An Error Analysis of Structured Light Scanning of Biological Tissue

Jensen, Sebastian Hoppe Nesgaard; Wilm, Jakob; Aanæs, Henrik

doi:10.1007/978-3-319-59126-1_12

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…Generally speaking, only the pure direct reflection can be used for determining the 3D shape of human face. However, the direct reflection is usually quite weak, i.e., only few percent of the incident signal for the measurement of human face, while most of the projected light and background illumination will go through the interface and undergo the multiple scattering process [ 26 ].…”

Section: Theoretical Analysesmentioning

confidence: 99%

Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light

Chen

Yue

et al. 2021

Sensors

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This work presents a new approach of surface measurement of human face via the combination of the projection of monochromatic structured light, the optical filtering technique, the polarization technique and the Fourier-transform-based image-processing algorithm. The theoretical analyses and experimental results carried out in this study showed that the monochromatic feature of projected fringe pattern generated using our designed laser-beam-based optical system ensures the use of optical filtering technique for removing the effect of background illumination; the linearly-polarized characteristic makes it possible to employ a polarizer for eliminating the noised signal contributed by multiply-scattered photons; and the high-contrast sinusoidal fringes of the projected structured light provide the condition for accurate reconstruction using one-shot measurement based on Fourier transform profilometry. The proposed method with the portable and stable optical setup may have potential applications of indoor medical scan of human face and outdoor facial recognition without strict requirements of a dark environment and a stable object being observed.

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Section: Theoretical Analysesmentioning

confidence: 99%

Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light

Chen

Yue

et al. 2021

Sensors

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“…Once depth has been captured, the pattern projection images are discarded afterwards. However, error will occur in the conversion process due to difficulties relating to the environment, surface properties and the hardware (Rachakonda et al 2019;Jensen et al 2017;Gupta et al 2011;Scharstein and Szeliski 2003b). Primarily, errors will occur at the pixel classification stage which for example can be caused by reflections or hardware malfunction.…”

Section: Introductionmentioning

confidence: 99%

Regularising disparity estimation via multi task learning with structured light reconstruction

Weld

Cartucho

et al. 2022

Computer Methods in Biomechanics and Biomedical Engineering: Im

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3D reconstruction is a useful tool for surgical planning and guidance. However, the lack of available medical data stunts research and development in this field, as supervised deep learning methods for accurate disparity estimation rely heavily on large datasets containing ground truth information. Alternative approaches to supervision have been explored, such as self-supervision, which can reduce or remove entirely the need for ground truth. However, no proposed alternatives have demonstrated performance capabilities close to what would be expected from a supervised setup. This work aims to alleviate this issue. In this paper, we investigate the learning of structured light projections to enhance the development of direct disparity estimation networks. We show for the first time that it is possible to accurately learn the projection of structured light on a scene, implicitly learning disparity. Secondly, we explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity. We present results which show that MTL with structured light improves disparity training; without increasing the number of model parameters. Our MTL setup outperformed the single task learning (STL) network in every validation test. Notably, in the medical generalisation test, the STL error was 1.4 times worse than that of the best MTL performance. The benefit of using MTL is emphasised when the training data is limited. A dataset containing stereoscopic images, disparity maps and structured light projections on medical phantoms and ex vivo tissue was created for evaluation together with virtual scenes. This dataset will be made publicly available in the future.

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“…Piedra-Cascón et al 14 utilized an infrared sensing face scanner to recover 3D face shape, which performs significantly better than the manual measurement in terms of accuracy and speed. However, the captured face image is usually poor owing to the imaging quality of the infrared camera, 15,16 subsurface scattering, 17,18 and so on, making it difficult work to measure the face 3D geometry accurately. 19,20 That the imaging quality of the infrared camera is worse than that of the visible camera makes the fringes captured through the infrared camera have properties of low contrast and poor sinusoidal feature extensively.…”

Section: Introductionmentioning

confidence: 99%

“…utilized an infrared sensing face scanner to recover 3D face shape, which performs significantly better than the manual measurement in terms of accuracy and speed. However, the captured face image is usually poor owing to the imaging quality of the infrared camera, 15 , 16 subsurface scattering, 17 , 18 and so on, making it difficult work to measure the face 3D geometry accurately 19 , 20 …”

Section: Introductionmentioning

confidence: 99%

Three-dimensional face measurement based on stripe edge detection using infrared light

Feng

Gai

2022

Opt. Eng.

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. Subject to the poor imaging quality of the infrared camera, the projector defocusing, and the subsurface scattering, the classical phase-shifting method is constrained for reconstructing three-dimensional (3D) face geometry. To address this issue, a face measurement method based on stripe edge detection using infrared light is proposed by designing the global codeword correction. Binary fringe instead of sinusoidal fringe is employed to resist the above disturbance. Significantly, an assistant fringe is developed according to the parity of the gray order, which then is utilized to generate the correction orders. Subsequently, the correction orders are employed to guide the decoding by the discriminator of the rising and falling edges in binary patterns. In this way, the 3D face without jump errors can be reconstructed using an infrared structured light system. Experiments verify the effectiveness of our method not only on the human face but also on the standard sphere and models.

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An Error Analysis of Structured Light Scanning of Biological Tissue

Cited by 4 publications

References 28 publications

Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light

Fourier-Transform-Based Surface Measurement and Reconstruction of Human Face Using the Projection of Monochromatic Structured Light

Regularising disparity estimation via multi task learning with structured light reconstruction

Three-dimensional face measurement based on stripe edge detection using infrared light

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