Application of generative deep learning models for approximation of image distribution density

Yashchenko, A. V.; Potapov, Alexey; Rodionov, S. A.; Zhdanov, Innokentii; Shcherbakov, Oleksandr; Peterson, Maxim

doi:10.1364/jot.86.000769

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Generative Target Tracking Algorithm1

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2022

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“…It can find the local extreme value in the density distribution of a group of data, and then iterate to the densest data. The final effect of the iteration process of MSA is that starting from the starting point, the local extreme value obtained through each iteration reaches the point with the densest feature points step by step, which is related to the set threshold [11][12]. When the MSA is applied to target tracking, the kernel function and weight coefficient need to be referenced to improve the accuracy and tracking ability of the algorithm.…”

Section: Generative Target Tracking Algorithmmentioning

confidence: 99%

Comparison between Generative Model and Discriminant Model Based on Support Vector Nachine Algorithm

2022

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Video target tracking is an important research direction in the field of computer vision, and plays an important role in artificial intelligence and big data applications. This paper mainly studies the comparison between generative model and discriminant model based on support vector machine (SVM) algorithm. This paper constructs MSA model and Structured SVM algorithm model according to the generation model and discriminant model classical target tracking algorithm respectively. This paper mainly uses OTB and TC-128 data sets as test data sets to conduct performance comparison experiments on target tracking algorithms of generative model and discriminant model. It can be seen from the experimental results that the Structured SVM model is superior to the MSA model in all aspects.

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Section: Generative Target Tracking Algorithmmentioning

confidence: 99%

Comparison between Generative Model and Discriminant Model Based on Support Vector Nachine Algorithm

2022

View full text Add to dashboard Cite

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Application of generative deep learning models for approximation of image distribution density

Cited by 1 publication

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Comparison between Generative Model and Discriminant Model Based on Support Vector Nachine Algorithm

Comparison between Generative Model and Discriminant Model Based on Support Vector Nachine Algorithm

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