Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction

Peng, Jiansheng; Fu, Kui; Wei, Qingjin; Qin, Yong; He, Qiang

doi:10.1155/2020/8871082

Cited by 10 publications

(7 citation statements)

References 45 publications

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“…First, the luminance component v in the channel of neurogenic intestine was extracted, and the illumination component was extracted by multiscale Gaussian blur. Second, the 2D gamma function is used to adjust the brightness component of the original image, and the highlight image is corrected on the premise of effectively retaining the effective information of the original image [17,18]. By recovering the global topological structure of the three-dimensional curve segment, the subspace three-dimensional curve segment of the binocular system was data concatenated to find the optimal path to reconstruct the complete three-dimensional model of the catheter centerline.…”

Section: D Image De-highlighting Algorithmmentioning

confidence: 99%

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Shen

Huang

Zhang

et al. 2022

Scientific Programming

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Neurogenic intestinal care is currently a major research topic in the medical field. In order to improve the accuracy of neurogenic intestinal care and simplify the process of neurogenic intestinal care, this paper adopts a three-dimensional analysis theory to extract the key indicators in neurogenic intestinal care. Through the analysis and calculation of the test data, the calculation results of neurogenic intestinal care under the effect of three-dimensional reconstruction technology were obtained. Through the analysis of the results, the calculation law under the action of the optimization model can be obtained. Based on the three-dimensional reconstruction theory, the contents of neurogenic intestinal care were analyzed by the three-dimensional reconstruction de-highlighting algorithm. The three-dimensional curve was used to screen the key indicators of neurogenic intestinal care, so as to obtain the optimized heavy weight model, and the three-dimensional reconstruction model can analyze the indicators of neurogenic intestinal care. It can be seen from relevant studies that different indicators have different trends in their range of change in the curve projection diagram of the 3D curve screening method. Among them, the curve shadow and curve point have obvious downward changes, while the curve deviation has U-shaped changes. The range of curve weights is relatively large. The variation rule between different data in the 3D curve segment can be obtained through the cost function. Among them, the cost function index and the cost model parameter have the same change trend, and the overall fluctuation range is relatively small through the space index. In the calculation results of neurogenic intestinal care, different factors will have a great influence on the calculation results. Neurons and intestinal peristalsis have the same change trend, while the linear characteristics of nerve endings are relatively obvious, and the fluctuation characteristics of gastrointestinal digestion are good. The health standard decreases linearly with the increase of the sample, and the dynamic reconstruction has a positive effect on the data. This study can provide research ideas for the analysis of neurogenic gut.

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Section: D Image De-highlighting Algorithmmentioning

confidence: 99%

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Shen

Huang

Zhang

et al. 2022

Scientific Programming

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“…During the calculation, a higher weight is assigned to the small area. Specifically, a similar size is obtained by subtracting the ratio of the size of one of the two regions to the size of the entire region [15]. The formula for calculating dimensional similarity is as follows:…”

Section: Image Object Extraction Algorithmmentioning

confidence: 99%

Object Extraction of Tennis Video Based on Deep Learning

Huang

2022

Wireless Communications and Mobile Computing

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Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.

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“…In general, similar to X2CT-GAN (Peng et al, 2020), the overall framework of our network combines GAN and U-like network (U-NET). The input is two 2D X-ray projection images and the output is 3D CT volumes.…”

Section: Network Frameworkmentioning

confidence: 99%

MFCT-GAN: multi-information network to reconstruct CT volumes for security screening

Jiang

2022

JIMSE

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PurposeAt airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.Design/methodology/approachTo more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.FindingsOn account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.Originality/valueThe authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.

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Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction

Cited by 10 publications

References 45 publications

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Application of Three-Dimensional Image Reconstruction Technology in Neurogenic Intestinal Nursing Tasks

Object Extraction of Tennis Video Based on Deep Learning

MFCT-GAN: multi-information network to reconstruct CT volumes for security screening

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