计算机科学
GSM演进的增强数据速率
人工智能
分割
校准
计算机视觉
任务(项目管理)
对象(语法)
边缘检测
模式识别(心理学)
感知
过程(计算)
目标检测
观察员(物理)
图像(数学)
图像处理
数学
统计
物理
操作系统
经济
生物
神经科学
管理
量子力学
作者
Ge-Peng Ji,Lei Zhu,Mingchen Zhuge,Keren Fu
标识
DOI:10.1016/j.patcog.2021.108414
摘要
Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest. As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task. Inspired by the biological visual perception process when a human observer discovers camouflaged objects, this paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU), which aim to model the visual perception behaviour and achieve effective edge prior and cross-comparison between potential camouflaged regions and background. More importantly, RRU incorporates diverse priors with more comprehensive information comparing to existing COD models. Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets. Especially, compared with the existing top-1 model SINet, ERRNet significantly improves the performance by ∼6% (mean E-measure) with notably high speed (79.3 FPS), showing that ERRNet could be a general and robust solution for the COD task.
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