The line-structured light vision sensor is widely used in 3D reconstruction and scanning, quality inspection and other fields. In this technique, the segmentation of light stripe image is an important preprocessing steps, which separates light stripe and extract ROI from an original image. The segmentation of light stripe is the basis of centerline extraction and also affects the efficiency of centerline extraction. In our work, the 3D reconstruction of wood surface needs to be realized through using line-structured light sensor, and then the 3D defects need to be detected. The measured wood has a curved, rough and scattering surface. The acquired light stripe is relative complex. Several algorithms of image segmentation are studied, and the segmentation performance and operation efficiency are compared and analyzed, so as to help us find the optimum technique.
KEYWORDS: Point clouds, Tunable filters, 3D vision, 3D modeling, 3D image processing, Optical filters, 3D acquisition, 3D scanning, Binocular vision, Structured light
The paper focuses on the emerging three-dimensional (3D) vision detection technology using in pavement crack detection, and takes 3D data acquisition, data preprocessing, crack feature extraction and identification as the main lines of idea, then provides a systematic review of 3D vision detection technology development for pavement cracks. Firstly, three commonly used 3D vision imaging techniques for pavement crack detection are analyzed, including measurement principle, measurement steps and technical characteristics. Secondly, 3D point cloud filtering methods are classified and compared. It lays a foundation for subsequent crack feature extraction and crack identification. Finally, the pavement crack detection method based on point cloud data is systematically sorted out. This paper provides reference for relevant researchers and application personnel to comprehensively and systematically understand the 3D vision detection technology of pavement cracks.
The ice cream stick is a kind of wood product with a plane surface. For the qualified stick it has the nearly flat surface. However, due to slender and thin shape the bend defect is prone to occur during machining of ice cream stick. At present, manual technique is often used to identify the bend defect in the industrial fields, which holds many problems such as low efficiency, low automation and unreliable detection result. In the paper, the PCA-based bend feature selection method is proposed which is applied in defect detection of ice cream stick. At first, the 3D data of object surface is obtained based on the three-dimensional (3D) measurement principle of line structured light. Then four kinds of bend features are designed according to the shape characteristics of the light stripe, which include variation coefficient, correlation coefficient, determination coefficient and straightness metric. At last, the bend feature selection is operated based on the PCA-based method. The research results provide valuable reference for the engineering application in the intelligent defect detection.
A method based on the local gradient direction information is proposed to locate the center of circular fringe. The new method is developed which based on the idea that the normal directions of any point on the circular fringe are always pointing towards the center. Besides, the local gradient direction of fringe is related to the normal line of fringe. We deduce theoretically the principle of local gradient direction estimation. Then a new circular fringe center location algorithm is developed with the help of digital image processing technology and statistical ideas. The simulation results show that new method can locate accurately the center of different types of circular fringes with less or without filtering. It also holds good robustness and can meet the requirements of actual engineering application.
In the accuracy measurement of phase from interferometers with adjustable fringe contrast, it needs to estimate the contrast of experimental patterns so as to obtain the interference patterns with the maximum contrast. We develop the Fourier-polar transform and combine the directional projection to estimate the global contrast of carrier fringe pattern. The technique is especially used for low-quality fringe pattern such as low contrast and low signal to noise ratio (SNR) that often appear in the interferometric experiment. An illustrative experiment based on the radial shearing interferometer is given. Results generated from this technique are compared with the derived values from theoretical model, and exemplary agreement between both is demonstrated.
Optical fringe is one of important output information from the optical systems. Some important optical or system parameters can be obtained by analyzing the fringe information from optical system such as interferometer system or diffraction setup. The straight fringe is a kind of optical fringes frequently appearing in Young’s double slit interference and single-slit diffraction and other optical structures. For the information extraction of straight fringes, it is often necessary to calculate the fringe spacing parameters. Popular straight fringes analysis methods include the fringe center method and the Fourier transform method. In addition, some image processing methods realized line detection can also be used to analyze this straight fringes image, which include Hough transform and Radon transform. In this paper, four algorithms for fringe analysis are discussed and compared, which focus on method’s principle, algorithm’s simulation and performance when they be applied to detect the fringes spacing. At the same time, the anti-noise performance of two image processing algorithms including Hough transform and Radon transform are analyzed.
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