Abstract
Because many model-based object representation approaches such as active appearance models (AAMs) use a fixed linear appearance model, they often fail to fit to a novel image that is captured in a different imaging condition from that of training images. To alleviate this problem, we propose to use adaptive linear appearance model that is updated by the incremental principal component analysis (PCA). Because the incremental update algorithm uses a new appearance data that is obtained in an on-line manner, a reliable method to measure the quality of the new data is required not to break the integrity of the appearance model. For this purpose, we modified the adaptive observation model (AOM), which has been used to model the varying appearance of the target object using statistical model such as Gaussian mixtures. Experiment results showed that the incremental AAM that uses adaptive linear appearance model greatly improved the robustness to the varying illumination condition when compared to the traditional AAM.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
