Stitching images with parallax for naturalness remains a challenging problem. This paper proposes an image stitching method which preserves the flatness of planes in the scene for a natural look. Our method formulates the alignment of images as the camera parameters and the normal vectors of planes. Given a set of feature point matches, a process of grouping points into different layers and rejecting outliers is introduced. According to the epipolar constraint of corresponding points in two images, the focal length and the pose change of the camera are recovered simultaneously. Then, the normal vectors are estimated from the point pairs. To achieve good alignment and guide the warping of images, the model is combined with the mesh deformation as a global similarity constraint. In addition, bundle adjustment is adopted to maintain the consistency for stitching multiple images. Experiment shows that the proposed approach outperforms some state-of-the-art warps on real-world scenes.

This article deals with the template matching problem, and a weighted smallest deformation similarity measure, which is robust to occlusions, background outliers, and complex deformations. The appearance-based nearest neighbor (NN) matching of points is constructed and the smallest location distance between each point in the template and its matching points is employed to penalize the deformation explicitly. Then, the weights are added to points in the template relied on their likelihood of belonging to the background through NN matching with the points around the target window. Experiments show that the proposed method improves the state-of-the-art performance on real-world scenario benchmarks and can be applied in rough positioning of surface mount technology components.

The role of machine vision detection technology as the core component in the surface mount technology devices (SMDs) cannot be denied. Single in-line package chip is an important type in IC chips and its leads forms varies a lot. This paper proposes an efficient method to soft measure single in-line package chips position and orientation. Chip image is taken by the industrial camera in the form of gray level and the region of interest (ROI) image is selected. In contour image which is extracted from the binary ROI image to get the precision position, Hough transform is used to locate the ranges of the chips leads on both sides and to calculate the accurate positions of the leads corners. Then the other parameters can be calculated as well. We test the performance of our approach by a mount of images in different light conditions taken from the surface mount system.