World of Warcraft (WoW) is one of the most popular massively multiplayer online role-playing games (MMORPGs) having more than 10 million subscribers over the world. In order to engage and retain users understanding an...
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ISBN:
(纸本)9781509049172
World of Warcraft (WoW) is one of the most popular massively multiplayer online role-playing games (MMORPGs) having more than 10 million subscribers over the world. In order to engage and retain users understanding and predicting their behavior can be very useful for game developers. An important component of WoW are so-called guilds, which are social communities whose members can act together efficiently to accomplish more difficult goals and also provide a social atmosphere in which the game might be more entertaining. In this paper, we build predictive models to forecast which of the players will leave their guild in the close future. Our best model uses fuzzy c-means clustering to capture groups of similar guilds, that serve as the basis of an ensemble model, which computes predictions for each cluster separately and combines individual predictions into one final prediction using the memberships of the fuzzy clusters. Empirical analysis on WoW game data shows that our methods convincingly outperform the only existing method in the literature. To ensure transparency and reproducibility we publish the source codes of the research and also provide a Docker image, which makes it possible for anyone who has Docker installed to reproduce all of our results with a single command.
This paper presents a new framework for human action classification using a tensor dynamical model of human action from 3-dimensional (3D) volume sequences and distance measurement on Grassmann manifold. The tensor dy...
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ISBN:
(纸本)9781538607336
This paper presents a new framework for human action classification using a tensor dynamical model of human action from 3-dimensional (3D) volume sequences and distance measurement on Grassmann manifold. The tensor dynamical model is an extension of linear dynamical models for multi-dimensional sequence analysis. Each sub-dimensional linear dynamic model is estimated from tensor sequences using an iterative expectation-maximization (EM) algorithm after projection of tensor sequence to each dimensional axis. The combination of distances on Grassmann manifold of linear dynamic systems in each dimension of the tensor dynamic model provides similarity measurement between two tensor dynamical systems. The proposed approach can be applied to 3D depth or convex hull data as well as 2D video image sequences. Experimental results show good performance in human action recognition from INRIA multiview human action database.
The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to...
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ISBN:
(纸本)9781538607336
The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to dense matrix multiplications and convolutions. As a result, these architectures are highly memory intensive, making them less suitable for embedded vision applications. Sparse Computations are known to be much more memory efficient. In this work, we train and build neural networks which implicitly use sparse computations. We introduce additional gate variables to perform parameter selection and show that this is equivalent to using a spike-and-slab prior. We experimentally validate our method on both small and large networks which result in highly sparse neural network models.
Group activity recognition in sports is often challenging due to the complex dynamics and interaction among the players. In this paper, we propose a recurrent neural network to classify puck possession events in ice h...
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ISBN:
(纸本)9781538607336
Group activity recognition in sports is often challenging due to the complex dynamics and interaction among the players. In this paper, we propose a recurrent neural network to classify puck possession events in ice hockey. Our method extracts features from the whole frame and appearances of the players using a pre-trained convolutional neural network. In this way, our model captures the context information, individual attributes and interaction among the players. Our model requires only the player positions on the image and does not need any explicit annotations for the individual actions or player trajectories, greatly simplifying the input of the system. We evaluate our model on a new Ice Hockey Dataset. Experimental results show that our model produces competitive results on this challenging dataset with much simpler inputs compared with the previous work.
The mitral valve plays a vital role in our circulatory system. To study its functionality, it is important to measure clinically relevant parameters, such as its thickness, mobility and shape. Since manual segmentatio...
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ISBN:
(纸本)9789897582158
The mitral valve plays a vital role in our circulatory system. To study its functionality, it is important to measure clinically relevant parameters, such as its thickness, mobility and shape. Since manual segmentation is impractical, time consuming and requires expert knowledge, an automatic segmentation tool can have a significant clinical impact, providing objective measures to clinicians for understanding the morphology and behaviour of the mitral valve. In this work, a real time tracking method has been proposed for ultrasound videos obtained with the Parasternal Long Axis view. The algorithm is semi-automatic, assumes manual Anterior Mitral Leaflet segmentation in the first frame and then it uses mathematical morphology algorithms to obtain tracking results, further refined by localized active contours during the whole cardiac cycle. Finally, the medial axis is extracted for a quantitative analysis. Results show that the algorithm can segment 1137 frames extracted from 9 fully annotated sequences of the real clinical video data in only 0.89 sec/frame, with an average error of 5 pixels. Furthermore, the algorithms exhibited robust tracking performance in the most difficult situations, which are large frame-to-frame displacements.
The effects of marine and instream energy devices on fish populations are not well-understood, and studying the behavior of fish around these devices is challenging. To address this problem, we have evaluated algorith...
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ISBN:
(纸本)9780692946909
The effects of marine and instream energy devices on fish populations are not well-understood, and studying the behavior of fish around these devices is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish or not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. This automated processing would significantly reduce labor time and costs, compared to current monitoring methods. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The proposed automated processing and recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that advances the scientific understanding of the ecological impacts of ocean and river energy devices.
We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent compon...
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Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-ranknes...
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ISBN:
(纸本)9781538610329
Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits performance in various low-level vision tasks. In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT. An efficient and unsupervised online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. We develop an efficient 3D spatio-temporal data reconstruction framework based on the proposed online learning method, which exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work of solving the proposed problem in an online fashion. We demonstrate video denoising results over commonly used videos from public datasets. Numerical experiments show that the proposed video denoising method outperforms competing methods.
Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in dev...
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ISBN:
(纸本)9781509054435
Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.
Principal Components Analysis (PCA) is the basic approach for processing of 3D tensor images (for example, multi- and hyper-spectral, multi-view, computer tomography, video, etc.). As a result of their processing, the...
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