Accurate landmarking from 3D facial scans by CNN and cascade regression

Wang, Kai; An, Jun; Zhao, Xi; Zou, Jianhua

doi:10.1142/s0219691318400076

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…The convolutional neural network was first proposed by Le Cun in 1989 [35] and has been well applied in the field of computer vision [36], [37]. After a convolution operation, each channel is summed to realize a joint mapping of channel and spatial correlations.…”

Section: ) Standard Convolutionmentioning

confidence: 99%

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

Liu

Cai

et al. 2019

IEEE Access

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This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is ''small, light, and fast,'' and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery. INDEX TERMS Residual convolutional neural networks, depthwise separable convolutions, deep learning, fault diagnosis, wavelet packet transform.

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Section: ) Standard Convolutionmentioning

confidence: 99%

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

Liu

Cai

et al. 2019

IEEE Access

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“…D EEP learning (DL) originated from the proposal of convolutional neural network (CNN). The CNN was first proposed by LeCun in 1989 [1] and they have been well applied in the field of image analysis [2], [3]. The potential of DL is limited by the computer hardware level and the gradient disappears in the calculation [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Deep Learning Classification Network for Image Features

Yang

Guo

et al. 2021

IEEE Access

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to noncausality. Concisely, data bias leads to comparable signal-toclutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Index Terms-Synthetic aperture radar (SAR), automatic target recognition (ATR), deep learning, data bias, Shapley value, causality 2 Texture refers to the spatial organization of a set of basic elements [7]. As shown in Fig. 1, the scattering point amplitude has different spatial distributions in the target, clutter, and shadow regions. These spatial distributions create texture signatures of different regions in SAR images.

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Regional land planning based on BPNN and space mining technology

2020

Neural Comput & Applic

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Accurate landmarking from 3D facial scans by CNN and cascade regression

Cited by 6 publications

References 25 publications

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

Performance Evaluation of Deep Learning Classification Network for Image Features

Regional land planning based on BPNN and space mining technology

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