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"content": "Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that employs Partial Least Squares (PLS) regression to derive a measure of similarity between two tensors (videos). We demonstrate the use of this tensor similarity measure along with SVM classifiers to solve the tasks of hand gesture recognition and action classification. We show that our methods significantly outperform the state of the art approaches on two popular datasets: Cambridge hand gesture dataset and UCF sports action dataset. Our method requires no parameter tuning.",
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"abstract": "The study of compressed storage schemes for highly repetitive sequence collections has been recently boosted by the availability of cheaper sequencing technologies and the flood of data they promise to generate. Such a storage scheme may range from the simple goal of retrieving whole individual sequences to the more advanced one of providing fast searches in the collection. In this paper we study alternatives to implement a particularly popular index, namely, the one able of finding all the positions in the collection of substrings of fixed length (Z_$q$_Z-grams). We introduce two novel techniques and show they constitute practical alternatives to handle this scenario. They excel particularly in two cases: when Z_$q$_Z is small (up to 6), and when the collection is extremely repetitive (less than 0.01% mutations).",
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"content": "The study of compressed storage schemes for highly repetitive sequence collections has been recently boosted by the availability of cheaper sequencing technologies and the flood of data they promise to generate. Such a storage scheme may range from the simple goal of retrieving whole individual sequences to the more advanced one of providing fast searches in the collection. In this paper we study alternatives to implement a particularly popular index, namely, the one able of finding all the positions in the collection of substrings of fixed length ($q$-grams). We introduce two novel techniques and show they constitute practical alternatives to handle this scenario. They excel particularly in two cases: when $q$ is small (up to 6), and when the collection is extremely repetitive (less than 0.01% mutations).",
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"doi": "10.1109/ICPR56361.2022.9956586",
"title": "Simple Yet Effective Approach to Repetitive Behavior Classification based on Siamese Network",
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"abstract": "Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.",
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"content": "Conventional studies dealing with repetition detection have mainly focused on tasks of temporal localization or counting the number of repetitions in videos. However, direct discrimination between repetitive and non-repetitive behaviors in videos, called repetitive behavior classification (RBC), has attracted less attention despite its great potential and advantages of: 1) filling the demands in the fields such as classification of repetitive behaviors in children with autism spectrum disorder (ASD) and helping to alleviate manual and time-consuming diagnostic procedures, 2) directly learning representation of differences between repetition and non-repetition patterns along the temporal dimension, and 3) being an effective alternative to the existing repetition counting and temporal segmentation tasks that are struggling with insufficient data and laborious manual annotation effort. In this paper, to the best of our knowledge, we firstly cast the problem of the RBC using deep learning frameworks. For this, we propose a simple yet effective add-on network, SiRepNet, that exploits the Siamese network structure to learn the inherent properties of repetitive behaviors. We also composed the RBC dataset by re-purposing and re-organizing Kinetics and Countix datasets for training our method. To validate our ideas, we carried out extensive experiments on RBC datasets, which showed performance improvement over state-of-the-art video classification algorithms by simply attaching our scheme to them for the RBC task.",
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"title": "Between the Frames - Evaluation of Various Motion Interpolation Algorithms to Improve 360° Video Quality",
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"abstract": "With the increasing availability of 360° video content, it becomes important to provide smoothly playing videos of high quality for end users. For this reason, we compare the influence of different Motion Interpolation (MI) algorithms on 360° video quality. After conducting a pre-test with 12 video experts in [3], we found that MI is a useful tool to increase the QoE (Quality of Experience) of omnidirectional videos. As a result of the pretest, we selected three suitable MI algorithms, namely ffmpeg Motion Compensated Interpolation (MCI), Butterflow and Super-SloMo. Subsequently, we interpolated 15 entertaining and realworld omnidirectional videos with a duration of 20 seconds from 30 fps (original framerate) to 90 fps, which is the native refresh rate of the HMD used, the HTC Vive Pro. To assess QoE, we conducted two subjective tests with 24 and 27 participants. In the first test we used a Modified Paired Comparison (M-PC) method, and in the second test the Absolute Category Rating (ACR) approach. In the M-PC test, 45 stimuli were used and in the ACR test 60. Results show that for most of the 360° videos, the interpolated versions obtained significantly higher quality scores than the lower-framerate source videos, validating our hypothesis that motion interpolation can improve the overall video quality for 360° video. As expected, it was observed that the relative comparisons in the M-PC test result in larger differences in terms of