Image correlations is an important computer vision technique for object detection localization applications, take superresolution optical imaging for example. The cosine similarity is an easy-to-used technique for correlation calculation. In this paper, we show that fast algorithm is available for cosine similarity and the performance of cosine similarity can be affected by background. To avoid the effect of background, gradient of the object image can be employed for robust object detection and localization. Simulation shows that the proposed cosine similarity image correlation algorithm results in high-quality correlation map for noisy image with strong background, which makes it attractive for high-performance object detection and localization.
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.