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Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction...
In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian...
As the human eye on the image of different regions of the contrast sensitivity is different, it is particularly important to segment the image region more accurately in the image quality evaluation. Based on this, this paper presents a non-reference image region division method based on deep learning. Firstly, the Canny operator performs image edge detection at low threshold to obtain the strong edge...
The accurate short-term traffic flow prediction can provide timely and accurate traffic condition information which can help one to make travel decision and mitigate the traffic jam. Deep learning (DL) provides a new paradigm for the analysis of big data generated by the urban daily traffic. In this paper, we propose a novel end-to-end deep learning architecture which consists of two modules. We combine...
Since their introduction over a year ago, Google's TensorFlow package for learning with multilayer neural networks and their Word2Vec representation of words have both gained a high degree of notoriety. This paper considers the application of TensorFlow-guided learning and Word2Vec-based representations to the problems of classification in requirements documents. In this paper, we compare three categories...
In recent years, Convolutional Neural Networks (ConvNets) have become the quintessential component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of applications, the performance needs vary significantly, from high-throughput image recognition to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be...
Research on Deep Learning algorithms has progressed rapidly in recent years. Since the inception of deep learning, numerous architectures have been proposed for various applications targeting pattern recognition, image, audio and information analysis. For example, often audio signal classifications use variations of Deep Belief Networks (DBN), while a Deep Neural Network (DNN) called AlexNet is widely...
Recent works on crowd counting have achieved promising performance by employing the Convolutional Neurol Network (CNN) based features. These works usually design a deep network to detect pedestrian heads, and then count them. In this paper, we propose a novel approach to count pedestrians effectively based on the statistical CNN features. In particular, our approach only uses the first layer features...
The paper presents an application of transfer learning using convolutional neural network (CNN) in recognition of the drill state on the basis of hole images drilled in the laminated chipboard. Three classes are recognized: red, yellow and green, which correspond with 3 stages of drill state. Red class indicates the drill, which is worn out and should be replaced immediately in drilling process. Yellow...
Recently, Convolutional neural network (CNN) architectures in deep learning have achieved significant results in the field of computer vision. To transform this performance toward the task of intrusion detection (ID) in cyber security, this paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised...
Learning and recognizing 3-dimension (3D) adaptive features are important for crowd scene understanding in video surveillance research. Deep learning architectures such as Convolutional Neural Networks (CNN) have recently shown much success in various computer vision applications. Existing approaches such as hand-crafted method and 2D-CNN architectures are widely used in adaptive feature representations...
Diabetic Macular Edema (DME) is one of the many eye diseases that is commonly found in diabetic patients. If it is left untreated it may cause vision loss. This paper focuses on classification of abnormal and normal OCT (Optical Coherence Tomography) image volumes using a pre-trained CNN (Convolutional Neural Network). Using VGG16 (Visual Geometry Group), features are extracted at different layers...
As VLSI technology nodes continue, the gap between lithography system manufacturing ability and transistor feature size induces serious problems, thus lithography hotspot detection is of importance in physical verification flow. Existing hotspot detection approaches can be categorized into pattern matching-based and machine learning-based. With extreme scaling of transistor feature size and the growing...
In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining...
Unmanned Aerial Vehicles, also known as drones, are becoming increasingly popular for video shooting tasks since they are capable of capturing spectacular aerial shots. Deep learning techniques, such as Convolutional Neural Networks (CNNs), can be utilized to assist various aspects of the flying and the shooting process allowing one human to operate one or more drones at once. However, using deep...
This paper presents an efficient deep learning framework for long-term monitoring of acoustic events from hydrophone big data. The large-scale noisy ONC (Ocean Networks Canada) data may contain rare acoustic events, which can be automatically recognized by utilizing a deep convolutional neural network. Few works have been reported in the area of deep learning for the recognition of different kinds...
Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent...
Hyperspectral image classification has been proved significant in remote sensing field. Traditional classification methods have meet bottlenecks due to the lack of remote sensing background knowledge or high dimensionality. Deep learning based methods, such as deep convolutional neural network (CNN), can effectively extract high level features from raw data. But the training of deep CNN is rather...
Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatially harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the...
Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution...
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