quality. Generally, the ACR method lead to similar results, while reflecting a more realistic viewing situation. In addition, we compared the different MI algorithms and can conclude that with sufficient available computing power Super-SloMo should be preferred for interpolation of omnidirectional videos, while MCI also shows a good performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the increasing availability of 360° video content, it becomes important to provide smoothly playing videos of high quality for end users. For this reason, we compare the influence of different Motion Interpolation (MI) algorithms on 360° video quality. After conducting a pre-test with 12 video experts in [3], we found that MI is a useful tool to increase the QoE (Quality of Experience) of omnidirectional videos. As a result of the pretest, we selected three suitable MI algorithms, namely ffmpeg Motion Compensated Interpolation (MCI), Butterflow and Super-SloMo. Subsequently, we interpolated 15 entertaining and realworld omnidirectional videos with a duration of 20 seconds from 30 fps (original framerate) to 90 fps, which is the native refresh rate of the HMD used, the HTC Vive Pro. To assess QoE, we conducted two subjective tests with 24 and 27 participants. In the first test we used a Modified Paired Comparison (M-PC) method, and in the second test the Absolute Category Rating (ACR) approach. In the M-PC test, 45 stimuli were used and in the ACR test 60. Results show that for most of the 360° videos, the interpolated versions obtained significantly higher quality scores than the lower-framerate source videos, validating our hypothesis that motion interpolation can improve the overall video quality for 360° video. As expected, it was observed that the relative comparisons in the M-PC test result in larger differences in terms of quality. Generally, the ACR method lead to similar results, while reflecting a more realistic viewing situation. In addition, we compared the different MI algorithms and can conclude that with sufficient available computing power Super-SloMo should be preferred for interpolation of omnidirectional videos, while MCI also shows a good performance.",
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"abstract": "3D/2D registration techniques can compensate for the deficiencies of X-ray angiography-based navigation in vascular interventional surgery, such as the lack of depth information and excessive use of contrast agents. In this study, we propose a novel Monte Carlo tree search-based 3D/2D vessel graph registration method. The registration problem is transferred to a tree search problem according to the topology of vessel centerlines. Then, the Monte Carlo tree search method is applied to find the optimal vessel matching associated with highest registration score. Experiments on uninitialized vessel data demonstrate that the proposed method can achieve the highest accuracy among four state-of-the-art methods. An average accuracy of 1.91 mm on clinical coronary artery data is obtained. For the independence of initial pose and robustness to noise, the proposed method can align 3D and 2D vessels without prior initialization in vascular interventional surgery.",
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"content": "Dense 3D shape correspondence is an important problem in computer vision and computer graphics. Recently, the local shape descriptor based 3D shape correspondence approaches have been widely studied, where the local shape descriptor is a real-valued vector to characterize the geometrical structure of the shape. Different from these realvalued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence. The binary spectral shape descriptor can require less storage space and enable fast matching. First, based on the eigenvectors of the Laplace-Beltrami operator, we construct a neural network to form a nonlinear spectral representation to characterize the shape. Then, for the defined positive and negative points on the shapes, we train the constructed neural network by minimizing the errors between the outputs and their corresponding binary descriptors, minimizing the variations of the outputs of the positive points and maximizing the variations of the outputs of the negative points, simultaneously. Finally, we binarize the output of the neural network to form the binary spectral shape descriptor for shape correspondence. The proposed binary spectral shape descriptor is evaluated on the SCAPE and TOSCA 3D shape datasets for shape correspondence. The experimental results demonstrate the effectiveness of the proposed binary shape descriptor for the shape correspondence task.",
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"abstract": "We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, where each point of a query shape receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input shapes. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields defined on two shapes as input, and outputs a soft map between the two given objects. The resulting correspondence is shown to be accurate on several challenging benchmarks comprising multiple categories, synthetic models, real scans with acquisition artifacts, topological noise, and partiality.",
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"abstract": "Recent studies have shown that technology and voltage scaling are expected to increase the likelihood that particle-induced soft errors manifest as multiple-bit errors. This raises concerns about the validity of using single bit-flips for assessing the impact of soft errors in fault injection experiments. The goal of this paper is to investigate whether multiple-bit errors could cause a higher percentage of silent data corruptions (SDCs) compared to single-bit errors. Based on 2700 fault injection campaigns with 15 benchmark programs, featuring a total of 27 million experiments, our results show that single-bit errors in most cases yields a higher percentage of SDCs compared to multiple-bit errors. However, in 8% of the campaigns we observed a higher percentage of SDCs for multiple-bit errors. For most of these campaigns, the highest percentage of SDCs was obtained by flipping at most 3 bits. Moreover, we propose three ways of pruning the error space based on the results.",
