Analysis of the Robustness of an Edge Detector Based on Cellular Automata Optimized by Particle Swarm
This is an incremental study addressing edge detection problems in image processing, with limited impact due to negative findings on optimization and transfer learning.
The study tackled weaknesses in edge detection, such as detecting loose edges and lacking context, by analyzing an adaptable detector based on cellular automata optimized with particle swarm and transfer learning. The results showed that expanding the optimization search space was ineffective, and transfer learning did not yield significant improvements, with the model adapting to inputs regardless of validation.
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.