Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional n...
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An intra-body communication (IBC) research platform based on virtual instrument was established for the hardware design of IBC transceiver. Firstly, the feasibility of constructing IBC research platform using optic-co...
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Nowadays, hybrid cloud platforms stand as an attractive solution for organizations intending to implement combined private and public cloud applications, in order to meet their profitability requirements. However, thi...
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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 s...
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ISBN:
(纸本)9781467388511
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.
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the Inter...
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Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks an...
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Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid art...
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Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid artery and detect signs of atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive to artifacts from cardiac motion, produce time-averaged images, and do not allow for carotid distensibility analysis. Carotid distensibility is an important, systematic measure of vascular health. Cine fast spin echo (FSE) is a new MR imaging that can obtain dynamic MR data (i.e., cardiac phase-resolved datasets). Dynamic imaging, however, comes at the cost of lower spatial resolution and signal-to-noise ratio, making these data potentially more difficult to segment. This paper introduces a semi-automated segmentation method that segments the common carotid artery (CCA) lumen across the cardiac cycle from dynamic MR images. To the best of our knowledge, this work is the first proposed technique for segmenting cardiac cycle-resolved cine FSE images. It combines a priori knowledge about the size and shape of the CCA, with the max-tree data structure, the tie-zone watershed transform (using identified internal and external markers) and supervised classification, to segment the carotid artery wall-lumen boundary. The user has to select only a seed point (centred in the carotid artery lumen). Technique performance was assessed using forty-five cine FSE data sets, each consisting of images reconstructed at sixteen temporal bins across the cardiac cycle. The automatic segmentation results were compared against the consensus of three different manual segmentation results. Our technique achieved an average Dice coefficient, sensitivity and false positive rate of 0.928±0.031 (mean ± standard deviation), 0.915 ± 0.037 and 0.056 ± 0.049, respectively. Our method achieved higher agreement versus the consensus of the three manual segmen
In this paper, we aim at improving the performance of synthesized speech in statistical parametric speech synthesis (SPSS) based on a generative adversarial network (GAN). In particular, we propose a novel architectur...
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Exercise-based rehabilitation plays a key role for patients with cardiovascular disease (CVD) in improving their well-being and reducing their symptoms. Monitoring and assessing the exercise response at an individual ...
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