Research On Image Matching Methods For Assembly Process Of Electronic Products

Image matching is a key link in many computer vision fields such as target detection,image mosaic,3D reconstruction and visual positioning.When assembling electronic products in the industry,it is necessary to match the template image with LCD screen or IC cable.Images of the same product captured at different times,at different viewing angles,or by different cameras often have problems such as interference information,image rotation or image distortion,resulting in lower accuracy and robustness of image matching and bad effects on subsequent processes.Therefore,the research on image matching in complex scenes has become a research hotspot in the field of computer vision applications.In order to solve the effects of interference,rotation and deformation of image matching in complex scenes,this thesis has carried out indepth research from the following three aspects,and the work is summarized as follows:Under simple conditions,the basic research of image matching is proposed,and a strategy based on adaptive Canny algorithm and improved RANSAC algorithm is proposed.Based on the classical SIFT algorithm to extract image feature points,an adaptive threshold Canny algorithm is proposed to detect the edges of the image and remove the unstable feature points that coincide with the edge points.Then,an improved RANSAC algorithm is proposed,which introduces RMSE as the criterion for mismatching points.And the algorithm is used to eliminate the mismatched pairs obtained in the matching process.The matching accuracy is compared with other matching strategies,and the robustness of the algorithm is verified under various noise conditions.Under the condition of image rotation,the reason why the traditional SURF algorithm is not strong against image rotation is analyzed.A composite feature matching algorithmcombining SURF feature and DAISY descriptor is proposed.The calculation method of Hessian matrix in SURF algorithm is used to detect feature points to ensure the fastness of the algorithm.Then the main direction allocation process of the DAISY descriptor is improved to make it rotate invariant,and the structural characteristics of the improved DAISY descriptor are combined with the SURF algorithm to generate a new feature description with rotation invariant characteristics.Finally,the rotation invariance and time efficiency of the algorithm are effectively verified,which improves the rotational robustness of image matching.Under the condition of image deformation,according to the degree of industrial image deformation,it is classified as the afine transformation between images,and an image affine transformation model is constructed.An affine image matching algorithm based on MSER algorithm and probability model is proposed.Firstly,based on the MSER algorithm,the ellipse region extraction and region fitting are carried out for the image with affine transformation,then the normalization of the extraction region is completed,which is used as the rough matching between the images.Then a probabilistic model is proposed to fine match the image and the affine transformation parameters are calculated.Finally,using the real industrial image sample and Mikolajczyk image dataset,the algorithm is compared with the classical algorithm in the scenes of affine change,illumination change and scale change,which improves the reliability of image matching algorithm under deformation conditions.