In this paper, we present a new method for 3D measurement and reconstruction for an active vision system. The vision system consists of a light pattern projector and a camera. With initial calibration of the other components in the system, the camera is allowed to change its internal and external parameters during a measurement task. This gives the system the ability to adapt to its environment or task. With this method, the image-to-world transformation is recovered on-line. The 3D scene can then be reconstructed via this transformation. Compared with other existing methods, our approach has the following features: (1) the CCD camera is allowed to undergo an unconstrained motion or change in focus or any of its parameters. In fact, no prior knowledge of the camera parameters is needed by our approach; (2) the computation cost is lower than the traditional method; (3) the method is linear in computation as only a set of linear equations needs to be solved. As a result, the inherent problems existing in nonlinear system, such as divergence or local convergence, are overcome. Simulation and real experiments have been conducted using our active vision system. Our method proves to be valid and the measurement and reconstruction results turn out to be satisfactory.
Machine tools are important factor to determine the surface quality of the workpiece, and the online detection of tool wear is of great significance to the production and processing. In this paper, turning tools are taken as the research object, the tool wear evaluation index is defined, and the online detection system of lathe tool wear based on machine vision is designed. The workpiece processing, tool wear image acquisition, transmission, storage, and processing are completed in this system. Aiming at the problem of tool wear state detection, an adaptive hybrid filtering method is proposed in order to remove noise in the image acquisition process, nonlinear transformation and unsharp masking methods are used to enhance tool wear image quality. The GrabCut improved algorithm is used to segment the tool wear image. The Canny edge detection operator with adaptive double thresholds is used to detect the edge of the tool wear area. Finally, the upper and lower boundaries of the tool wear area are detected by using the Hough transform method, and the wear value of the tool flank is calculated, which is compared with the blunt standard VB=06mm to determine whether the tool needs to be replaced. The accuracy of the detection method is verified by experimental measurement of the surface roughness of the workpiece after machining.
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