Abstract
Detection of visual landmarks is an important problem in the development of automated, vision-based agents working on unstructured environments. In this paper, we present an unsupervised approach to select and to detect landmarks in images coming from a video stream. Our approach integrates three main visual mechanisms: attention, area segmentation, and landmark characterization. In particular, we demonstrate that an incorrect segmentation of a landmark produces severe problems in the next steps of the analysis, and that by using multiple segmentation algorithms we can greatly increase the robustness of the system. We test our approach with encouraging results in two image sets taken in real world scenarios. We obtained a significant 52% increase in recognition when using the multiple segmentation approach with respect to using single segmentation algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.