Analysis of the Robustness of an Edge Detector Based on Cellular Automata Optimized by Particle Swarm

TL;DR

This paper evaluates an edge detector based on cellular automata optimized by particle swarm, examining its robustness and adaptability across natural and specialized image sets, with findings indicating limited benefits from expanded search space and transfer learning.

Contribution

It introduces an adaptable edge detector using cellular automata and meta-heuristic optimization, analyzing its robustness and the effects of search space expansion and transfer learning.

Findings

Expanding the search space was not effective for the chosen images.

The model demonstrated good adaptability regardless of validation.

Transfer learning techniques showed no significant improvements.

Abstract

The edge detection task is essential in image processing aiming to extract relevant information from an image. One recurring problem in this task is the weaknesses found in some detectors, such as the difficulty in detecting loose edges and the lack of context to extract relevant information from specific problems. To address these weaknesses and adapt the detector to the properties of an image, an adaptable detector described by two-dimensional cellular automaton and optimized by meta-heuristic combined with transfer learning techniques was developed. This study aims to analyze the impact of expanding the search space of the optimization phase and the robustness of the adaptability of the detector in identifying edges of a set of natural images and specialized subsets extracted from the same image set. The results obtained prove that expanding the search space of the optimization phase was not effective for the chosen image set. The study also analyzed the adaptability of the model through a series of experiments and validation techniques and found that, regardless of the validation, the model was able to adapt to the input and the transfer learning techniques applied to the model showed no significant improvements.