基于梯度和流形的超像素分割算法
SUPERPIXEL SEGMENTATION BASED ON GRADIENT AND MANIFOLD
陈彤 1廖闻剑2
作者信息
- 1. 武汉邮电科学研究院 湖北武汉 430074
- 2. 南京烽火天地通信科技有限公司 江苏南京 210019
- 折叠
摘要
当今许多图像处理任务常用超像素作为降维手段和边缘优化的依据.针对现有方法分割数量过于依赖经验和存在离散点的问题,提出一种基于梯度和流形距离的超像素数量的分割方法,自适应估算图像适合的超像素数量,令细节的分割更为精准同时减少背景区域的过分割.以BSDS500数据集进行实验,该方法在各项指标上有较好表现,尤其解决了离散点问题,在紧致度上得到巨大提升.
Abstract
In today's image processing tasks,the super pixel is often used as a method of dimensionality reduction for image as well as the basis of edge optimization.A super-pixel segmentation method based on gradient and manifold distance is proposed to solve the problem of experience-dependent segment number and discrete point of existing methods.It estimated the suitable number of superpixels for images adaptively,making segmentation for details more accurate and reducing over-segmentation for background.Experiments were conducted on BSDS500 dataset.We achieved good performance in various indicators.Escpecially,the elimination of discrete points leads to the compact with huge improvement.
关键词
测地线距离/自适应/梯度/超像素/孤立点消除Key words
Geodesic distance/Adaptive/Gradient/Superpixel/Outlier elimination引用本文复制引用
基金项目
国家重点研发计划项目(2017YFB1400704)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据