Adaptive threshold selection for background removal in fringe projection profilometry

Zhang, Wei; Li, Weishi; Yan, Jianwen; Yu, Liandong; Pan, Chengliang

doi:10.1016/j.optlaseng.2016.09.013

Cited by 26 publications

(12 citation statements)

References 17 publications

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“…Three sinusoidal phase-shift fringe patterns with equal phase shifts captured by camera can be mathematically described as:

where

denotes the average intensity,

denotes the intensity modulation, and

denotes the modulating phase to be solved. Combining the above equations, the three variables can be calculated as [ 42 , 43 , 44 ]:

…”

Section: Methodsmentioning

confidence: 99%

Modified Gray-Level Coding Method for Absolute Phase Retrieval

Chen

Luo

et al. 2017

Sensors

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Fringe projection systems have been widely applied in three-dimensional (3D) shape measurements. One of the important issues is how to retrieve the absolute phase. This paper presents a modified gray-level coding method for absolute phase retrieval. Specifically, two groups of fringe patterns are projected onto the measured objects, including three phase-shift patterns for the wrapped phase, and three n-ary gray-level (nGL) patterns for the fringe order. Compared with the binary gray-level (bGL) method which just uses two intensity values, the nGL method can generate many more unique codewords with multiple intensity values. With assistance from the average intensity and modulation of phase-shift patterns, the intensities of nGL patterns are normalized to deal with ambient light and surface contrast. To reduce the codeword detection errors caused by camera/projector defocus, nGL patterns are designed as n-ary gray-code (nGC) patterns to ensure that at most, one code changes at each point. Experiments verify the robustness and effectiveness of the proposed method to measure isolated objects with complex surfaces.

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“…Three sinusoidal phase-shift fringe patterns with equal phase shifts captured by camera can be mathematically described as:

where

denotes the average intensity,

denotes the intensity modulation, and

denotes the modulating phase to be solved. Combining the above equations, the three variables can be calculated as [ 42 , 43 , 44 ]:

…”

Section: Methodsmentioning

confidence: 99%

Modified Gray-Level Coding Method for Absolute Phase Retrieval

Chen

Luo

et al. 2017

Sensors

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show abstract

“…We evaluate our technique with Area Under Curve for Precision and Recall (AUC PR), Dice co-efficient (F1-score) and Area Under Curve for Receive Operating Characteristics (AUC ROC). The probability map is threshold using Ostu thresholding [39], which is mostly used to separate fore-ground from background for calculation of dice co-efficient. Pixels inside the Field Of View (FOV) is counted when we are computing the measures, for fair measurement.…”

Section: Parameters Of Proposed Modelmentioning

confidence: 99%

Generative Adversarial Network for Medical Images (MI-GAN)

2018

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Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.

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“…The above two methods for automatic threshold selection are intended for image segment based on gray-level histogram. The literature [ 8 ] utilizes them in modulation histogram for object segmentation. However, in their work, the background is dark, so invalid points in shadow and background are with low modulation level, and the object is with higher modulation level; only one threshold is enough to segment the object.…”

Section: Related Workmentioning

confidence: 99%

“…Lu et al [ 7 ] proposed a technique to remove shadow points by mapping the 3D results into projector coordinates, and the modulation is not needed. However, this method can only detect shadow caused by the DFP system [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Both Otsu’s method and Ng’s method aim for image segmentation based on intensity histogram. The literature [ 8 ] utilized the automatic thresholding method in modulation histogram for object detection. However, their method can only deal with dark background with low modulation, since the background and shadow are with similar low modulation, while the object is with obviously higher modulation level, and only one threshold is needed to segment the object.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved 3D measurement with a novel preprocessing method in DFP

Xiao

2017

Robot. Biomim.

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Shadow and background are two common factors in digital fringe projection, which lead to ambiguity in three-dimensional measurement and thereby need to be seriously considered. Preprocessing is often needed to segment the object from invalid points. The existing segmentation approaches based on modulation normally perform well in pure dark background circumstances, which, however, lose accuracy in situations of white or complex background. In this paper, an accurate shadow and background removal technique is proposed, which segments the shadow by one threshold from modulation histogram and segments the background by the threshold in intensity histogram. Experiments are well designed and conducted to verify the effectiveness and reliability of the proposed method.

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Adaptive threshold selection for background removal in fringe projection profilometry

Cited by 26 publications

References 17 publications

Modified Gray-Level Coding Method for Absolute Phase Retrieval

Modified Gray-Level Coding Method for Absolute Phase Retrieval

Generative Adversarial Network for Medical Images (MI-GAN)

Improved 3D measurement with a novel preprocessing method in DFP

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