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"abstract": "Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.",
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"title": "MSTI: A New Clustering Validity Index for Hierarchical Clustering",
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"abstract": "The clustering validity index (CVI) is an important tool to measure the clustering effect and determine the optimal clustering number (K_opt). However, most of the existing CVIs cannot properly deal with some non-spherical distributions data sets and data sets with great differences in sample size and density among clusters. This paper proposes a revised hierarchical clustering algorithm based on the new clustering validity index (MSTI). Firstly, the new index uses spanning tree related knowledge to construct minimum spanning tree in inter clusters and maximum spanning tree in intra clusters. Then, the new MSTI is defined as the ratio of the cost of the minimum spanning tree among clusters to the cost of maximum spanning tree in each cluster. Under this circumstance, the K_opt is acquired when the above ratio reaches the biggest value. Finally, the new algorithm for optimizing and determining the K_opt is designed by leveraging the Average-Linkage hierarchical clustering algorithm and the new proposed MSTI. We compared the new algorithm integrated with MSTI with the traditional algorithms integrated with 4 commonly used CVIs by utilizing 3 simulated datasets and 3 UCI datasets. The experimental results have shown that the new proposed algorithm integrated with MSTI is effective and accurate in determining the K_opt and the optimal clustering partition for all the tested data sets.",
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"content": "The clustering validity index (CVI) is an important tool to measure the clustering effect and determine the optimal clustering number (K_opt). However, most of the existing CVIs cannot properly deal with some non-spherical distributions data sets and data sets with great differences in sample size and density among clusters. This paper proposes a revised hierarchical clustering algorithm based on the new clustering validity index (MSTI). Firstly, the new index uses spanning tree related knowledge to construct minimum spanning tree in inter clusters and maximum spanning tree in intra clusters. Then, the new MSTI is defined as the ratio of the cost of the minimum spanning tree among clusters to the cost of maximum spanning tree in each cluster. Under this circumstance, the K_opt is acquired when the above ratio reaches the biggest value. Finally, the new algorithm for optimizing and determining the K_opt is designed by leveraging the Average-Linkage hierarchical clustering algorithm and the new proposed MSTI. We compared the new algorithm integrated with MSTI with the traditional algorithms integrated with 4 commonly used CVIs by utilizing 3 simulated datasets and 3 UCI datasets. The experimental results have shown that the new proposed algorithm integrated with MSTI is effective and accurate in determining the K_opt and the optimal clustering partition for all the tested data sets.",
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"fullName": "Peng Li",
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"fullName": "Feng Liu",
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{
"affiliation": "Anhui University",
"fullName": "Er-Zhou Zhua",
"givenName": "Er-Zhou",
"surname": "Zhua",
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"abstract": "Inverse procedural modeling discovers a procedural representation of an existing geometric model and the discovered procedural model then supports synthesizing new similar models. We introduce an automatic approach that generates a compact, efficient, and re-usable procedural representation of a polygonal 3D architectural model. This representation is then used for structure-aware editing and synthesis of new geometric models that resemble the original. Our framework captures the pattern hierarchy of the input model into a split tree data representation. A context-free split grammar, supporting a hierarchical nesting of procedural rules, is extracted from the tree, which establishes the base of our interactive procedural editing engine. We show the application of our approach to a variety of architectural structures obtained by procedurally editing web-sourced models. The grammar generation takes a few minutes even for the most complex input and synthesis is fully interactive for buildings composed of up to 200k polygons.",
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"title": "Medial Axis (Inverse) Transform in Complete 3-Dimensional Riemannian Manifolds",
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"abstract": "The main contribution of this work is the generalisation of the medial axis transform (MAT) and the medial axis inverse transform MAIT) on complete Riemannian manifolds. It is known that almost every solid can be reconstructed from its medial axis and the corresponding radius function. In the past this reconstruction scheme has only been implemented in Euclidean spaces. We will use the concepts of Fermi coordinates that represent a natural generalisation of normal coordinates. However, this concept only works out properly if some substantial conditions for the radius function are established. Several approaches for the computation of the medial axis have been implemented so far but almost all of them lack good numerical results. Usually numerical errors occur because the approaches operate on a discretised model of the corresponding objects. In this work we will assume that both the 3D Riemannian space and the modelled object can be represented by smooth mappings and coordinate charts respectively. Therefore, we can introduce the so called medial equations that will allow us to compute medial surface patches using the implicit function theorem. Finally we will give examples for the MAT and the MAIT and show to what extent the inverse transform is applicable in the context of computer aided geometric design. The geodesic medial modeller is one of those applications.",
