Classification of reflectors with an ultrasonic sensor for mobile robot applications

This paper describes a sensor model made up of four ultrasonic transducers able to classify reflectors (wall, edge or corner) in specular environments. The main goal has been to effect the classification from a single reading cycle: emission of ultrasounds and reception of echoes (measuring only times of flight—TOFs). Working from the four TOFs obtained after a single emission of ultrasounds (thereby facilitating its practical implementation in a mobile robot, when readings are taken while the robot is moving), an algorithm has been proposed for discriminating between edge and plane type reflectors. The configuration of the four transducers enabled dependent discriminating functions to be determined directly from the quadratic terms of the TOFs, without the need for previous geometric transformations. Special attention was given to the effect of the separation between the sensor transducers and the reading-associated noise. Finally, some considerations have been pointed out about the possibility of two transducers emitting, so allowing discrimination between walls and corners.     

A corner differentiation algorithm by a single sonar sensor for mobile robots

Ultrasonic sensors have been widely applied in mobile robots to obtain environmental information and avoid obstacles. In general, a typical domestic environment consists of planes, edges and corners. It is usually difficult to distinguish a plane from a corner directly with a single ultrasonic sensor. To overcome this difficulty, a corner differentiation algorithm for a single ultrasonic sensor is proposed in this paper. The algorithm is based on the features of all of the actual reflection points from the environment obtained by a reflection search algorithm from which the corners are realized by a corner differentiation algorithm. The experimental results show that the developed algorithm can successfully detect all planes and corners. Furthermore, an environmental map can be built based on the information obtained on planes and corners. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

三角网格曲面角点的鲁棒性检测算法

为有效检测三角网格曲面上的角点特征,提出一种基于最小主曲率的角点检测算法.首先通过计算网格顶点处的最小主曲率,利用加权最小主曲率定义角点特征函数,并计算角点特征值;然后利用迭代阈值法自动产生检测阈值,以去除噪声和特征不明显的角点;最后采用非极大值抑制法消除局部邻域内的角点聚簇获取特征明显的角点.在此基础上,在多个尺度下分别计算每个网格顶点处的角点特征值,并通过加权将其合并成多尺度角点特征值,新的角点特征值使得角点检测算法具有较高的稳定性和鲁棒性.通过重复检测率实验和部分重叠曲面的配准实验,验证了文中算法的有效性与实用性.     

改进的Harris亚像素角点快速定位

针对Harris算法检测角点存在偏差、运算慢、像素级精度难于满足实际应用需要等问题,改进了Harris角点检测方法。该方法在Harris提取角点过程中,通过两次角点筛选,剔除非角点和伪角点,利用角点响应函数执行非极大值抑制,以局部角点响应函数最大值的像素点作为初始角点,并以该初始角点为中心,以一定半径搜索角点簇,采用最小二乘法加权角点簇与待求角点的欧几里得距离,精化初始角点坐标,从而实现Harris亚像素角点准确快速定位。实验结果表明了该方法的有效性和实用性。     

多特征复合的角点提取方法

提出了一种利用图象灰度特征和边界轮廓点特征相复合的角点检测方法；首先在SUSAN特征检测原则基础上，提出了基于图象灰度特征的快速自适应特征检测方法，用以提取不同图象对比度下目标轮廓上的初角点，利用边沿元对这些包含了部分边缘点的初角点沿边绝缘方向跟踪排序后，再由根据图象边缘特征检测的边界方向变化情况来确定角点位置，同时剔除由于图象数字化而导致的虚假角点，这种方法克服了单一特征提取角点所带来的弊病，提高了角点检测的精度，抗噪能力强，运算量较小，适于实时实现。     

A corner-looping based tool path for pocket milling

In milling around corners, cutting resistance rises momentarily due to an increase of cutter contact length. NC tool path generation in dealing with sharp corners thus requires special consideration. This paper describes an improved NC tool path pattern for pocket milling. The basic pattern of the improved tool path is a conventional contour-parallel tool path. Bow-like tool path segments are appended to the basic tool path at the corner positions. When reaching a corner, the cutter loops around the appended tool path segments so that corner material is removed progressively in several passes. By using the corner-looping based tool path, cutter contact length can be controlled by adjusting the number of appended tool path loops. The procedures of creating the improved tool path for different corner shapes are explained. The proposed tool path generation was implemented as an add-on user function in a CAD/CAM system. Cutting tests were conducted to demonstrate and verify the significance of the proposed method.     

