The perspective effect is common in real optical systems using projected patterns for machine vision applications. In the past, the frequencies of these sinusoidal patterns are assumed to be uniform at different heights when reconstructing moving objects. Therefore, the error caused by a perspective projection system becomes pronounced in phase-measuring profilometry, especially for some high precision metrology applications such as measuring the surfaces of the semiconductor components at micrometer level. In this work, we investigate the perspective effect on phase-measuring profilometry when reconstructing the surfaces of moving objects. Using a polynomial to approximate the phase distribution under a perspective projection system, which we call a polynomial phase-measuring profilometry (P-PMP) model, we are able to generalize the phase-measuring profilometry model discussed in our previous work and solve the phase reconstruction problem effectively. Furthermore, we can characterize how the frequency of the projected pattern changes according to the height variations and how the phase of the projected pattern distributes in the measuring space. We also propose a polynomial phase-shift algorithm (P-PSA) to correct the phase-shift error due to perspective effect during phase reconstruction. Simulation experiments show that the proposed method can improve the reconstruction quality both visually and numerically.
This article [Opt. Eng.. 51, , 097001 (2012)] was originally published on 13 Sep. 2012 with an error on p. 6, column 2, line 1. There was a stray multiplication symbol before the equation. The corrected line should appear as follows:
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"...function {where L(x,y)=100exp[(x−128 220 ) 2 +(y−128 220 ) 2 ]} ."
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The paper was corrected online on 18 Sep 2012. The article appears correctly in print.
Uneven illumination is a common problem in practical optical systems designed for machine vision applications, and it leads to significant errors when phase-shifting algorithms (PSA) are used to reconstruct the surface of a moving object. We propose an illumination-reflectivity-focus model to characterize this uneven illumination effect on phase-measuring profilometry. With this model, we separate the illumination factor effectively and consider the phase reconstruction from an optimization perspective. Furthermore, we formulate an illumination-invariant phase-shifting algorithm (II-PSA) to reconstruct the surface of a moving object under an uneven illumination environment. Experimental results show that it can improve the reconstruction quality both visually and numerically.
Uneven illumination is a common problem in real optical systems for machine vision applications, and it contributes
significant errors when using phase-shifting algorithms (PSA) to reconstruct the surface of a moving
object. Here, we propose an illumination-reflectivity-focus (IRF) model to characterize this uneven illumination
effect on phase-measuring profilometry. With this model, we separate the illumination factor effectively, and
then formulate the phase reconstruction as an optimization problem. To simplify the optimization process, we
calibrate the uneven illumination distribution beforehand, and then use the calibrated illumination information
during surface profilometry. After calibration, the degrees of freedom are reduced. Accordingly, we develop
a novel illumination-invariant phase-shifting algorithm (II-PSA) to reconstruct the surface of a moving object
under an uneven illumination environment. Experimental results show that the proposed algorithm can improve
the reconstruction quality both visually and numerically. Therefore, using this IRF model and the corresponding
II-PSA, not only can we handle uneven illumination in a real optical system with a large field of view (FOV),
but we also develop a robust and efficient method for reconstructing the surface of a moving object.
In this paper, an image matching algorithm combining a SVD matching approach and scale invariant measure is proposed to relate images with large-scale variations. To obtain a better performance on handling redundant points, we modify the SVD matching approach which enforces the condition of minimal distance between the structures of point patterns at the same time ensures the likeliness of the matched points. Together with the adoption of scale invariant features, the proposed method can match features undergoing significant scale changes and provide a set of matches containing a high percentage of correct matches without any statistical outlier detection.
This paper presents a novel algorithm for line segment matching over two views. Two image pyramids are built by applying wavelet decomposition to the two images. After defining supporters for a line segment to be the edge points lying close to it, line segments are matched from the coarsest to the finest level. At each level the supporters are matched using the cross-correlation techniques. This method can match line segments over two uncalibrated views, in which the line segments need not be the images of the same section of a 3D line segment. The hierarchical strategy helps to reduce the computational complexity. Wavelet is adopted to build the hierarchical frame for its built-in multi-scale structure and fast decomposition algorithm. Furthermore, it overcomes the flattening-out problem in the traditional multi-scale Gaussian pyramid technique. Experiments on real image pairs are given to demonstrate the effectiveness and the robustness of our algorithm.
In this paper, the classical RANSAC approach is considered for
robust matching to remove mismatches (outliers) in a list of
putative correspondences. We will examine the justification for
using the minimal size of sample set in a RANSAC trial and propose
that the size of the sample set should be varied dynamically
depending on the noise and data set. Using larger sample set will
not increase the number of iterations dramatically but it can
provide a more reliable solution. A new adjusting factor is added
into the original RANSAC sampling equation such that the equation
can model the noisy world better. In the proposed method, the noise
variances, percentage of outliers and number of iterations are all
estimated iteratively. Experimental results show that the estimated
parameters are close to the ground truth. The modification can also
be applied to any sampling consensus methods extended from RANSAC.
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