This paper focuses on gesture recognition and interactive lighting *** collection of gesture data adopts the Myo armband to obtain surface electromyography(sEMG).Considering that many factors affect sEMG,a customized ...
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This paper focuses on gesture recognition and interactive lighting *** collection of gesture data adopts the Myo armband to obtain surface electromyography(sEMG).Considering that many factors affect sEMG,a customized classifier based on user calibration data is used for gesture *** this paper,machine learning classifiers k-nearest neighbor(KNN),support vector machines(SVM),and naive Bayesian(NB)classifier,which can be used in small sample sets,are selected to classify four gesture *** performance of the three classifiers under different training parameters,different input features,including root mean square(RMS),mean absolute value(MAV),waveform length(WL),slope sign change(SSC)number,zero crossing(ZC)number,and variance(VAR)are tested,and different input channels are also *** results show that:The NB classifier,which assumes that the prior probability of features is polynomial distribution,has the best performance,reaching more than 95%***,an interactive stage lighting controlsystem based on Myo armband gesture recognition is implemented.
This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capa...
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Short videos on the Internet have a huge amount, but most of them are unlabeled. In this paper, a rough short video labelling method based on the image classification neural network is proposed. Convolutional auto-enc...
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Short videos on the Internet have a huge amount, but most of them are unlabeled. In this paper, a rough short video labelling method based on the image classification neural network is proposed. Convolutional auto-encoder is applied to train and learn unlabeled video frames, in order to obtain feature in the specific level. With these features, the video key-frames are extracted by the feature clustering method. These key-frames which represent the video content are put into an image classification network, so that the labels of every video clip can be got. In addition, the different architectures of convolutional auto-encoder are estimated, and a better performance architecture through the experiment result is selected. In the final experiment, the video frame features from the convolutional auto-encoder are compared with those from other extraction methods, where it illustrates remarkable results by the proposed method.
For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great *** this paper,th...
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For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great *** this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the *** addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional *** experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image *** study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.
Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer *** boost the unsupervise...
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Learning-based multi-task models have been widely used in various scene understanding tasks,and complement each other,i.e.,they allow us to consider prior semantic information to better infer *** boost the unsupervised monocular depth estimation using semantic segmentation as an auxiliary *** address the lack of cross-domain datasets and catastrophic forgetting problems encountered in multi-task training,we utilize existing methodology to obtain redundant segmentation maps to build our cross-domain dataset,which not only provides a new way to conduct multi-task training,but also helps us to evaluate results compared with those of other *** addition,in order to comprehensively use the extracted features of the two tasks in the early perception stage,we use a strategy of sharing weights in the network to fuse cross-domain features,and introduce a novel multi-task loss function to further smooth the depth *** experiments on KITTI and Cityscapes datasets show that our method has achieved state-of-the-art performance in the depth estimation task,as well improved semantic segmentation.
The paper presents a limit analysis of psychophyslcal methods which is designed for the changes in visual perception of when visual stimulus appear alternately. The purpose of this experiment is to reveal the quantizi...
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ISBN:
(纸本)9781467391955
The paper presents a limit analysis of psychophyslcal methods which is designed for the changes in visual perception of when visual stimulus appear alternately. The purpose of this experiment is to reveal the quantizing relationship between stimulate and feeling response. Meanwhile, we studied the perception of image on visual information considering the color psychological attributes to clarify the changes of visual perception when image information presented by limit analysis approach. Based on the psychological scale, the test images were divided into three types using psychophyslcal analysis. The test results of psychophyslcal methods also were validated, focusing the verifications on the correctness of the main image date behind the limit analysis. The images data is derived from entropy, moment of inertia, local balance, and correlation. Both the two kinds of analysis arrived at the same conclusion, which turned out that the image perception happens in emotional process and depends upon the psychophyslcal property and signal superposition response.
Aiming at overcoming the defects of basic lighting color rendering for LED stage lighting, this paper establishes a new mixing light method with RGBW four-color mixing model. Compared with traditional tricolor light m...
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The paper presents a limit analysis of psychophysical methods which is designed for the changes in visual perception of when visual stimulus appear alternately. The purpose of this experiment is to reveal the quantizi...
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Aiming at overcoming the defects of basic lighting color rendering for LED stage lighting, this paper establishes a new mixing light method with RGBW four-color mixing model. Compared with traditional tricolor light m...
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Depth maps obtained by RGB-D cameras such as ZED are noisy, and there are invalid areas where depth values absent in the maps, which will affect the subsequent improved processing. This paper investigates the restorat...
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