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Article is devoted to the system development allowing to restore the volume of the left ventricle of heart, to estimate final systolic and diastolic volumes on the basis of the sequence of MRT-images from a parasternal position of a short axis in the automatic mode. The realized system was built on convolutional neural networks, 500 patients were used for training, for testing 200.
To solve the problem of training rate decline in neural network caused by too much noise in the traditional image, a new method of expression recognition based on CNN was proposed. First, in order to narrow the face range, face image could be detected from the original image by using the AdaBoost cascade classifier. Then, the coordinates of the eye, mouth and other key parts and brow, nasolabial and...
Recently, deep learning has been proposed and verified to possess the strong ability to learn and express complex features, which has brought significant research achievements in signal processing. As a challenging task in speech signal processing, monaural speech separation has always been the research focus of researchers. From the usage of traditional signal processing methods and shallow models...
Human action recognition is one of the most active research areas of computer vision. With the rapid development of deep learning, using neural networks to realize action recognition becomes a popular thesis. This paper proposes a self-learned action recognition method based on neural networks. The proposed method trains dictionaries with sparse autoencoder (SAE) and extracts the key frames with sparse...
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3-...
We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the...
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from...
Various researches had attempted to unveil the technique of virtuoso pianists using technologies. These researches employ different types of sensors in order to capture motion data of piano playing. Researches that embark on this area faced a common problem, the sensors used in these works are directly touching the pianist, in other words this causes a change of piano playing experience. Since piano...
Most previous algorithms for the recognition of Action Units (AUs) were trained on a small number of sample images. This was due to the limited amount of labeled data available at the time. This meant that data-hungry deep neural networks, which have shown their potential in other computer vision problems, could not be successfully trained to detect AUs. A recent publicly available database with close...
A vibration controller based on wavelet neural network (WNN) is designed for jacket-type offshore platforms with time delay. The offshore platform is regarded as a single-degree-of-freedom (SDOF) system while wave forces are irregular disturbances, and we can rebuild them in the context of Morison equation and wave theory. To reduce the vibration, a WNN vibration controller is designed based on a...
We investigate the differentiation of cold and heat syndromes in Traditional Chinese Medicine with a special concern on the issue of data imbalance. Data imbalance occurs frequently in syndrome differentiation. In this study, we use a neural network classifier, fastText, to differentiate cold and heat syndromes, which have skewed distributions in the medical records and in the population. We investigate...
Thanks to their ability to absorb large amounts of data, Convolutional Neural Networks (CNNs) have become state-of-the-art in numerous vision challenges, sometimes even on par with biological vision. They rely on optimisation routines that typically require intensive computational power, thus the question of embedded architectures is a very active field of research. Of particular interest is the problem...
Fraud detection is an enduring topic that pose a threat to banking, insurance, financial sectors and information security systems such as intrusion detection systems (IDS), etc. Data mining and machine learning techniques help to anticipate and quickly detect fraud and take immediate action to minimize costs. This paper starts with the definition of intrusion detection system and its types, focuses...
In this paper, we propose a new approach for the classification of reaching targets before movement onset, during visually-guided reaching in 3D space. Our approach combines the discriminant power of two-dimensional Electroencephalography (EEG) signals (i.e., EEG images) built from short epochs, with the feature extraction and classification capabilities of deep learning (DL) techniques, such as the...
We propose a method for generating caustic images in real time using a deep/convolutional neural network (CNN). To do so, training images are first rendered using photon mapping, and the CNN learns the correspondences between the depth images and caustic images. After learning, the CNN generates a caustic image from a depth image within 55 milliseconds. In addition, the similarity between the generated...
We propose herein a data-driven dead-zone (DZ) compensation strategy using a model-free Virtual Reference Feedback Tuning (VRFT) approach. The VRFT tuning scheme is accommodated for two controller structures: the first one which explicitly includes a model of the DZ inverse to be identified and the second one which uses a Neural Network (NN) to model the controller to be identified. The main question...
Understanding the types of defects is of practical interest, which could help developers adopt proper measures in current and future software releases. As the amount of bug reports increasing, manual classification brings a heavy burden to developers. In this paper, we propose a word2vec based framework of multi-granularity automatic classification for bug reports based on fault triggers. Except classifying...
Autoimmune diseases are the third cause of mortality in the world. The identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also...
Facial expression recognition is a very important research field to understand human emotions. Many facial expression recognition systems have been proposed in the literature over the years. Some of these methods use neural network approaches with deep architectures to address the problem. Although it seems that the facial expression recognition problem has been solved, there is a large difference...
Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference. Especially, fully-connected layers contain a large number of weights, thus they usually need many off-chip memory accesses for inference. We propose a weight compression method for deep neural networks, which allows values of +1 or −1 only at predetermined positions of the weights so that decoding using...
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