Robust Fringe Projection Profilometry via Sparse Representation

Budianto,; Lun, Daniel Pak-Kong

doi:10.1109/tip.2016.2530313

Cited by 30 publications

(6 citation statements)

References 59 publications

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“…The maximum sensitivity corresponds to the smallest possible fringe period, which results in many nominally identical fringes being present in the captured images, and the necessity to "unwrap" the phase. For continuous surfaces, the simplest approach is to use localized intensity alterations as markers, in one or several locations [455][456][457][458][459][460], or even on every fringe [461,462], and apply spatial phase unwrapping from the reference point or line to recover the positions. In well-controlled geometries where the sample can be placed in the same position as the calibration surface, it is also possible to refer to calibration data for the correct geometry reconstruction.…”

Section: Phase-measuring Methodsmentioning

confidence: 99%

Deflectometry for specular surfaces: an overview

Burke¹,

Pak²,

Höfer³

et al. 2022

Preprint

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Deflectometry as a technical approach to assessing reflective surfaces has now existed for almost 40 years. Different aspects and variations of the method have been studied in multiple theses and research articles, and reviews are also becoming available for certain subtopics. Still a field of active development with many unsolved problems, deflectometry now encompasses a large variety of application domains, hardware setup types, and processing workflows designed for different purposes, and spans a range from qualitative defect inspection of large vehicles to precision measurements of microscopic optics. Over these years, many exciting developments have accumulated in the underlying theory, in the systems design, and in the implementation specifics. This diversity of topics is difficult to grasp for experts and nonexperts alike and may present an obstacle to a wider acceptance of deflectometry as a useful tool in other research fields and in the industry.This paper presents an attempt to summarize the status of deflectometry, and to map relations between its notable "spin-off" branches. The intention of the paper is to provide a common communication basis for practitioners and at the same time to offer a convenient entry point for those interested in learning and using the method. The list of references is extensive but definitely not exhaustive, introducing some prominent trends and established research groups in order to facilitate further selfdirected exploration by the reader.

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Section: Phase-measuring Methodsmentioning

confidence: 99%

Deflectometry for specular surfaces: an overview

Burke¹,

Pak²,

Höfer³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In Ref. [32], a FPP algorithm using the sparse dictionary learning and sparse coding techniques is proposed, which can improve the robustness of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic projection theory for fringe projection profilometry

Sheng

Zhang

2017

Appl. Opt.

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Fringe projection profilometry (FPP) has been widely used for three-dimensional reconstruction, surface measurement, and reverse engineering. However, FPP is prone to overexposure if objects have a wide range of reflectance. In this paper, we propose a dynamic projection theory based on FPP to rapidly measure the overexposed region with an attempt to conquer this challenge. This theory modifies the projected fringe image to the next better measurement based on the feedback provided by the previously captured image intensity. Experiments demonstrated that the number of overexposed points can be drastically reduced after one or two iterations. Compared with the state-of-the-art methods, our proposed dynamic projection theory measures the overexposed region quickly and effectively and, thus, broadens the applications of FPP.

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“…These elements are called as atoms and they compose a dictionary. Patch-based sparse representation model has been successfully used in various image processing and computer vision tasks [41,42]. However, patch-based sparse representation model usually suffers from some limits, such as dictionary learning with great computational complexity and neglecting the correlations between sparsely-coded patches [44].…”

Section: Group Sparse Representationmentioning

confidence: 99%

Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation

Zha¹,

Zhang²,

Wang³

et al. 2017

Preprint

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Rank minimization methods have attracted considerable interest in various areas, such as computer vision and machine learning. The most representative work is nuclear norm minimization (NNM), which can recover the matrix rank exactly under some restricted and theoretical guarantee conditions. However, for many real applications, NNM is not able to approximate the matrix rank accurately, since it often tends to over-shrink the rank components. To rectify the weakness of NNM, recent advances have shown that weighted nuclear norm minimization (WNNM) can achieve a better matrix rank approximation than NNM, which heuristically set the weight being inverse to the singular values. However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM. In this paper, we propose a scheme to analyze WNNM and NNM from the perspective of the group sparse representation. Specifically, we design an adaptive dictionary to bridge the gap between the group sparse representation and the rank minimization models. Based on this scheme, we provide a mathematical derivation to explain why WNNM is more feasible than NNM. Moreover, due to the heuristical set of the weight, WNNM sometimes pops out error in the operation of SVD, and thus we present an adaptive weight setting scheme to avoid this error. We then employ the proposed scheme on two low-level vision tasks including image denoising and image inpainting. Experimental results demonstrate that WNNM is more feasible than NNM and the proposed scheme outperforms many current state-of-the-art methods.

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Robust Fringe Projection Profilometry via Sparse Representation

Cited by 30 publications

References 59 publications

Deflectometry for specular surfaces: an overview

Deflectometry for specular surfaces: an overview

Dynamic projection theory for fringe projection profilometry

Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation

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