Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.

翻译：近期，基于深度学习的超像素分割方法在分割效率与性能上均取得了显著进展。然而，在生成严格贴合物体边界且传递丰富视觉显著性信息的超像素方面仍存在重大挑战，尤其是当跨表面颜色相关性可能干扰物体识别时。受神经结构与视觉机制启发，我们提出了一种包含增强筛选模块（ESM）与新型边界感知标签（BAL）的生物网络架构用于超像素分割。ESM通过模拟视觉皮层的交互式投射机制增强语义信息，而BAL则通过模拟视觉皮层细胞的空间频率特性，促进生成具有强边界贴合性的超像素。我们在BSDS500数据集和NYUv2数据集上的评估验证了该方法的有效性。