We address the problem of video stabilization via image registration. We propose a single convolutional neural network, which is simultaneously a dense feature descriptor and a keypoint detector, to find reliable keypoints and their features under each frames. To obtain accurate keypoint localization, the authors leverage the inherent feature hierarchy to restore spatial resolution and low-level details. Compared with traditional detectors, the obtained keypoints are more stable for the following image registration. Based on the correspondences collected from registration results, we propose a technique for video stabilization which is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. In practice, the authors assign each vertex an unique motion vector via their neighboring correspondences and a median filter. The video stabilization is performed on the vertex profiles, which are motion vectors collected at the same vertex location over time. The quantitative and qualitative evaluations show that the proposed registration-based video stabilization method can produce comparable results with the state-of-the-art methods and achieve more stable performance on challenging situations.
The pose of a non-cooperative target represented by point cloud can be estimated through point cloud registration, which is generally performed by searching good correspondences. Seeking correspondences in the context of non-cooperative target pose estimation is a challenging task due to the low texture, noise and occlusion, resulting in a number of outliers in the initial correspondences. In order to gain a high quality set of feature correspondences, we employ a combination of local and global constraints to remove the outliers in initial correspondences. On a local scale, we use simple and low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. In the experiments, we use four groups of different non-cooperative targets to evaluate our algorithm and the results verify that the quality of the correspondence set has been greatly improved by our method and the pose can be accurately estimated.
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.