An Adaptive Threshold for the Canny Edge With Actor-Critic Algorithm

Abstract

We propose a method to automatically select proper values of three thresholds in the Canny edge algorithm. Edge detection is widely used for object recognition, detection, and segmentation. Due to its good performance, the Canny edge algorithm is still widely used among many edge detection algorithms. But, it requires manually selecting three appropriate thresholds for the given image. Some approaches have been proposed for automatically setting thresholds in the Canny edge algorithm. But, they either deal with partial among three entries or only show their performance in a limited range of variation. In natural scenes, images are acquired under various illumination, pose, and weather conditions. This paper proposes a method that can operate in various environments. We formulate the given problem by adopting an actor-critic algorithm. We propose an actor and critic network to solve the problem with an actor-critic algorithm. Also, we suggest a reward configuration based on an edge evaluation network and measure to prevent the reversal between high and low thresholds. The edge evaluation network uses an original image and an edge image as input. We set a negative reward when reversing the high and low thresholds occur. The proposed algorithm can adapt to unseen environments using images without requiring ground truth labels. Experimental results using diverse datasets show the feasibility of the proposed algorithm.

Keywords

• Actor-critic algorithm
• edge detection
• deep reinforcement learning
• deep learning