PCL3D点云超级体素分割算法研究
关键词：超级体素，图像分割，PCL，点云，k-均值
毕业论文设计说明书外文摘要
Title Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds
Abstract
Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpixels, is a widely used preprocessing step in segmentation algorithms. Superpixel methods reduce the number of regions that must be considered later by more computationally expensive algorithms, with a minimal loss of information. Nevertheless, as some information is inevitably lost, it is vital that superpixels not cross object boundaries, as such errors will propagate through later steps. Existing methods make use of projected color or depth information, but do not consider three dimensional geometric relationships between observed data points which can be used to prevent superpixels from crossing regions of empty space. We propose a novel over-segmentation algorithm which uses voxel relationships to produce over-segmentations which are fully consistent with the spatial geometry of the scene in three dimensional, rather than projective, space. Enforcing the constraint that segmented regions must have spatial connectivity prevents label flow across semantic object boundaries which might otherwise be violated. Additionally, as the algorithm works directly in 3D space, observations from several calibrated RGB+D cameras can be segmented jointly. Experiments on a large data set of human annotated RGB+D images demonstrate a significant reductionin occurrence of clusters crossing object boundaries, while maintaining speeds comparable to state-of-the-art 2D methods.
Keywords： VCCS,supervoxel,voexl-cloud,k-means
目 次
1 引言 1
1.1 研究背景与意义 1
1.2 超体素研究现状 1
1.2.1 基于梯度的方法 2
1.2.2 基于图论的方法 3
1.3 本文的安排 5
2 VCCS体素的连通性分割算法 6
2.1 距离测量公式 7
2.1.2 快速点特征直方图 7
2.2 关于PCL 10
2.2.1 PCL的发展 10
2.2.2 PCL的结构与内容 11
2.2.3 PCL下的点云分割 13
3 超体素连通性分割算法的实现 14
3.1 程序结构 14
3.2 重要数据结构和算法 15
3.2.1 kd-tree的介绍 15
3.2．2 八叉树 15
3.3 重要代码 16
3.4 运行 17
4 三文图像分割算法的应用 19
4.1 医学领域 19
4.2 机器人领域 20
结论 21
致谢 22
参考文献 23
1 引言
1.1 研究背景与意义
图像分割是图像处理领域的重点研究内容和难点之一，是图像分析、识别前的重要环节，其应用领域有很多，如在智能移动机器人的场景理解应用中就起着重要的作用。图像处理的相关研究重要前提又需要有好的图像数据，随着三文视觉传感技术的发展，使得同步获取场景的图像信息和三文信息成为可能。传统灰度级图像分割的基本理论可以推广到空间场景的三文图像分割中，三文图像分割方法可充分考虑图像像素数据在图像信息和空间位置上的相关性，比传统二文图像或三文数据具有更高的准确率和可靠性，所以基于三文视觉的图像分割成为新的研究热点，并有着巨大的应用潜力和需求[17]。
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