Two-Stream Convolutional Networks for Hyperspectral Target Detection

Zhu, Dehui; Du, Bo; Zhang, Liangpei

doi:10.1109/tgrs.2020.3031902

Cited by 77 publications

(27 citation statements)

References 48 publications

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“…There are three main commonly used characteristics: gray scale characteristics, movement characteristics and frequency characteristics. In terms of expression recognition methods, there are mainly: overall recognition method and local recognition method, deformation extraction method and motion extraction method, geometric feature method and facial feature method [19].…”

Section: Principle and Typical Methods Of Facial Expression Recognitionmentioning

confidence: 99%

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Chen

Shen

Fan

2023

Preprint

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To solve the problem of trait differences in expression characteristics among different age groups, shortage of research on cross-age facial expression analysis, and the lack of datasets across ages, put forward the model in age factor deep sparse fusion extension of the expression recognition. The model utilizes a linear dictionary sequence of a high-level form as an input signal for a sparse representation, the input objects are constructed in a linear combination, and select the optimal solution in all solutions with the sparse level as an indicator, further through the convolutional, pooling and full connection processing of convolutional neural networks, the problems of high similarity, insufficient quantity and uneven distribution of data sets are solved through the fusion extension strategy, on this basis, the age operator is combined as feature elements and expression features are extracted and used as the basis for classification decision. Extension by deep sparse fusion, additional factors were extracted using anthropometric models, internal angle calculation and skin wrinkle detection of age operator as expression features, the comparison of results of multiple dataset experiments demonstrate the effectiveness and stability across datasets of the algorithm, the advantages of this algorithm are also demonstrated by the horizontal alignment of the representative cross-age expression classification algorithm. This method is effective for the accuracy and robustness of expression expressions across ages, it has the research value and the reference significance.

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Section: Principle and Typical Methods Of Facial Expression Recognitionmentioning

confidence: 99%

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Chen

Shen

Fan

2023

Preprint

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“…In order to solve this problem, double-line interpolation is used to determine the pixel value of the point. The linear combination of the four-pixel values around the point is used to represent the value of the point [20,21].…”

Section: Local Binary Patternsmentioning

confidence: 99%

Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

Wang

2022

Applied Sciences

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This paper presents a convolution kernel initialization method based on the local binary patterns (LBP) algorithm and sparse autoencoder. This method can be applied to the initialization of the convolution kernel in the convolutional neural network (CNN). The main function of the convolution kernel is to extract the local pattern of the image by template matching as the target feature of subsequent image recognition. In general, the Xavier initialization method and the He initialization method are used to initialize the convolution kernel. In this paper, firstly, some typical sample images were selected from the training set, and the LBP algorithm was applied to extract the texture information of the typical sample images. Then, the texture information was divided into several small blocks, and these blocks were input into the sparse autoencoder (SAE) for pre-training. After finishing the training, the weight values of the sparse autoencoder that met the statistical features of the data set were used as the initial value of the convolution kernel in the CNN. The experimental result indicates that the method proposed in this paper can speed up the convergence of the network in the network training process and improve the recognition rate of the network to an extent.

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“…Due to the special data characteristics of remote sensing, it is necessary to develop dedicated outlier detectors, instead of using existing ones. In [26], a detector based on convolutional network is proposed to address the problem under limited target samples. In order to take advantage of the spectral information at high dimension, [27] proposes a subspace-based detector for hyperspectral images by using two types of additional information.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

et al. 2021

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Model predictive control (MPC) has been successfully applied in practical wind tunnel systems. As a critical component of MPC, the accurate prediction of Mach number becomes hence very significant. Real measurement data records often contain outliers or noisy data because of the harsh working conditions of practical systems. Such datasets are detrimental to most data-driven Mach number predictors. This paper focuses on improving Mach number prediction via a regression-based outlier detection framework. The development of a regression-based outlier detector for enhancing Mach number prediction is natural since only regression analysis needs to be implemented for both prediction and outlier detection. In contrast to existing regression-based detectors, we have taken into account three problems that may greatly impact the detecting performance. Specifically, we use Bagging technique to reduce the negative influence of unknown outliers during the training phase. Then, the notion of extreme value analysis is used to determine an appropriate threshold for the calculated outlier scores. A robust scaling method based on Hampel identifier is also used to alleviate the influence of outliers on data scaling. Several datasets stemming from a real-world wind tunnel are used in experiments to verify the effectiveness of our detector. The experimental results show that regression-based outlier detectors often perform better if the prior knowledge about the dependent variable is used. In addition, the proposed detector has outperformed other regression-based detectors.

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Two-Stream Convolutional Networks for Hyperspectral Target Detection

Cited by 77 publications

References 48 publications

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

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