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由区域,边缘和突出性函数驱动的选择性图像分割

Shafiullah Soomro^1,2, Asim Niaz¹, Toufique Ahmed Soomro³

¹Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.

|December 15, 2023

概括

这项研究引入了一种新的图像细分方法,将区域,边缘和突出技术结合起来,以克服活跃轮模型的局限性,提高具有挑战性的图像的准确性.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
计算成像技术的成像

背景情况:

活跃的轮方法由于纹理,颜色或强度变化 (不均性) 而与图像分割作斗争.
现有的方法面临着局部最小值,缓慢的计算和弱边界的挑战,限制了它们的有效性.
目前的技术往往缺乏复杂或实时图像分析所需的精度.

研究的目的:

开发一种先进的图像细分模型,克服传统主动轮方法的局限性.
为了提高对具有不均区域和微妙边界的图像的细分精度.
引入一种灵活的方法,能够对选择性对象进行细分.

主要方法:

一种混合方法,同步基于区域,基于边缘和基于突出性的细分技术.
使用零交叉特征探测器 (ZCD) 进行边缘突出显示和突出区域检测的突出功能.
整合全球调整的签名压力力 (SPF) 术语和水平设置演变,与高斯核进行简化重新启动.

主要成果:

拟议的方法有效地细分了纹理,颜色和强度变化的区域.
具备精确细分图像的能力,图像边界较弱或微妙.
成功执行选择性对象分割,允许用户定义的对象选择.

结论:

同步方法为各种图像细分挑战提供了强大而高效的解决方案.
该方法在对自然图像进行细分时显示出高精度,均和不均.
消除惩罚术语简化了级别设置重新启动过程,提高了计算效率.

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