Research On Vision Detection Algorithm For Tracing Printing System

In traditional printing system, many domestic factories detect the accuracy of the printing process by human vision. This method has many shortcomings, such as lack of uniform standard, low detection accuracy, high labor strength and so on. Recent years, along with the development and maturity of machine vision technology, it is possible to use the computer graphics in industrial printing system. In order to develop industry-leading tracking printing system, this thesis mainly studies the algorithmic parts of the system.In this thesis, the structure under laboratory conditions is firstly investigated. Then based on the overall performance of the tracking printing system, a set of practical hardware solution is proposed, and also several important components, such as CCD camera, light source, frame grabber, are described in detail.In tracking printing system, whether the core algorithm is good or bad directly determines the success or failure of the entire system. As the most important part, the detection algorithm should not only ensure the detection accuracy, but also meet the real-time constraint. So the system achieves the tracking algorithm in both horizontal and vertical directions. In the direction of the fabric moving forward, as the printing fabric is particular, a template matching algorithm based on histogram and projection characteristic is presented. Firstly the new method does a rough match by comparing the histogram feature, so as to pick up the regions that the target may probably exit, secondly it does an accurate match by calculating the horizontal and vertical projection. While in the vertical direction, as the template is too large, a template matching algorithm based on the hausdorff distance is presented. Firstly it detects the probable contours by edge detection. Secondly it does an accurate match by one-way hausdorff distance.In order to optimize the algorithm, this paper proposes an adaptive threshold segmentation method. A large number of experiments results illustrate that, compared with the existing image algorithms, the new method can reduce the computation. It can not only ensure the accuracy of the test results, but also enable to meet the real-time constraints of the system.