Neural image reconstruction using a heuristic validation mechanism

Połap, Dawid; Srivastava, Gautam

doi:10.1007/s00521-020-05046-8

Cited by 28 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that the number of feature points of 3D image visual communication is M ⋅ N, the 3D image is virtually reconstructed, and the feature decomposition formula of 3D image virtual reconstruction in visual communication is obtained as follows [18]:…”

Section: Design Of 3d Image Virtualmentioning

confidence: 99%

The Effect of 3D Image Virtual Reconstruction Based on Visual Communication

Bai²,

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction in visual communication are decomposed, and the 3D image visual communication model is constructed. Based on the calculation of the difference of 3D image texture feature points, the spatial position relationship of 3D image feature points after virtual reconstruction is calculated to complete the texture mapping of 3D image. The deep texture feature points of 3D image are extracted. According to the description coefficient of 3D image virtual reconstruction in visual communication, the virtual reconstruction results of 3D image are constrained. The virtual reconstruction algorithm of 3D image is designed to realize the virtual reconstruction of 3D image. The results show that when the number of samples is 200, the virtual reconstruction time of this paper method is 2.1 s, and the system running time is 5 s; the SNR of the virtual reconstruction is 35.5 db. The MSE of 3D image virtual reconstruction is 3%, and the SSIM of virtual reconstruction is 1.38%, which shows that this paper method can effectively improve the ability of 3D image virtual reconstruction.

show abstract

Section: Design Of 3d Image Virtualmentioning

confidence: 99%

The Effect of 3D Image Virtual Reconstruction Based on Visual Communication

Bai²,

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…( 1) 6 kk CD q N    (18) where k is the number of algorithms, N is the number of datasets, and qα can be obtained by looking up the table, where α = 0.05. If the difference between the average sequence values of the two algorithms exceeds the CD, the assumption that the performance of the two algorithms is the same is rejected.…”

Section: Performance Analysismentioning

confidence: 99%

“…In terms of image reconstruction and image fusion, auto-encoders and convolutional neural networks also show their advantages. The authors of [18] showed that even simple autoencoders can be trained to reconstruct an image in such a way that the human eye would not be able to distinguish the noise and signal from a damaged sample. Professor Lu Baoliang of Shanghai Jiao Tong University and his team have done a lot of work regarding the integration of EEG and EOG.…”

Section: Introductionmentioning

confidence: 99%

Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Song

Zhou

Wang

2021

Entropy

View full text Add to dashboard Cite

Vigilance estimation of drivers is a hot research field of current traffic safety. Wearable devices can monitor information regarding the driver’s state in real time, which is then analyzed by a data analysis model to provide an estimation of vigilance. The accuracy of the data analysis model directly affects the effect of vigilance estimation. In this paper, we propose a deep coupling recurrent auto-encoder (DCRA) that combines electroencephalography (EEG) and electrooculography (EOG). This model uses a coupling layer to connect two single-modal auto-encoders to construct a joint objective loss function optimization model, which consists of single-modal loss and multi-modal loss. The single-modal loss is measured by Euclidean distance, and the multi-modal loss is measured by a Mahalanobis distance of metric learning, which can effectively reflect the distance between different modal data so that the distance between different modes can be described more accurately in the new feature space based on the metric matrix. In order to ensure gradient stability in the long sequence learning process, a multi-layer gated recurrent unit (GRU) auto-encoder model was adopted. The DCRA integrates data feature extraction and feature fusion. Relevant comparative experiments show that the DCRA is better than the single-modal method and the latest multi-modal fusion. The DCRA has a lower root mean square error (RMSE) and a higher Pearson correlation coefficient (PCC).

show abstract

“…In recent years, deep learning-based methods have become a major focus in research on automatic image annotation [13]. Image features are extracted by convolution operations [14], and the relationship between image features and labels is set up by using a deep neural network training model. In 2006, Hinton [15] first proposed to effectively train features in a training set using a deep neural network.…”

Section: Related Workmentioning

confidence: 99%

Automatic image annotation method based on a convolutional neural network with threshold optimization

2020

View full text Add to dashboard Cite

In this study, a convolutional neural network with threshold optimization (CNN-THOP) is proposed to solve the issue of overlabeling or downlabeling arising during the multilabel image annotation process in the use of a ranking function for label annotation along with prediction probability. This model fuses the threshold optimization algorithm to the CNN structure. First, an optimal model trained by the CNN is used to predict the test set images, and batch normalization (BN) is added to the CNN structure to effectively accelerate the convergence speed and obtain a group of prediction probabilities. Second, threshold optimization is performed on the obtained prediction probability to derive an optimal threshold for each class of labels to form a group of optimal thresholds. When the prediction probability for this class of labels is greater than or equal to the corresponding optimal threshold, this class of labels is used as the annotation result for the image. During the annotation process, the multilabel annotation for the image to be annotated is realized by loading the optimal model and the optimal threshold. Verification experiments are performed on the MIML, COREL5K, and MSRC datasets. Compared with the MBRM, the CNN-THOP increases the average precision on MIML, COREL5K, and MSRC by 27%, 28% and 33%, respectively. Compared with the E2E-DCNN, the CNN-THOP increases the average recall rate by 3% on both COREL5K and MSRC. The most precise annotation effect for CNN-THOP is observed on the MIML dataset, with a complete matching degree reaching 64.8%.

show abstract

Neural image reconstruction using a heuristic validation mechanism

Cited by 28 publications

References 29 publications

The Effect of 3D Image Virtual Reconstruction Based on Visual Communication

The Effect of 3D Image Virtual Reconstruction Based on Visual Communication

Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Automatic image annotation method based on a convolutional neural network with threshold optimization

Contact Info

Product

Resources

About