用粒子群优化细胞自动机，提升边缘检测适应性
Analysis of the Robustness of an Edge Detector Based on Cellular Automata Optimized by Particle Swarm
- 用粒子群算法优化二维细胞自动机进行边缘检测
- 扩大搜索空间未提升性能，迁移学习效果不显著
- 模型能自适应输入图像，适合复杂自然图像场景
边缘检测在图像处理中至关重要，旨在从图像中提取关键信息。现有检测器常面临难以识别松散边缘、缺乏上下文信息等问题。为此，本文提出一种基于二维细胞自动机的可调边缘检测器，结合元启发式优化与迁移学习技术，以适应不同图像特性。研究分析了优化阶段扩展搜索空间的影响，以及模型在一组自然图像及从中提取的子集上的适应能力。结果表明，在所选图像集上，扩大搜索空间并未带来性能提升；通过多轮实验与验证发现，模型虽能有效适应输入，但迁移学习对性能改善不明显。
原文摘要 · Abstract (English)
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.
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