A New Approach for Distorted Image Restoration By Distance-based Correction and Cubic Spline Interpolation

Qi, Meiling; Yu, Guanrong; Xu, Yuelei

doi:10.1145/3369973.3369977

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The interpolation method that we used is often used to compute structural defects ( Kodera, Nishitani & Okihara, 2020 ; Wang et al, 2018 ), correct and restore images ( Qi, Yu & Xu, 2019 ), simulate human faces for identification ( Xu & Wang, 2017 ), and reconstruct 3D leaf models ( Oqielat, 2020 ). It has also been used to simulate shapes and analyze characteristics of eyebrows ( Du et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional quantitative analysis of temporal region morphology in Chinese young adult

Zeng

Feng

et al. 2023

PeerJ

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Background Temporal filling is commonly used to correct temporal depression. However, there is a lack of quantitative criteria for pre- and post-operative evaluations. The use of three-dimensional scanning may help improving the success of temporal filling by providing quantitative assessments. The study aimed to compare the results of qualitative morphological evaluation of the temporal region with a quantitative, numerical analysis of the temporal difference value (TDV). Methods We enrolled twenty-six male and forty-nine female volunteers aged 18 to 29 years. Facial images were acquired in OBJ format using 3dMD facial stereo-photography. The morphologies of the temporal regions were separately evaluated by four researchers in the form of two-dimensional (2D) images. Results were classified as either aesthetic or unaesthetic. The quantitative evaluation of the temporal region was then conducted. First, the temporal region was trimmed out from the original 3D image into a new OBJ file. Second, interpolation was used to construct a smooth, adapted surface. Third, a mathematical model of temporal region flatness denoted as the TDV, which was defined as the sum of the Euclidean distances of all 3D points between the constructed surface and the temporal-region OBJ file. The classification of each sample was compared with its TDV to verify the mathematical model’s validity. The cutoff threshold and prediction accuracy of this mathematical model were calculated. Results The cutoff threshold between aesthetic and unaesthetic TDV was found to be 24.66 for males and 28.11 for females. The prediction accuracy rate was 0.73 for men and 0.73 for women. Conclusion The method has high overlap and good repeatability and minimizes the influence of subjective aesthetics on morphological judgment. TDV has a certain reference value for clinical temporal region evaluation.

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