噪声背景下线状目标的编组提取方法研究

项目名称： 噪声背景下线状目标的编组提取方法研究

项目编号： No.61301277

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 邹勤

作者单位： 武汉大学

项目金额： 26万元

中文摘要： 线状目标提取是一种基本的图像操作,它在计算机视觉与模式识别中具有重要的应用。传统的线状目标提取方法可分为两类，一类基于边缘检测，另一类基于区域分割。然而，在噪声背景下提取不同亮度、不同宽度、形状不规则的线状目标时，单独利用边缘信息或区域信息的方法遇到了严峻挑战。本项目计划研究一种新的基于感知编组的线状目标提取方法，将线状目标提取问题转化为结合边缘信息和区域信息的轮廓编组问题。主要研究内容包括：1）设计针对线状目标的显著度计算模型和算法，实现对不同亮度、不同宽度的线状目标在线性显著度上的统一度量；2）研究基于分类学习的边缘检测算法，实现对噪声背景下线状目标的边缘检测；3）推导结合边缘信息和区域信息(线性显著度)的编组模型，利用图论技术及最优化计算，实现对不规则线状目标的完整提取。该项目的研究成果将形成一种统一的方法，解决对不同亮度、不同宽度、形状不规则的线状目标的提取问题。

中文关键词： 路径投票；阴影消除；卷积网络；线状显著性；线状目标

英文摘要： The extraction of curve structures, also referred to as curves, is a fundamental image operation, which has many applications in computer vision and pattern recognition. Traditional methods for curve structure extraction can be divided into two categories. One is based on edge detection, and the other is based on segmentation. However, great challenges are raised when extracting curve structures with different intensity, different width, and arbitrary shapes from noisy background, by solely using edge information or region information. In this project, a novel method based on perceptual grouping will be studied, where the problem of curve structure extraction is formulated as a problem of contour grouping by combining edge information and region information. First, a saliency model for curve structures is defined to provide a unified solution to produce the linear saliency of curve structures regardless of their intensity or width. Then, a class-oriented learning method for edge detection is exploited to detect faint edges from noisy background. Finally,a grouping model incorporating both edge information and region information(i.e., linear saliency) is studied, and graph techniques and optimization algorithms are employed to extract curve structures with arbitrary shapes. The research of the project will form a

英文关键词： path voting；shadow removal；convolutional network；line saliency；line object