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"content": "The main contribution of this work is the generalisation of the medial axis transform (MAT) and the medial axis inverse transform MAIT) on complete Riemannian manifolds. It is known that almost every solid can be reconstructed from its medial axis and the corresponding radius function. In the past this reconstruction scheme has only been implemented in Euclidean spaces. We will use the concepts of Fermi coordinates that represent a natural generalisation of normal coordinates. However, this concept only works out properly if some substantial conditions for the radius function are established. Several approaches for the computation of the medial axis have been implemented so far but almost all of them lack good numerical results. Usually numerical errors occur because the approaches operate on a discretised model of the corresponding objects. In this work we will assume that both the 3D Riemannian space and the modelled object can be represented by smooth mappings and coordinate charts respectively. Therefore, we can introduce the so called medial equations that will allow us to compute medial surface patches using the implicit function theorem. Finally we will give examples for the MAT and the MAIT and show to what extent the inverse transform is applicable in the context of computer aided geometric design. The geodesic medial modeller is one of those applications.",
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"Errors",
"Transforms",
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"authors": [
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"affiliation": "Leibniz Univ. Hannover, Hannover",
"fullName": "Henning Nass",
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"affiliation": "Leibniz Univ. Hannover, Hannover",
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"affiliation": "Leibniz Univ. Hannover, Hannover",
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"affiliation": "Leibniz Univ. Hannover, Hannover",
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"content": "State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.",
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"abstract": "In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects repre-sented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral co-ordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to iso-metric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.",
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"abstract": "Face anti-spoofing is very significant to the security of face recognition. Many existing literatures focus on the study of photo attack. For the video attack, however, the related research efforts are still insufficient. In this paper, instead of extracting features from a single image, features are learned from video frames. To realize face anti-spoofing, the spatiotemporal features of continuous video frames are extracted using 3D convolution neural network (CNN) from the short video frame level. Experimental results show that the two sets of face anti-spoofing public databases, Replay-Attack and CASIA, have achieved the HTER (Half Total Error Rate) of 0.04% and 10.65%, respectively, which is better than the state-of-the-art.",
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"content": "Face anti-spoofing is very significant to the security of face recognition. Many existing literatures focus on the study of photo attack. For the video attack, however, the related research efforts are still insufficient. In this paper, instead of extracting features from a single image, features are learned from video frames. To realize face anti-spoofing, the spatiotemporal features of continuous video frames are extracted using 3D convolution neural network (CNN) from the short video frame level. Experimental results show that the two sets of face anti-spoofing public databases, Replay-Attack and CASIA, have achieved the HTER (Half Total Error Rate) of 0.04% and 10.65%, respectively, which is better than the state-of-the-art.",
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"abstract": "Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is why not recycle off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 x 3 x 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time. Furthermore, we propose a new architecture, named Pseudo-3D Residual Net (P3D ResNet), that exploits all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks. Our P3D ResNet achieves clear improvements on Sports-1M video classification dataset against 3D CNN and frame-based 2D CNN by 5.3% and 1.8%, respectively. We further examine the generalization performance of video representation produced by our pre-trained P3D ResNet on five different benchmarks and three different tasks, demonstrating superior performances over several state-of-the-art techniques.",
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"content": "Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is why not recycle off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 x 3 x 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time. Furthermore, we propose a new architecture, named Pseudo-3D Residual Net (P3D ResNet), that exploits all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks. Our P3D ResNet achieves clear improvements on Sports-1M video classification dataset against 3D CNN and frame-based 2D CNN by 5.3% and 1.8%, respectively. We further examine the generalization performance of video representation produced by our pre-trained P3D ResNet on five different benchmarks and three different tasks, demonstrating superior performances over several state-of-the-art techniques.",
