2020
DOI: 10.1049/iet-ipr.2019.0854
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Improving 3D reconstruction accuracy in wavelet transform profilometry by reducing shadow effects

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Cited by 14 publications
(6 citation statements)
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“…If the true match error is within 3 pixels, the keypoint pair is deemed to be the right match. (5) if N kp > 1 then (6) Split child node (7) else then (8) Store child node (9) else then (10) Store child node (11) else then (12) if N store > N set then (13) Store the point which has the largest response in each node (14) Figure 3. Its basic principle is to use the mathematical characteristics between the number of feature points in the same area of various shapes of the image to measure the distribution of feature points.…”
Section: Experiments and Resultsmentioning
confidence: 99%
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“…If the true match error is within 3 pixels, the keypoint pair is deemed to be the right match. (5) if N kp > 1 then (6) Split child node (7) else then (8) Store child node (9) else then (10) Store child node (11) else then (12) if N store > N set then (13) Store the point which has the largest response in each node (14) Figure 3. Its basic principle is to use the mathematical characteristics between the number of feature points in the same area of various shapes of the image to measure the distribution of feature points.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…Image registration plays an important role in computer vision. Image registration is widely used in many aspects such as image matching [1][2][3][4][5][6][7], change detection [8,9], 3D reconstruction [10][11][12], guidance [13][14][15], mapping sciences [16][17][18][19][20][21], and mobile robot [22,23]. In general, image registration methods can be mainly divided into two kinds: gray-scale matching methods and feature-based matching methods.…”
Section: Introductionmentioning
confidence: 99%
“…Reference [20] proposed an adaptive 3D reconstruction algorithm of light field based on array images. References [21,22] used wavelet to decompose the image twice to obtain the high-frequency component features of the object. In the virtual reality environment, the two features are fused to output the detection results of weak targets and small targets in complex background.…”
Section: Related Workmentioning
confidence: 99%
“…In projection correlation method, they are generally divided into Fringe Projection Profilometry (FPP) [17][18][19][20] and Speckle Projection Profilometry (SPP) [21][22] .SPP can calculate the out-of-plane displacement by using the common 2D-DIC method to track the highly modulated speckle, it's easy to implement.However, FPP has a complex phase unwrapping process.So SPP is simpler than FPP.In addition, SPP can calculate by only one reference image and one deformed image, which is more suitable for high-speed measurement. However, FPP method, such as phase shifting method [23][24][25] , requires at least three accurate phase shifting images, which greatly reduces the measurement speed.If we use the single fringe pattern FPP method such as Fourier Transform Profilometry(FTP) [26][27][28] and Wavelet Transform Profilometry(WTP) [29][30][31][32] , its accuracy will be lost. This paper combines SPP with 2D-DIC.Out-of-plane deformation is measured by SPP and in-plane deformation is measured by 2D-DIC.In order to perfectly separate the projected speckle and the speckle attached to the surface of the specimen,Fluorescence Digital Image Correlation (FDIC) method is introduced to replace the traditional DIC method.FDIC method was first proposed by berfield [33] for nano scale deformation measurement.…”
mentioning
confidence: 99%