KEYWORDS: Detection and tracking algorithms, Network on a chip, RGB color model, Venus, Distributed interactive simulations, Binary data, Evolutionary algorithms, Algorithm development
When extracting disparity map, for the high non-occlusion region error rate and low efficiency of stereo matching algorithms, we defined internal similarity (IS) of neighbor pixels in reference color image, and in the color and sub-pixel space, we defined external similarity (ES) of candidate matching pixels between the reference and target images, and given the calculation methods of the internal similarity and external similarity. We discussed the relationship between the internal similarity and external similarity, and taken the internal similarity as the aggregation degree of the external similarity, and the internal and external similarity aggregation (IESA) algorithm was proposed to aggregate the external similarity by the internal similarity along eight directions, after that, the disparity map was calculated by the winner take all (WTA) algorithm, which was used to search the disparity corresponding to the maximum aggregation similarity. Finally, the box plot filter (BPF) was proposed to smooth the disparity map. Experiments on the Middlebury stereo datasets and the extend datasets show that the proposed algorithms achieve the state-of-the-art results.
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.