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"content": "Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding. Convolutional Neural Networks (CNNs) have shown its remarkable potential in learning appearance representations from images. However, the learning of dynamic representation, and how it can be effectively combined with appearance features for video analysis, remains an open problem. In this study, we propose a novel spatio-temporal CNN, named Slicing CNN (S-CNN), based on the decomposition of 3D feature maps into 2D spatio-and 2D temporal-slices representations. The decomposition brings unique advantages: (1) the model is capable of capturing dynamics of different semantic units such as groups and objects, (2) it learns separated appearance and dynamic representations while keeping proper interactions between them, and (3) it exploits the selectiveness of spatial filters to discard irrelevant background clutter for crowd understanding. We demonstrate the effectiveness of the proposed S-CNN model on the WWW crowd video dataset for attribute recognition and observe significant performance improvements to the state-of-the-art methods (62.55% from 51.84% [21]).",
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"abstract": "In this work, we present a novel method for capturing human body shape from a single scaled silhouette. We combine deep correlated features capturing different 2D views, and embedding spaces based on 3D cues in a novel convolutional neural network (CNN) based architecture. We first train a CNN to find a richer body shape representation space from pose invariant 3D human shape descriptors. Then, we learn a mapping from silhouettes to this representation space, with the help of a novel architecture that exploits correlation of multi-view data during training time, to improve prediction at test time. We extensively validate our results on synthetic and real data, demonstrating significant improvements in accuracy as compared to the state-of-the-art, and providing a practical system for detailed human body measurements from a single image.",
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"abstract": "It has recently been shown that inducing the ownership illusion and then manipulating the movements of one's self-avatar can lead to compensatory motor control strategies in gait rehabilitation. In order to maximize this effect, there is a need for a method that measures, and monitors embodiment levels of participants immersed in VR to induce and maintain a strong ownership illusion. The objective of this study was to propose a novel approach to measuring embodiment by presenting visual feedback that conflicts with motor control to embodied subjects. Twenty healthy participants were recruited. During experimentations, participants wore an EEG cap and motion capture markers, with an avatar displayed in a HMD from a first-person perspective. They were cued to either perform, watch or imagine a single step forward or to initiate walking on the treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). Results show that subjective levels of embodiment correlate strongly with the difference in μ - ERS power over the motor and pre-motor cortex between the modified and non-modified feedback trials.",
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"content": "It has recently been shown that inducing the ownership illusion and then manipulating the movements of one's self-avatar can lead to compensatory motor control strategies in gait rehabilitation. In order to maximize this effect, there is a need for a method that measures, and monitors embodiment levels of participants immersed in VR to induce and maintain a strong ownership illusion. The objective of this study was to propose a novel approach to measuring embodiment by presenting visual feedback that conflicts with motor control to embodied subjects. Twenty healthy participants were recruited. During experimentations, participants wore an EEG cap and motion capture markers, with an avatar displayed in a HMD from a first-person perspective. They were cued to either perform, watch or imagine a single step forward or to initiate walking on the treadmill. For some of the trials, the avatar took a step with the contralateral limb or stopped walking before the participant stopped (modified feedback). Results show that subjective levels of embodiment correlate strongly with the difference in μ - ERS power over the motor and pre-motor cortex between the modified and non-modified feedback trials.",
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"affiliation": "Ecole de technologie superieure, Montreal, Canada",
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"title": "Effect of Continuous and Discrete Feedback on Agency and Frustration in a Brain-Computer Interface Virtual Reality Interaction",
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"abstract": "Brain-computer interfaces (BCIs) provide users with a means to control external devices or applications using only voluntarily produced brain activity. Controlling a BCI through motor imagery is a skill that must be acquired, however, little evidence is available on how the user's agency and frustration are affected by different types of feedback during an interaction with a BCI. This was investigated during a virtual reality interaction where 14 naive participants controlled an avatar with a BCI while receiving either continuous or discrete feedback on their performance. The agency, frustration, ownership and BCI performance were assessed after each of the two conditions (continuous and discrete feedback). There was no statistical difference between the conditions although the participants generally rated agency higher for the continuous feedback which was also uncorrelated to the BCI performance. This suggests that continuous feedback can be useful for increasing agency for users with poor BCI performance by providing them with some knowledge of performance.",
