Deep Belief Network for Feature Extraction of Urban Artificial Targets

Dai, Xiaoai; Cheng, Junying; Gao, Yu; Guo, Shouheng; Yang, Xiaoxia; Xu, Xiaoqian; Yi, Chuixiang

doi:10.1155/2020/2387823

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…of the methods in this article, it can be observed that the two methods can both be used to evaluate the continuity level of the detection results of different image features, but the method put forward in this paper can distinguish the images more effectively. In addition, the evaluation time of the method put forward in this paper is less than that of the method described in literature [11][12][13][14]. Hence, it has significant advantages in the application.…”

Section: Settings Of the Main Parametersmentioning

confidence: 95%

Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

Sun

2021

Advances in Multimedia

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In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.

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Section: Settings Of the Main Parametersmentioning

confidence: 95%

Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

Sun

2021

Advances in Multimedia

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“…The majority of those people had diabetes and were being handled with steroids for SARS-CoV-2 disease, which could have caused them more vulnerable to fungal infection. Mucormycosis seems to be more prevalent among people who have a low immune system or who have had a bone marrow transplant with fewer neutrophils [35] [85]. COVID-19's introduction has resulted in a slew of new illnesses and problems [16] [63].…”

Section: Black Fungus Infection In India: Mucormycosis Spreading In I...mentioning

confidence: 99%

“…The fungus detection system required a powerful feature extraction method in order to deal with all of these difficult difficulties. Dalal et al [35] presented Histogram of Oriented Gradients as a method for detecting persons (HOG). The extraction of features from photos was accomplished using this methodology.…”

Section: Feature Extractionmentioning

confidence: 99%

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Nira

Kumar

2022

Multimed Tools Appl

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As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

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“…e visualization analysis is conducted on the 3D image recognition residual network. e diversity and accuracy are defined, and comprehensive evaluation is conducted to complete the visualization selectivity analysis of the 3D image recognition residual network [15][16][17]. rough the analysis of residual network visualization in 3D image recognition by composing 3D image recognition residual network visualization based on the particle swarm in the search space, the characteristic vector of the data information on the corresponding 3D image recognition residual network visualization can be expressed in the following form:…”

Section: Svm-knn Network For 3d Imagementioning

confidence: 99%

Visualization of the Residual Network for 3D Image Recognition considering the SVM Combined with the K-Nearest Neighbor Algorithm

Liu¹

2022

Mobile Information Systems

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The support vector machine (SVM) combined with the K-nearest neighbor (KNN) algorithm is applied to the 3D image recognition algorithm to cope with the issues of 3D image recognition in the image processing process. In this paper, the KNN algorithm is used to extend the eigenvalues of the residual network from frequency to complex plane according to the eigenvalues of 3D images, and the relevant data are extracted from the images by using the corresponding separation rules of the SVM. Subsequently, a sparse observation model is established based on the SVM combined with the KNN, and the 3D image processing issue is converted to a residual network visualization issue of 3D image recognition by establishing a parameterized database. Meanwhile, detection of high-resolution distance of 3D images is conducted, and the variational inference method is used to visualize and analyze the 3D images accordingly. Finally, through the experimental study, it can be known that the method of SVM combined with the KNN algorithm can reach an accuracy of up to 94.18% in the dataset of the Princeton 3D polygonal models. After comparison with the existing SVM-KNN method and deep learning, it can be concluded that the method put forward in this paper has a higher recognition rate and stronger anti-interference capacity, and the algorithm has a superior convergence speed with less adjustment parameters.

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Deep Belief Network for Feature Extraction of Urban Artificial Targets

Cited by 16 publications

References 17 publications

Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Visualization of the Residual Network for 3D Image Recognition considering the SVM Combined with the K-Nearest Neighbor Algorithm

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