Research On The Key Technology Of Veneer Defect Image Detection And Patching Based On Variational PDE

Veneer quality has a directly influences on the strength, the surface quality and level of man-made sheets which are made by veneer, at present, in order to improve the level of veneer and the rate of wood utilization, we usually use artificial methods to detect veneer defects and cut or repair veneer defects, because of low level of automation, labor-intensive and low productivity, which have a serious impact on economic benefits and also increase the production cost. So bringing the machine vision and robotic technology into production can be effectively overcome the shortcomings which caused by artificial repair, and can improve the level of automation of the wood-based panels industry, which has an important academic significance and application value. The paper is based on the image processing of variational and partial differential equations(PDE), image restoration techniques and robotics, combining the characteristics of veneers they can have an effective identification and repair about the defects of veneer surface and then form the methods of rapid detection,digging and patching of knots. The main contents are as follows:In this paper, it improves the model of the C-V and solves the limitations about multi-objective segmentation and complex background to fit the multiple objects segmentation of veneer defect image. First, combining the background filling technology, improved C-V model with the AOS semi-implicit method, the paper puts forward segmentation algorithm of multi-phase level veneer defect which based on improved C-V model of AOS and the coupling of background filling,solves the multi-objective automatic segmentation problem of veneer or wood images which have defects. Second, as the collection images from systems are mostly vector images or color images, about veneer vector or color image for defect segmentation problems, it puts forward image segmentation method of veneer defect vector which based on improved multiphase vector C-V model of AOS and background filling, process veneer vector image as a whole and achieve multi-objective segmentation of veneer defect color image.Third, about the defect color image of the veneer with texture, combining multi-channel Gabor filter with improved vector C-V model,it puts forward color image segmentation method of veneer defect which based on improved vector C-V model and Gabor filtering,it also solves the multi-objective identification problem and get segmentation of image which recognition results and original image are the same, therefore generates repaired color mask image of veneer defect.About the defect of veneer knot for a variety of shape, especially the knots which have concave areas and the background color is similar to the color of veneer defect,the edge is unclear such kind of multi-objective recognition problems which caused recognition difficulties, in this paper, it combines the active contour model which based on edge and region, and puts forward multi-objective detection method of active contour model based on a fast global minimization and then the use of the numerical calculation in the dual form, it greatly reduces the calculation and improves the segmentation speed and realizes effective testing of the veneer knot objects under the complex texture background.For the defect veneer image with rich texture, the paper uses the image decomposition method based on variational and PDE to inspecting the defect of veneer. First, based on the ROF model, combining image decomposition model of higher derivative, the paper puts forward a veneer defect image decomposition model of eliminating ladder effect, using the Half-Quadratic Regularization method, we solve this model and get effective method of decomposition of veneer defect image, therefore protect the edge of the structure image and can better extract the texture features.Second, combine the AAFC model and TV with the general form, the paper puts forward a new model of decomposition which joints image structure and texture decomposition and coupling of edge detection. The paper decomposes the part of image structure and the texture of defect veneer image effectively, at the same time get a better edge detection result.In order to apply image restoration theory and methods to the automation repair of the veneer knots on the surface defect images. The paper puts forward an improved BSCB algorithm, and gets a relatively good result of repairing veneer knot images of non-textured.For veneer knots which is larger regional or texture is more complex,the paper puts forward a new restoration algorithm of veneer defect image which based on coupling improved BSCB algorithm and image restoration algorithm of sample block, and realizes restoration for veneer defect image,and achieves a better restoration result.For the limitations of single image restoration method, the paper proposed a image restoration method of veneer defects based on image decomposition. First, improved VO model and realized effectively decomposing the color image of veneer defects, got the texture and structure parts of defect veneer images. Then, adopted the improved BSCB algorithm and repaired the structure parts of veneer images; the paper selects Criminisi algorithm based on the sample for repairing the texture parts. At last, overlay and synthesis the restored images to achieve better restoration results.At last, the paper puts forward a design scheme and develops the robot veneer defect detection and repair system which is based on the machine vision, then it analysis system component and working theory.