Use of fast realistic simulations on GPU to extract CAD models from microtomographic data in the presence of strong CT artefacts

Vidal, Franck; Mitchell, Iwan T.; Létang, Jean Michel

doi:10.1016/j.precisioneng.2021.10.014

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Some articles analyze the robustness of the algorithms against additive/blurry/motion noise, the success rate reaching 96% in [ 59 ] (48 of 50 pairs correctly matched) and 98.8% in [ 87 ]. Some research automates the identification of medical images (forensic identification, histopathology tissue samples, 3D model reconstruction), with high success rates that translate into a large reduction of manual work by specialists [ 12 , 16 , 82 , 103 ]. For algorithms that attempt to match pairs of feature points, a measure of performance is given by the mean distance between the paired points, in the target image and transformed sensed image.…”

Section: Discussionmentioning

confidence: 99%

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Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

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Section: Discussionmentioning

confidence: 99%

“…Vidal et al, 2022: In [ 16 ], the CMA-ES [ 17 ] algorithm is used to construct 3D CAD models from slices of X-ray scan of a material. The slices are automatically registered to create the final 3D model.…”

Section: Articlesmentioning

confidence: 99%