e-ACJ: Accurate Junction Extraction For Event Cameras
This work addresses the need for improved feature extraction in event-based vision systems, offering a more detailed alternative to corner detection for applications like image matching and motion analysis, though it is incremental as it adapts an existing method to a new data type.
The paper tackled the problem of extracting junctions with geometrical structure information from event camera data, adapting a frame-based method to directly process asynchronous events and achieving high accuracy in junction location while also determining branch orientations and scales.
Junctions reflect the important geometrical structure information of the image, and are of primary significance to applications such as image matching and motion analysis. Previous event-based feature extraction methods are mainly focused on corners, which mainly find their locations, however, ignoring the geometrical structure information like orientations and scales of edges. This paper adapts the frame-based a-contrario junction detector(ACJ) to event data, proposing the event-based a-contrario junction detector(e-ACJ), which yields junctions' locations while giving the scales and orientations of their branches. The proposed method relies on an a-contrario model and can operate on asynchronous events directly without generating synthesized event frames. We evaluate the performance on public event datasets. The result shows our method successfully finds the orientations and scales of branches, while maintaining high accuracy in junction's location.