WWN-8: Incremental Online Stereo with Shape-from-X Using Life-Long Big Data from Multiple Modalities

Solgi, Mojtaba; Weng, Juyang

doi:10.1016/j.procs.2015.07.309

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…However, the lack of viewpoint information in a single image and the need to introduce a lot of human-computer interaction to build a 3D model of the scene or target greatly restrict its development. Some scholars have proposed 3D reconstruction based on multiple images, such as the shape-based shape from X method [19][20][21], which can reconstruct the depth of small target objects and has the advantages of stability, non-contact, and easy operation. As shown in table 1, shape from X methods can be broadly classified into shape from shading (SFS) [22], shape from motion (SFM) [23], shape from focus (SFF) [24], and shape from defocus (SFD) [25].…”

Section: Introductionmentioning

confidence: 99%

An image fusion-based method for recovering the 3D shape of roll surface defects

Xu,

Lou

et al. 2023

Meas. Sci. Technol.

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Most of the existing studies on roll surface defects focus on qualitative detection and lack of quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are stylus method, laser scanning method and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform (NSST) is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called Mm-PCNN to obtain a fully focused image. The method achieves 3D shape recovery of defects by modelling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. The experimental results show that the proposed method effectively integrates detailed information and achieves a reconstruction error control of less than 4%, thus ensuring higher accuracy and improved noise immunity.

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