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Direct Range Image Processing (DRIP)

直接范围图像处理 (DRIP)

基本信息

批准号：
EP/C006283/1
负责人：
Sonya Coleman
金额：
$ 16.05万
依托单位：
University of Ulster
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FC006283%2F1
关键词：
Direct Range Image Processing DRIP

项目摘要

Edge detection or, more commonly, feature extraction can be readily performed on intensity images where the pixels are regularly placed like squares on a chessboard. More recently, the world of imaging and computer vision has moved towards the use of range data, obtained using a range camera or sensor. This range data is not regularly spaced, but instead is slightly randomly spaced and some of the required information may be missing. In order to perform any type of image processing, such as feature extraction or segmentation, on such irregularly spaced data, the data must be re-aligned mathematically on to a regular lattice, and in some cases the missing data is reconstructed. It not only takes time to perform these calculations, but such calculations can introduce approximation errors and data mis-representations. To avoid this unnecessary computation and error introduction, this project proposes a technique based on the use of finite element methods (FEMs) that will enable feature extraction operators to be generated that can be applied directly to the range image without any such pre-processing, thus proving to be more appropiate for real-time vision with the application of moving robots. This project will initially develop and implement the operators for use on irregular data and evaluate them in comparison to other existing techniques available. The project will then address the issue of finding out what type of feature has been found in the range image. The reason for this is that range image contain various types of edges: roof, jump, crease and smooth edge. Each of these features has different characteristics that must be found in order to determine the type of feature in the image. This is an important aspect of this project as most existing research focuses only on finding crease and jump edges in range images and not roof or smooth edges.Many object recognition systems used today are based on segmentation algorithms. Rather than a robot being able to determine precisely all the detail of a scene, it segments the scene into recognisable regions or objects and tries to match them with objects that it has seen before. On perfecting the finite element based feature extraction techniques and evaluating their ability to accurately characterise the features found in the range images, this technique will be used for segmentation of range data. The technique, combined with a simple edge-linking algorithm, should provide enclosed regions and reduce over or under segmentation.In order for this research to be appropriate for real-time imaging and hence useful for developing robot vision systems, it is required that the programs are coded in the C++ programming language, where the programmer has control of garbage collection and the time that it takes the program to run can be easily measured.Overall, this project aims to provide feature extraction operators that can be applied directly to range data for range image processing without the pre-processing steps that are currently essential to other techniques. This will reduce the mathematical computation required and thus enable improved real-time vision that can be useful for developing robot vision systems.

边缘检测或更常见的特征提取可以很容易地在强度图像上执行，其中像素像棋盘上的正方形一样规则地放置。最近，成像和计算机视觉的世界已经转向使用使用范围相机或传感器获得的范围数据。该范围数据不是规则间隔的，而是稍微随机间隔的，并且可能丢失一些所需的信息。为了对这种不规则间隔的数据执行任何类型的图像处理，例如特征提取或分割，必须将数据在数学上重新对准到规则网格上，并且在某些情况下重建丢失的数据。它不仅需要时间来执行这些计算，但这样的计算可以引入近似误差和数据错误表示。为了避免这种不必要的计算和错误引入，该项目提出了一种基于使用有限元方法（FEM）的技术，该技术将使生成的特征提取运算符能够直接应用于距离图像，而无需任何此类预处理，从而证明更适合移动机器人应用的实时视觉。该项目最初将开发和实施用于非常规数据的运算符，并与其他现有技术进行比较。然后，该项目将解决找出在距离图像中发现的特征类型的问题。这是因为深度图像包含各种类型的边缘：屋顶，跳跃，折痕和平滑边缘。这些特征中的每一个都具有不同的特征，必须找到这些特征以确定图像中的特征类型。这是这个项目的一个重要方面，因为大多数现有的研究只集中在寻找折痕和跳跃边缘的范围图像，而不是屋顶或光滑的边缘。机器人不能精确地确定场景的所有细节，而是将场景分割成可识别的区域或对象，并试图将它们与之前见过的对象进行匹配。在完善基于有限元的特征提取技术并评估其准确识别深度图像中发现的特征的能力之后，该技术将用于深度数据的分割。该技术，结合一个简单的边缘连接算法，应提供封闭的区域，并减少过度或不足segmentation.In为了使这项研究是适合于实时成像，因此对开发机器人视觉系统是有用的，这是需要的程序是在C++编程语言编码，其中程序员可以控制垃圾收集，并且可以容易地测量程序运行所花费的时间。总的来说，该项目旨在提供特征提取算子，这些算子可以直接应用于距离数据，用于距离图像处理，而无需目前对其他技术必不可少的预处理步骤。这将减少所需的数学计算，从而实现改进的实时视觉，这对开发机器人视觉系统非常有用。