Matching accuracy plays a vital role in image matching and image recognition. In order to improve the accuracy and robust of image matching, a bidirectional matching algorithm is proposed to delete false matching relationships, so the matching accuracy can be improved. Based on the unique constraint of unilateral matching, the positive matching results from template image to the image to be matched can be obtained. Then the negative matching results from the original image to be matched to the original template image can also be obtained. Now the final bidirectional matching results can be achieved by the intersection of the positive and negative results. Precision ratio is taken as the evaluation indicator. Through various image transformation scenes, experimental results show that the proposed algorithm has a higher precision ratio on the contrast of unilateral matching algorithm. So the proposed bidirectional matching algorithm can improve the precision ratio and robust of unilateral matching algorithm and improve the matching accuracy of image matching.
PMP (Phase measuring Profilometry) is an excellent 3D online measurement method for its high precision. However, the
measuring range is limited. While the rail is so long that far exceeds the measuring limit, the image stitching should be
used to extent it. In this paper, based on the improved Stoilov algorithm, the rail shape is three-dimensionally
reconstructed and the abrasion is detected combines image stitching. Two types of schemes are researched: (1)image
stitching is firstly used on the deformed fringe patterns and then a larger range rail is constructed with Stoilov algorithm;
(2)the three-dimensional construction of two fringe pattern is firstly performed, and then the constructed images are
stitched into longer rail. In this paper, the improved Stoilov algorithm based on statistical approach and stitching
algorithm are analyzed. 3D Peaks function is simulated to verify the two methods, and then three-dimensional rail shape
is recovered based on these two methods and the rail abrasion is measured with the relative precision of higher than
0.1%, which is much higher than traditional methods, such as linear laser scanning.
With the rapid development of high-speed and heavy-load in modern rail transit, the abrasion and surface defect of rail
are getting serious, and the demand of measuring the rail shape and surface defect has been rising. Phase Measuring
Profilometry (PMP), due to the good characters of non-contact, high precision, easy to control automatically etc., is often
used for precise 3D shape reconstruction. In this paper, PMP technology and Stoilov phase shift algorithm are adopted,
three deformed fringe patterns of rail are collected with fixed phase shift between them, and branch cut phase
unwrapping algorithm is used, based on which the three-dimensional surface shape of the rail is reconstructed and the
artificial surface flaws are restored and measured. This method provides a good reference for the precise online detection
of the rail abrasion and surface defect.
In three dimensional detection of the rail shape by Fourier Transform profilometry (FTP), filtering is one of the key links before Fourier transform. The choice of filtering window decides the spectrum overlapping degree of deformed fringes, so as to decide the measurement precision of the rail shape. In this paper, based on wavelet ridge theory the size of the filter window is self-adaptive according to the frequency alternation of deformed fringes. And thus the optimum matching window size is decided, the frequency overlapping is furthest reduced and the measurement precision is improved. Simulation and experiments manifest that self-adaptive filtering can greatly enhance the precision in three dimensional detection, which offers a new thinking and method in rail shape recovery and defect detection.
The defects of wheel sets seriously affect the train operation safety which of problem to be solved. A method of
detecting the wheel sets defect is investigated through the three-dimension profile. The three-dimension profile
reconstruction of wheel sets is realized by combining two-dimension CCD imaging with line laser and encoder. The
theoretical derivation of detecting principle is established, and the factors of influencing the measurement accuracy are
analyzed. An algorithm is set to processing measurement data, the main parameters of wheel set are obtained, including
flange thickness, flange height, vertical wear (QR) and tread wear. The results of simulations and field experiments show
that the proposed method can detect the faults on the wheel correctly, and satisfy the requirements of high efficient and
A novel approach is proposed for obtaining high resolution image of removing the optical aberrations by disturbing the
optical wave-front phase and digital image processing. An optical random phase mask of the phase spectrum
fluctuation corresponds to Kolmogorv distribution is placed between the exit pupil and image plane of optical system to
make the optical aberration image blurred termed the intermediate image. The intermediate image acquired by digital
detector is restored through the blind deconvolution algorithm base on maximum-likelihood estimation technique. The
effect of optical aberrations on restoration image and superresolution performance of image was explored. As a
demonstration to verify the utility of this method, the primary aberrations corresponding to the optical system are
applied, and the image of removing the aberrations by a computer simulation and experiment is shown. The results
suggest that the present method is well suited for improving the imaging quality of the optical system, and partly
removing the diffraction effect of optical system on restoration image.