稀疏采样下非限定场景的图像拼接方法研究

项目名称： 稀疏采样下非限定场景的图像拼接方法研究

项目编号： No.61301221

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： 曾丹

作者单位： 上海大学

项目金额： 25万元

中文摘要： 当前图像拼接技术限于密集采样或单一平面场景，主要受两方面制约:相似内容间的匹配误差；稀疏采样下曲面/平面间相对位移导致的拼接误差。申请课题提出稀疏采样下非限定场景的图像拼接新方法：针对相似内容，研究兼具不变性及唯一性的拓扑相似性度量参量及准则，提出全局匹配算法，去除灰度/梯度局部匹配误差；针对稀疏采样下多平面及曲面场景，研究平面与投影矩阵间变换模型及多平面多投影方法，去除单投影导致的结构误差；提出基于结构反馈的迭代优选拼接方法，包括结构/灰度/梯度的误差度量方法、结构/灰度/梯度差异最小化缝合线、结构反馈迭代配准缝合算法，以避免缺失、重复、变形、错乱等四大结构误差。项目的创新在于：提出了采样及场景无约束的图像拼接方法；拓扑参量的不变性及唯一性、多平面多投影保证了图像配准精度；基于结构反馈的优选缝合可使结构差异最小化，保证了非限定场景拼接精度。研究成果对提高图像拼接效果及工程应用具有积极意义

中文关键词： 图像拼接；图像配准；特征匹配；误匹配去除；最佳缝合线

英文摘要： The effect and application range of image stitching is influenced by two factors: matching error caused by similar contents, and structure error caused by multi-planes or curved surfaces with sparse sampling. To solve these problems and realize engineering applications, a novel method of image stitching of unrestricted scene with sparse sampling is proposed in this project. First, invariant and unique topology similarity measure parameters and measure rules will be studied for global matching which are used to improve matching accuracy. To eliminate image registration errors caused by multi-plane, plane segmentation and respective mapping method based on multi-homography will be studied. To avoid structure error such as false negative, false repeat, structure distortion and position disorder, structure parameters along with intensity and gradient is added into a error cost function, which aims to find the best stitching line which has the smallest difference in structure, intensity and gradient. Finally, we can stitching images of scene with multi-similar-contents， multi-plane and curved surface which are sparse sampled. The novelty of this proposed project is reflected in: the proposed image stitching method can be applied to large scale complicated environment multi-similar-contents and multi-plane without

英文关键词： image stitching；image registration；feature matching；remove mismatching；optimal seam