimization. Our method can work in the in situ scenario where VFD are down-sampled at simulation time for storage saving and these reduced VFD are upsampled back to their original resolution during postprocessing. To demonstrate the effectiveness of SSR-VFD, we show quantitative and qualitative results with several vector field data sets of different characteristics and compare our method against volume upscaling using bicubic interpolation, and two solutions based on CNN and GAN, respectively.",
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"authors": [
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"affiliation": "Nankai University",
"fullName": "Li Guo",
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{
"affiliation": "Zhejiang University",
"fullName": "Shaojie Ye",
"givenName": "Shaojie",
"surname": "Ye",
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{
"affiliation": "University of Notre Dame",
"fullName": "Jun Han",
"givenName": "Jun",
"surname": "Han",
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{
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"abstract": "In user-item networks, the link prediction problem has received considerable attentions and has many applications (e.g., recommender systems, ranking item popularity) in recent years. Many previous works commonly fail to utilize the dynamic nature of the networks. This paper focuses on dealing with the temporal information and proposes an algorithm to cope with the link prediction problem on bipartite networks. We describe a temporal bipartite projection method that yields a projected item graph, called the temporal projection graph (TPG). Based on the TPG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair. STEP leverages the historical behaviors of individual users and the social aggregated behaviors learned from the TPG for the link prediction problem. Furthermore, we use TPG and PageRank to rank the popularity of items. To validate our algorithms, we perform various experiments by using the DBLP author-conference dataset, the Flickr dataset and the Delicious dataset. We show that our results of the link prediction problem for new links are substantially better than other temporal link prediction algorithms. We also find the item rankings generated by our approach match very well with that existed in the real world.",
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"abstract": "An algorithm for link prediction in bipartite network is presented. In the algorithm, we first map the bipartite network onto a unipartite one called projected graph. Based on the projected graph, we define the concept of candidate node pair (CNP). We perform the link prediction only within the CNPs so as to reduce the computation time. We also define the pattern covered by the CNPs and the weight of the patterns. By calculating the weights of the patterns a CNP covers, the connectivity of the CNP can be obtained, which can be used as the final score of link prediction. Experimental results show that our algorithm can get higher speed and superior quality link prediction results in bipartite networks than other methods.",
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"abstract": "Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Spatial Weighted Frequent Itemset (SWFI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an SWFI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated SWFIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all SWFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Spatial Weighted Frequent Itemset (SWFI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an SWFI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated SWFIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all SWFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Spatial Weighted Frequent Itemset (SWFI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an SWFI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated SWFIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all SWFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.",
"fno": "489600a987",
"keywords": [
"Data Mining",
"Database Management Systems",
"Spatial Weighted Frequent Pattern Growth",
"Spatial Weighted Frequent Itemset",
"Spatiotemporal Characteristics",
"User Specified Threshold Value",
"Data Mining",
"Weighted Frequent Itemset Mining",
"Air Pollution Database",
"Spatiotemporal Database",
"Itemsets",
"Spatiotemporal Phenomena",
"Data Mining",
"Spatial Databases",
"Air Pollution",
"Data Models",
"Data Mining Pattern Mining Weighted Frequent Itemset Pattern Growth Spatiotemporal Data"
],
"authors": [
{
"affiliation": "NICT",
"fullName": "Rage Uday Kiran",
"givenName": "Rage",
"surname": "Uday Kiran",
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},
{
"affiliation": "International Institute of Information Technology-Hyderabad",
"fullName": "P. P. C. Reddy",
"givenName": "P. P. C.",
"surname": "Reddy",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "NICT",
"fullName": "Koji Zettsu",
"givenName": "Koji",
"surname": "Zettsu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The University of Tokyo",
"fullName": "Masashi Toyoda",
"givenName": "Masashi",
"surname": "Toyoda",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "National Institute of Informatics",
"fullName": "Masaru Kitsuregawa",
"givenName": "Masaru",
"surname": "Kitsuregawa",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "IIIT-Hyderabad",
"fullName": "Pollipalli Krishna Reddy",
"givenName": "Pollipalli",
"surname": "Krishna Reddy",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2019-11-01T00:00:00",
"pubType": "proceedings",
"pages": "987-996",
"year": "2019",
"issn": null,
"isbn": "978-1-7281-4896-0",
"notes": null,
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