e-ACJ: Accurate Junction Extraction For Event Cameras

TL;DR

This paper introduces e-ACJ, a novel event-based junction detector that accurately finds junction locations along with their orientations and scales directly from asynchronous event data, enhancing geometric feature extraction for image analysis.

Contribution

It adapts the frame-based a-contrario junction detector to event data, enabling direct, accurate extraction of junctions with scale and orientation information without frame synthesis.

Findings

Successfully detects junction locations with high accuracy.

Provides orientations and scales of junction branches.

Operates directly on asynchronous event data.

Abstract

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.