Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

Wongkhuenkaew, Ritipong; Auephanwiriyakul, Sansanee; Chaiworawitkul, Marasri; Theera-Umpon, Nipon

doi:10.3390/app11052363

Cited by 8 publications

(12 citation statements)

References 30 publications

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“…First, the center of the two point-clouds were calculated. This was an imaginary point where the mass of the object was concentrated, and its coordinates were obtained by calculating the average of the coordinate values of all points in the point cloud [28]. Each point in the point cloud was subtracted from the coordinates of the center of mass point and finally resaved as a new point cloud datum for the next step of singular value decomposition (SVD) to find the transformation matrix.…”

Section: Accurate Registration Of the Point Cloudmentioning

confidence: 99%

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

Wang

et al. 2022

Agronomy

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Precise reconstruction of the morphological structure of the soybean canopy and acquisition of plant traits have great theoretical significance and practical value for soybean variety selection, scientific cultivation, and fine management. Since it is difficult to obtain all-around information on living plants with traditional single or binocular machine vision, this paper proposes a three-dimensional (3D) method of reconstructing the soybean canopy for calculation of phenotypic traits based on multivision. First, a multivision acquisition system based on the Kinect sensor was constructed to obtain all-around point cloud data of soybean in three viewpoints, with different fertility stages of soybean as the research object. Second, conditional filtering and K-nearest neighbor filtering (KNN) algorithms were used to preprocess the raw 3D point cloud. The point clouds were matched and fused by the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms to accomplish the 3D reconstruction of the soybean canopy. Finally, the plant height, leafstalk angle and crown width of soybean were calculated based on the 3D reconstruction of soybean canopy. The experimental results showed that the average deviations of the method was 2.84 cm, 4.0866° and 0.0213 m, respectively. The determination coefficients between the calculated values and measured values were 0.984, 0.9195 and 0.9235. The average deviation of the RANSAC + ICP was 0.0323, which was 0.0214 lower thanthe value calculated by the ICP algorithm. The results enable the precise 3D reconstruction of living soybean plants and quantitative detection for phenotypic traits.

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Section: Accurate Registration Of the Point Cloudmentioning

confidence: 99%

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

Wang

et al. 2022

Agronomy

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“…where t 2 is the random number generation time required for Equations ( 2), ( 3), and ( 6) in the updating process, t 3 is the time required for the MOA optimized by compound cycloid updating, and t 4 is the time required for individual updating, according to Equations ( 3) and (6).…”

Section: Time Complexity Of the Algorithmmentioning

confidence: 99%

“…Therefore, extensive research has been conducted on swarm intelligence algorithms in recent years. Inspired by the laws underlying the development of natural things, some examples of these algorithms are the teaching and learning optimization algorithm (TLBO) [1], the positive chord algorithm (SCA) [2,3], the particle swarm optimization (PSO) [4][5][6], and the genetic algorithm (GA) [7,8]. They can also be inspired by the collective or social intelligence of natural biology, as in the case of the Harris hawks algorithm (HHO) [9,10], the artificial fish swarm algorithm (FSA) [11], the sparrow search algorithm (CSA) [12][13][14], and the gray wolf optimization algorithm (GWO) [15].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Improved Arithmetic Optimization Algorithm

Liu

Pang

et al. 2022

Symmetry

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To improve the performance of the arithmetic optimization algorithm (AOA) and solve problems in the AOA, a novel improved AOA using a multi-strategy approach is proposed. Firstly, circle chaotic mapping is used to increase the diversity of the population. Secondly, a math optimizer accelerated (MOA) function optimized by means of a composite cycloid is proposed to improve the convergence speed of the algorithm. Meanwhile, the symmetry of the composite cycloid is used to balance the global search ability in the early and late iterations. Thirdly, an optimal mutation strategy combining the sparrow elite mutation approach and Cauchy disturbances is used to increase the ability of individuals to jump out of the local optimal. The Rastrigin function is selected as the reference test function to analyze the effectiveness of the improved strategy. Twenty benchmark test functions, algorithm time complexity, the Wilcoxon rank-sum test, and the CEC2019 test set are selected to test the overall performance of the improved algorithm, and the results are then compared with those of other algorithms. The test results show that the improved algorithm has obvious advantages in terms of both its global search ability and convergence speed. Finally, the improved algorithm is applied to an engineering example to further verify its practicability.

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“…However, only rotation and/or translation were used in those matching locations. Hence, in our previous work [ 39 ], the statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method was used to find the optimal affine transform (translation, scaling, rotation, and shearing (shortened from a shearing mapping that displaces each point in a fixed direction by an amount proportional to its signed distance from a given line parallel to that direction)) between teeth optical images.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to a research ethical approval requirement, we will not use real images taken from children. Hence, we postulate scanned images from two commercial tooth models and then create point cloud images [ 39 ]. To avoid premature convergence and to balance between exploration and exploitation, we modify the grey wolf optimization algorithm [ 40 ] with behavior considerations and dimensional learning strategies [ 41 , 42 , 43 , 44 ], called BCDL-GWO, to find the suitable affine transform between the source and target images.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Wongkhuenkaew,

Auephanwiriyakul,

Chaiworawitkul

et al. 2024

Bioengineering

Self Cite

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Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar.

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Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

Cited by 8 publications

References 30 publications

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits

A Multi-Strategy Improved Arithmetic Optimization Algorithm

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

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