Abstract:
Binary descriptors are popular for multi-view matching and robotic navigation. In non-planar scenes, severe scale and viewpoint changes reduce their matching performance.
We suggest using compact spatio-temporal descriptors to encode the varying appearance of selected 3D scene points tracked by a moving camera. First, we track interest points and record their temporal variations at multiple scales.
We validate feature tracks using 3D reconstruction and compress the temporal sequence of descriptors by encoding the most frequent and stable binary values. Finally, we match views with severe scale differences to find multi-scale correspondences.
The spatio-temporal multi-scale approach works with many binary descriptors. We demonstrate the effectiveness of joint multi-scale extraction and temporal reduction by comparing temporal reduction strategies and applying to several binary descriptors.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
