Direct Range Image Processing (DRIP)

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方块一样规则地放置。最近，成像和计算机视觉的世界已经转向使用距离数据，这些数据是通过距离相机或传感器获得的。该范围数据不是规则间隔的，而是稍微随机间隔的，并且可能缺少一些所需的信息。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，必须在数学上将数据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。执行这些计算不仅需要时间，而且这样的计算可能会引入近似误差和数据误表示。为了避免这种不必要的计算和误差引入，本项目提出了一种基于有限元方法(FEMS)的技术，该技术将能够生成特征提取算子，该算子可以直接应用于深度图像，而不需要任何这样的预处理，从而被证明更适合于使用移动机器人的实时视觉。该项目将初步开发和实施用于不规则数据的运算符，并与其他现有技术进行比较，对其进行评估。然后，该项目将解决找出在距离像中发现了哪种类型的特征的问题。这是因为距离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，必须找到这些特征才能确定图像中的特征类型。这是该项目的一个重要方面，因为大多数现有的研究只集中在距离图像中的折痕和跳跃边缘，而不是屋顶或平滑边缘。目前使用的许多目标识别系统都基于分割算法。机器人不是能够准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，并试图将它们与它以前见过的对象进行匹配。为了完善基于有限元的特征提取技术，评估它们对深度图像特征的准确刻画能力，将该技术用于深度数据的分割。该技术与简单的边缘链接算法相结合，应该提供封闭区域并减少过度或不足分割。为了使这项研究适合于实时成像，从而对开发机器人视觉系统有用，要求程序用C语言编写，程序员可以控制垃圾收集，并且可以很容易地测量程序运行所需的时间。总体而言，本项目的目标是提供特征提取算子，可以直接应用于距离图像处理的距离数据，而不需要目前对其他技术至关重要的预处理步骤。这将减少所需的数学计算，从而实现改进的实时视觉，这对于开发机器人视觉系统是有用的。

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.