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基于 EM 驱动的可变形和可旋转指向性探测掩模的仿生轮廓提取。

Bio-inspired contour extraction via EM-driven deformable and rotatable directivity-probing mask.

机构信息

Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, 20224, Taiwan.

AI Research Center, National Taiwan Ocean University, Keelung City, 20224, Taiwan.

出版信息

Sci Rep. 2022 Jul 19;12(1):12309. doi: 10.1038/s41598-022-16040-6.

Abstract

This paper presents a novel bio-inspired edge-oriented approach to perceptual contour extraction. Our method does not rely on segmentation and can unsupervised learn to identify edge points that are readily grouped, without invoking any connecting mechanism, into object boundaries as perceived by human. This goal is achieved by using a dynamic mask to statistically assess the inter-edge relations and probe the principal direction that acts as an edge-grouping cue. The novelty of this work is that the mask, centered at a target pixel and driven by EM algorithm, can iteratively deform and rotate until it covers pixels that best fit the Bayesian likelihood of the binary class w.r.t a target pixel. By creating an effect of enlarging receptive field, contiguous edges of the same object can be identified while suppressing noise and textures, the resulting contour is in good agreement with gestalt laws of continuity, similarity and proximity. All theoretical derivations and parameters updates are conducted under the framework of EM-based Bayesian inference. Issues of stability and parameter uncertainty are addressed. Both qualitative and quantitative comparison with existing approaches proves the superiority of the proposed method in terms of tracking curved contours, noises/texture resilience, and detection of low-contrast contours.

摘要

本文提出了一种新颖的基于生物启发的边缘导向方法,用于感知轮廓提取。我们的方法不依赖于分割,可以无监督地学习识别易于分组的边缘点,而无需调用任何连接机制,将其分组为人类感知到的对象边界。这一目标是通过使用动态掩模来统计评估边缘之间的关系,并探测作为边缘分组线索的主要方向来实现的。这项工作的新颖之处在于,掩模以目标像素为中心,并由 EM 算法驱动,可以迭代地变形和旋转,直到它覆盖最符合目标像素的二进制类的贝叶斯似然的像素。通过创建扩大感受野的效果,可以识别同一对象的连续边缘,同时抑制噪声和纹理,得到的轮廓与连续性、相似性和邻近性的格式塔定律很好地吻合。所有的理论推导和参数更新都是在基于 EM 的贝叶斯推理框架下进行的。稳定性和参数不确定性问题得到了解决。与现有方法的定性和定量比较证明了所提出的方法在跟踪弯曲轮廓、抗噪声/纹理能力以及检测低对比度轮廓方面的优越性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a133/9296603/af8d3056539f/41598_2022_16040_Fig1_HTML.jpg

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