An eikonal equation based path planning method using polygon decomposition and curve evolution

Sun, Zaihong; Shao, Zhufeng; Li, Hui

doi:10.1016/j.dt.2019.12.005

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…The variational method is a method to study and solve functional extremum (maximum or minimum). It is a mathematical field dealing with functions [24], which corresponds to ordinary calculus of functions dealing with numbers. Such a functional can be constructed by the integral of the unknown function and its derivative, so the variational problem is to find the extreme value of the functional.…”

Section: Statistics Of Lightness and Chromaticity Characteristic Values Is Calculated According Tomentioning

confidence: 99%

Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images

Liu

Wang

2021

Advances in Mathematical Physics

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The average accuracy of the fusion of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.77% higher than that of NP-DCNN. Traditional image aesthetic evaluation methods are only effective for specific image sets or specific style images and are not suitable for all types of images. Based on the introduction of the partial differential equation image filtering method, through the parallel supervised learning of aesthetic attribute labels, this paper extracts the global aesthetic depth features, adopts the partial differential equation to evolve the contour C constant, and constructs a convolution neural network. The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. Through the aesthetic evaluation experiment, the overall test accuracy is improved by 0.58% and the average accuracy of the integration of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.83% higher than that of NP-DCNN. It has achieved better test accuracy than before in the seven subcategories with discrimination between high aesthetic and low aesthetic images. It has achieved better classification performance than the traditional feature extraction methods in the public dataset CUHK database, and it has excellent aesthetic reference value.

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Section: Statistics Of Lightness and Chromaticity Characteristic Values Is Calculated According Tomentioning

confidence: 99%