"abstracts": [
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"content": "Brain-computer interfaces (BCIs) provide users with a means to control external devices or applications using only voluntarily produced brain activity. Controlling a BCI through motor imagery is a skill that must be acquired, however, little evidence is available on how the user's agency and frustration are affected by different types of feedback during an interaction with a BCI. This was investigated during a virtual reality interaction where 14 naive participants controlled an avatar with a BCI while receiving either continuous or discrete feedback on their performance. The agency, frustration, ownership and BCI performance were assessed after each of the two conditions (continuous and discrete feedback). There was no statistical difference between the conditions although the participants generally rated agency higher for the continuous feedback which was also uncorrelated to the BCI performance. This suggests that continuous feedback can be useful for increasing agency for users with poor BCI performance by providing them with some knowledge of performance.",
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"affiliation": "Aalborg University,Design and Media Technology,Department of Architecture,Aalborg,Denmark",
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"affiliation": "Aalborg University,Design and Media Technology,Department of Architecture,Aalborg,Denmark",
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"affiliation": "Aalborg University,Design and Media Technology,Department of Architecture,Aalborg,Denmark",
"fullName": "Bastian I. Hougaard",
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"affiliation": "Aalborg University,Design and Media Technology,Department of Architecture,Aalborg,Denmark",
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"affiliation": "Aalborg University,Design and Media Technology,Department of Architecture,Aalborg,Denmark",
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"abstract": "Recently, great success has been made in facial attribute editing by using adversarial learning. However, most existing studies focus on how to generate an image with high fidelity while ignore where to manipulate. Hence, in this paper, generative adversarial network with semantic masks (SM-GAN), a new framework for accurate facial attribute editing is proposed. Specifically, the proposed framework is constructed by combining GAN with semantic segmentation network. Here, the semantic segmentation network provides the mask with respect to attribute-related region so that the editing only occurs in expected areas. Qualitative and quantitative experiments on public dataset CelebA demonstrate that the proposed approach can not only generate realistic attribute editing results, but also preserve attribute-irrelevant areas unchanged.",
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"abstract": "Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.",
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"normalizedAbstract": "Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.",
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"affiliation": "National University of Singapore",
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"affiliation": "National University of Singapore. Singapore",
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"abstract": "We propose a self-supervised approach to improve the training of Generative Adversarial Networks (GANs) via inducing the discriminator to examine the structural consistency of images. Although natural image samples provide ideal examples of both valid structure and valid texture, learning to reproduce both together remains an open challenge. In our approach, we augment the training set of natural images with modified examples that have degraded structural consistency. These degraded examples are automatically created by randomly exchanging pairs of patches in an image's convolutional feature map. We call this approach feature exchange. With this setup, we propose a novel GAN formulation, termed Feature eXchange GAN (FX-GAN), in which the discriminator is trained not only to distinguish real versus generated images, but also to perform the auxiliary task of distinguishing between real images and structurally corrupted (feature-exchanged) real images. This auxiliary task causes the discriminator to learn the proper feature structure of natural images, which in turn guides the generator to produce images with more realistic structure. Compared with strong GAN baselines, our proposed self-supervision approach improves generated image quality, diversity, and training stability for both the unconditional and class-conditional settings.",
"abstracts": [
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"content": "We propose a self-supervised approach to improve the training of Generative Adversarial Networks (GANs) via inducing the discriminator to examine the structural consistency of images. Although natural image samples provide ideal examples of both valid structure and valid texture, learning to reproduce both together remains an open challenge. In our approach, we augment the training set of natural images with modified examples that have degraded structural consistency. These degraded examples are automatically created by randomly exchanging pairs of patches in an image's convolutional feature map. We call this approach feature exchange. With this setup, we propose a novel GAN formulation, termed Feature eXchange GAN (FX-GAN), in which the discriminator is trained not only to distinguish real versus generated images, but also to perform the auxiliary task of distinguishing between real images and structurally corrupted (feature-exchanged) real images. This auxiliary task causes the discriminator to learn the proper feature structure of natural images, which in turn guides the generator to produce images with more realistic structure. Compared with strong GAN baselines, our proposed self-supervision approach improves generated image quality, diversity, and training stability for both the unconditional and class-conditional settings.",
