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Modern patient data tends to be large-scale and multi-dimensional, containing both spatial and temporal features. Learning good spatio-temporal features from large patient data is a challenging task, especially when there are missing observations. In this paper, we propose a spatio-temporal autoencoder (STAE), an unsupervised deep learning scheme, to learn features from large-scale and high-dimensional...
Traditional image classification methods require the independence and the same distribution of training and testing data. However, this requirement cannot always be satisfied in some real-world applications, especially for crossdomain image classification tasks. In this paper, we propose to deal with this problem by combining transfer learning with sparse coding and dictionary learning. In dictionary...
Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
Multi-target stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, a large interval between two neighboring stimulus frequencies is often used to improve classification accuracy. Although recent progresses in stimulus coding and target identification methods that have significantly improved...
In this paper, we exploit the intrinsic relation between different adjective labels and develop a novel multilabel dictionary learning and sparse coding method which is improved by introducing the structured output association information. Such a method makes use of the label correlation information and is more suitable for the multi-label tactile understanding task. In addition, we develop a globally-convergent...
In this paper, we address the problem of conditional modality learning, whereby one is interested in generating one modality given the other. While it is straightforward to learn a joint distribution over multiple modalities using a deep multi-modal architecture, we observe that such models are not very effective at conditional generation. Hence, we address the problem by learning conditional distributions...
Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most practical BCIs because of its high recognition accuracies and little training of a user. Mixed frequency and phase coding which can implement a number of commands and achieve a high information transfer rate (ITR) has recently been gaining much attention. In order to implement mixed-coded SSVEP-BCI...
HEVC (High Efficiency Video Coding) achieves cutting edge encoding efficiency and outperforms previous standards, such as the H.264/AVC. One of the key contributions to the improvement is the intra-frame coding that employs abundant coding unit (CU) sizes. However finding the optimal CU size is computationally expensive. To alleviate the intra encoding complexity and facilitate the real-time implementation,...
We study the problem of multi-class image classification with large number of classes, of which the one-vs-all based approach is prohibitive in practical applications. Recent state-of-the-art approaches rely on label tree to reduce classification complexity. However, building optimal tree structures and learning precise classifiers to optimize tree loss is challenging. In this paper, we introduce...
This paper analyses the unlinkability and the irreversibility of the iris biometric template protection system based on Bloom filters introduced at ICB 2013. Hermans et al. presented at BIOSIG 2014 an attack on the unlinkability of these templates. In the worst case, their attack succeeds with probability at least 96%. But in their attack, they assume protected templates generated from the same iriscode...
I frame or I slice which adopts the intra prediction is a key part of video coding. Intra prediction is important because the prediction accuracy directly affects the efficiency of the following transformation, quantization and entropy coding. According to the case of intra mode selection in HEVC, it is necessary to improve the prediction for the two most frequently used modes, DC and PLANAR. A significant...
Autonomous Underwater Vehicles (AUVs) gather large volumes of visual imagery, which can help monitor marine ecosystems and plan future surveys. One key task in marine ecology is benthic habitat mapping, the classification of large regions of the ocean floor into broad habitat categories. Since visual data only covers a small fraction of the ocean floor, traditional habitat mapping is performed using...
The High Efficiency Video Coding (HEVC) standard provides a large improvement in terms of compression efficiency in comparison to its predecessors, mainly due to the introduction of new coding tools and more flexible data structures. However, since much more options are tested in a Rate-Distortion (R-D) optimization scheme, such improvement is accompanied by a significant increase in the encoding...
A novel filtering approach that naturally combines information from both intra-frame and motion compensated referencing for efficient prediction is proposed to fully exploit the spatio-temporal correlations of video signals, thereby achieving superior compression performance. Inspiration was drawn from our recent work on extrapolation filter based intra prediction, which views the spatial signal as...
In this paper, a fast learning-based Coding Unit (CU) size selection method is presented for High Efficiency Video Coding (HEVC) intra prediction. Our analysis shows that non-normalized histogram of oriented gradient (n-HOG) can be used to select CU size. For each size of CU, n-HOGs of training sequences are clustered to construct a codebook offline. The optimum CU sizes are determined by comparing...
The application of machine learning algorithms in wireless communications has attracted increasing attention due to the promising performance gains recently achieved. Static classification algorithms have been successfully applied to training protocols that adapt transmission parameters according to context information. However, in reality, there are many time-varying reasons for fading channel quality...
The paper proposes a No-Reference (NR) metric to objectively assess the H.264/AVC video quality. The proposed model takes into account the typical artefacts introduced by hybrid block-based motion compensated predictive video codecs as the one related to the H.264/AVC standard. More specifically, these artefacts are the blockiness introduced at the boundaries of each coded block and the temporal flickering...
By methods of Literature, two-way behavioral event interview and statistics, the article studies the components of competence of leisure sports club instructors and points out 21 items of competence components. Meanwhile, the components are also ascertained from the viewpoints of students through the interview of excellent instructors' students. The results show that 14 items o f the total are also...
For natural images, there are usually repeating similar contents but hard to be well predicted locally. Prediction using template matching is an effective technology to exploit such a non-local correlation. In this paper, we propose an alternative scheme to further exploit the non-local correlation. In the proposed scheme, template matching is also used to search for probable similar references to...
Precoding is an efficient approach to obtain high channel capacities and high quality in multiple-input-multiple-output systems and draws much attention in recent researches. In the codebook-based precoding systems, the tradeoff between performance and feedback overhead should be considered. In this paper, a novel version of the Linde-Buzo-Gray (LBG) algorithm is first utilized in the codebook design...
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