digital painting is a process of creating a digital artwork using modern human-computer interaction technologies. One of the core enabling technologies is the real-time tracking of user's strokes, which is general...
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digital painting is a process of creating a digital artwork using modern human-computer interaction technologies. One of the core enabling technologies is the real-time tracking of user's strokes, which is generally supplied by a digital tablet with a stylus. While the digital tablet technology provides highly accurate tracking, the drawing should be done with a rigid stylus on a plastic surface. This sometimes destroys the realism of drawing, such as interaction with the digital tablet cannot provide the feedback of subtle texture, friction of the paper/fabric canvas and tension of soft painting brush. This becomes particularly problematic for traditional painting artists who are trained with and prefer real painting brush and paper/fabric canvas. Thus, the aim of this work is to present an alternative solution where the user's strokes can be tracked even when the actual brush and canvas are used. To this end, we proposed two approaches for digitally tracking the tip of flexible bristles of a soft brush, so that the painting can be createddigitally on a computer. The first approach captures the silhouette of deforming bristles using a pair of well-aligned infra-red (IR) cameras, which extracts the tip from the silhouette, and reconstructs the 2d position of the tip. The second approach predicts the brush tip position through a deep ensemble network-based approach where the relationship between the brush tip position and brush handle pose are trained with our novel model comprising of Long-Short Term Memory Autoencoder and1-d convolutional neural network. The trained model is used to predict the brush tip position in realtime. Both approaches extensively evaluated through multiple tests. Furthermore, our model outperforms the state-of-the-art models.
This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutionalneuralnetwork (1-d CNN) and a decision-level fusion strategy. As structura...
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This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutionalneuralnetwork (1-d CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering;the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-d CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrateddetection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved;for the numerical and experimental models, the detection accuracy was 10% and 16-30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.
Pu-erh tea is a famous Chinese fermented tea, and its quality and flavor are closely related to the storage time used for its fermentation. This paper puts forward one method to discriminate the age of Pu-erh tea by e...
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Pu-erh tea is a famous Chinese fermented tea, and its quality and flavor are closely related to the storage time used for its fermentation. This paper puts forward one method to discriminate the age of Pu-erh tea by employing a voltammetric electronic tongue (VE-Tongue) combined with deep learning and transfer learning techniques. To make the deep learning model suitable for processing VE-Tongue signals, a one-dimensional convolutionalneuralnetwork (1-d CNN) was developed to automatically perform feature extraction and classification. Transfer learning (TL) was introduced to train the model to reduce the training complexity and enhance the generalization capability of the CNN. The performance of the proposed model was further compared with that of traditional machine learning methods such as the backpropagation neuralnetwork, support vector machine and extreme learning machine. The results showed that the proposed model exhibited better performance in classifying Pu-erh tea than other methods. Its accuracy for the test set, precision, recall and F1 score was 98.80%, 98.2%, 98%, and 0.98, respectively. This study found that the VE-Tongue combined with deep learning and TL algorithms could be a sensitive, reliable and effective detection method for identifying the amount of storage time of Pu-erh tea, which could further expand its applications to other related fields.
Recently, the new Geographic object-based image analysis (GEOBIA) was proposed as an alternative classification approach to pixel based ones. In GEOBIA, image segments can be depicted with various attributes such as s...
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ISBN:
(纸本)9781510621626
Recently, the new Geographic object-based image analysis (GEOBIA) was proposed as an alternative classification approach to pixel based ones. In GEOBIA, image segments can be depicted with various attributes such as spectral, texture, shape, deep features and context, and hence final classification can produce better land cover/use map. The presence of such a large number of features poses significant challenges to standard machine learning methods and has rendered many existing classification techniques impractical. In this work, we are interested to feature selection techniques, which are employed to reduce the dimensionality of the data while keeping the most of its expressive power. Inspired by recent works in remote sensing using convolutionalneuralnetworks (CNNs), especially for hyperspectral band selection, a feature selection approach based on One-dimensional convolutionalneuralnetworks (1-d CNN) is proposed in this study. All object-based features are used to train the 1-d CNN to obtain well trained model. After testing different feature combinations, we use the well trained model to obtain their test classification accuracies, and finally we select the subset of features with the highest precision. In our experiments, we evaluate our feature selection approach on 30-cm resolution colour infrared (CIR) aerial orthoimagery. A multi-resolution segmentation is performed to segment the images into regions, which are characterized later using various spectral, textural and spatial attributes to form the final object-based feature dataset. The obtained experimental results show that the proposed method can achieve satisfactory results when compared with traditional feature selection approaches.
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