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"affiliation": "Carnegie Mellon University",
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"affiliation": "University of Kansas",
"fullName": "Wenju Xu",
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"affiliation": "Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA",
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"abstract": "Besides several other factors, radiometric differences between a reference and a floating image greatly influence the achievable accuracy of image registration. In this work we derive the magnitude of registration inaccuracy coming from changes in radiometric properties. This is done for the example of medical X-ray image registration. We therefore estimate the change of image intensity with respect to object shape, X-ray attenuation of the object material and the initial X-ray energy by modeling a simplified image formation process. The change in intensity is then used to determine a closed form estimation of the resulting registration error, independent from a specific registration algorithm. Finally the theoretical calculations are compared to the accuracy of intensity based registration performed on X-ray images with different radiometric properties. Results show that the herewith derived accuracy estimation is well suited to predict the achievable accuracy of a registration for images with radiometric differences.",
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"content": "Besides several other factors, radiometric differences between a reference and a floating image greatly influence the achievable accuracy of image registration. In this work we derive the magnitude of registration inaccuracy coming from changes in radiometric properties. This is done for the example of medical X-ray image registration. We therefore estimate the change of image intensity with respect to object shape, X-ray attenuation of the object material and the initial X-ray energy by modeling a simplified image formation process. The change in intensity is then used to determine a closed form estimation of the resulting registration error, independent from a specific registration algorithm. Finally the theoretical calculations are compared to the accuracy of intensity based registration performed on X-ray images with different radiometric properties. Results show that the herewith derived accuracy estimation is well suited to predict the achievable accuracy of a registration for images with radiometric differences.",
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"abstract": "This paper addresses the problem of correcting visual registration errors in video-based augmented-reality systems. Accurate visual registration between real and computer-generated objects in combined images is critically important for conveying the perception that both types of object occupy the same 3-dimensional (3D) space. To date, augmented-reality systems have concentrated on simply improving 3D coordinate system registration in order to improve apparent (image) registration error. This paper introduces the idea of dynamically measuring registration error in combined images (2D error) and using that information to correct 3D coordinate system registration error which in turn improves registration in the combined images. Registration can be made exact in every combined image if a small video delay can be tolerated. Our experimental augmented-reality system achieves improved image registration, stability, and error tolerance from tracking system drift and jitter over current augmented-reality systems. No additional tracking hardware or other devices are needed on the user's head-mounted display. Computer-generated objects can be \"nailed\" to real-world reference points in every image the user sees with an easily-implemented algorithm. Dynamic error correction as demonstrated here will likely be a key component of future augmented-reality systems.",
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"content": "This paper addresses the problem of correcting visual registration errors in video-based augmented-reality systems. Accurate visual registration between real and computer-generated objects in combined images is critically important for conveying the perception that both types of object occupy the same 3-dimensional (3D) space. To date, augmented-reality systems have concentrated on simply improving 3D coordinate system registration in order to improve apparent (image) registration error. This paper introduces the idea of dynamically measuring registration error in combined images (2D error) and using that information to correct 3D coordinate system registration error which in turn improves registration in the combined images. Registration can be made exact in every combined image if a small video delay can be tolerated. Our experimental augmented-reality system achieves improved image registration, stability, and error tolerance from tracking system drift and jitter over current augmented-reality systems. No additional tracking hardware or other devices are needed on the user's head-mounted display. Computer-generated objects can be \"nailed\" to real-world reference points in every image the user sees with an easily-implemented algorithm. Dynamic error correction as demonstrated here will likely be a key component of future augmented-reality systems.",
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