Image stitching is a cost-effective way to expand the field-of-view of imaging system. The traditional homography-based image stitching uses a global homography transformation matrix for image transformation, which is stable, but only works well for flat scenes, relative far scenes or the scenes which are captured by the camera with rotation only. The AsProjective-As-Possible and Content-Preserving-Warping methods, which are realized by mesh optimization, improve the stitching result to a certain degree, but there is obvious ghost in the near scenes or images which have relatively large parallax. In this paper, an image stitching method which utilizes depth information and mesh optimization is proposed. The feature points are detected and then clustered, and the depth information are used to assign weights to each mesh to compute homography for each mesh respectively. Experiments show proposed method has better results than other methods.
In the image-based industrial inspection field, the imaging distance between the scene and the camera is relatively short, and the field-of-view of the imaging system is too small to meet the requirements of detection. So a close-range image stitching method is needed to get high quality and large field-of-view images. The traditional image stitching method uses a global homography transformation matrix for image stitching, which is stable, but only suitable for flat scenes, remote scenes or the scenes which are captured by the camera with rotation only. The As-Projective-As-Possible and Content- Preserving-Warping methods, which are realized by mesh optimization, improve the stitching result to a certain degree, but there will still have obvious ghost for the close-range scenes and images which have relatively large parallax. In this paper, an image stitching method which utilizes depth information and mesh optimization is proposed. The feature points are detected and clustered, and the depth information and grouping points are used to assign weights to each mesh to compute homography for each mesh respectively. Other state-of-the-art methods are compared with our method, it can be seen that the proposed method can get a better result.
Image stitching is to create a wider viewing angle image with high quality from a series of images which have overlapping regions. It is one of the most important fields of image processing. The traditional global homography method, such as AutoStitch, will be invalid when the scene is not planar or the views differ not purely by rotation. The local homography warping method, which is based on the grid optimization algorithm, such as as-projective-as-possible(APAP) warping can get a better result relative to global homography method, but it deeply relies on the quality and quantity of matching points. In this paper, a new method for low texture scene stitching was proposed which combines point features and line features to compute local warping matrix. So the method can get enough features in low texture region. Our results are compared with APAP and AutoStitch method. The results show that our method have less ghosting and deformation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.