基于概率密度梯度方向的角点重定位技术

由于各种外界因素的影响,角点检测算法检测的角点可能出现位置偏移。在分析该现象的基础上,提出对角点进行重新的定位。首先,通过计算角点的二阶模板值来进行角点模糊定位;其次,引入像素点的概率密度梯度方向,通过比较角点和角点邻域内的像素点的概率密度梯度方向的关系来判断错误角点,并找出所有的候选替代点;最后,根据角点和候选像素点之间的关系,寻找出新的角点。通过实验对比发现该算法能正确地对发生位置偏移的角点进行重新定位。     

Autocalibration of multiprojector CAVE-like immersive environments

In this paper, we present the first method for the geometric autocalibration of multiple projectors on a set of CAVE-like immersive display surfaces including truncated domes and 4 or 5-wall CAVEs (three side walls, floor, and/or ceiling). All such surfaces can be categorized as swept surfaces and multiple projectors can be registered on them using a single uncalibrated camera without using any physical markers on the surface. Our method can also handle nonlinear distortion in the projectors, common in compact setups where a short throw lens is mounted on each projector. Further, when the whole swept surface is not visible from a single camera view, we can register the projectors using multiple pan and tilted views of the same camera. Thus, our method scales well with different size and resolution of the display. Since we recover the 3D shape of the display, we can achieve registration that is correct from any arbitrary viewpoint appropriate for head-tracked single-user virtual reality systems. We can also achieve wallpapered registration, more appropriate for multiuser collaborative explorations. Though much more immersive than common surfaces like planes and cylinders, general swept surfaces are used today only for niche display environments. Even the more popular 4 or 5-wall CAVE is treated as a piecewise planar surface for calibration purposes and hence projectors are not allowed to be overlapped across the corners. Our method opens up the possibility of using such swept surfaces to create more immersive VR systems without compromising the simplicity of having a completely automatic calibration technique. Such calibration allows completely arbitrary positioning of the projectors in a 5-wall CAVE, without respecting the corners.     

Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach

Reliable corner detection is an important task in determining shape of different regions in an image. To detect corners in a gray level image under imprecise information, an algorithm based on fuzzy set theoretic model is proposed. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, etc., usually result in missing of significant curvature junctions (corners). Fuzzy set theory based modeling is well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model image properties in fuzzy frame work for reliable decision making. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm.     

二值图像中拐点的实时检测算法

鉴于数字图像中的拐点通常成为重要的信息载体,因此准确、稳定和实时地检测出拐点便成为拐点检测算法面临的主要问题,针对该问题,提出了一种新的二值图像中拐点的实时检测算法。该算法与传统基于边界链码的拐点检测算法不同,其是首先构建像素的k(k>8)邻域,并将图像中物体的边界表示为k邻域链码;然后根据曲率定义的差分形式计算各边界点处的曲率;最后通过检测曲率直方图的局部峰值精确定位出拐点,并利用拐角内部像素的颜色统计信息迅速判断出拐点的凸凹性.为验证该算法的效果,给出了该算法与4种已有算法的对比实验.结果表明,该算法不仅稳定性、准确性较高,而且算法简单,实时性强,并适合于嵌入式计算环境。     

基于细节增强的自适应Harris角点检测算法

针对传统Harris角点检测法计算速度较慢、聚簇现象较严重和在纹理信息少的区域提取角点数较少等不足,提出一种自适应Harris角点检测法,该算法利用巴特沃斯滤波器增强小波细节系数,通过计算角点响应函数的二阶值设定自适应阈值,从而提高Harris角点检测的精度和效率.实验表明,与传统的Harris角点检测法比较,该方法检测速度较快、角点的分布较均匀,且伪角点较少.     

Equidistant Smoothing of Polyhedra with Arbitrary Topologies

Smoothing of polyhedron with arbitrary topology is an important issue in CAGD and CAD/CAM, but so far it is deemed to be difficult to smooth the complex corners of a polyhedron. In this paper, the concept of distance surfaces of a surface and a solid is introduced, and the incisive properties of such surfaces are addressed which provide a theoretical foundation for modifying a general corner. The method is based on making constricted volume and the maximum distance the volume can be constricted is given too. It is shown that by the proposed method in this paper any polyhedron can be G¹ smoothed with quadraic and, sometimes toroidal surfaces. The new approach is suitable for engineering design and NC machining. The associated algorithm based on the classification theorem of corners is simple, fast and robust.     

基于角的形状匹配

提出了利用角检测技术进行图像的相似性检索。首先利用角检测技术检测出图像的角点,用这些角点刻画图像内的物体形状,并结合图论中最优对集的思想和一种改进的Hausdorff距离,提出了一种新的刻画两幅图像内容的相似程度方法。实验结果表明,利用所提出的方法进行图像的相似性检索能够取得较好效果。     

基于B样条的改进型Harris角点检测算法

在研究Harris角点检测算法时发现由于该算法采用高斯低通滤波进行平滑,因而对一些图像进行角点提取时,存在角点信息丢失和位置偏移等现象,而B样条函数可以收敛于高斯函数,并具有良好的逼近能力和紧支性等一些优秀的性质,从而基于B样条函数提出了一种改进的Harris角点提取方法.实验表明,该方法对提取角点非常有效.     

基于多方向对称和匀质约束的SUSAN棋盘格角点检测算法

棋盘格角点检测是摄像机标定过程中的一个重要步骤。针对原有SUSAN角点检测算法在棋盘格角点检测应用中边缘点与角点难以区分的问题,通过对棋盘格角点特性的分析和实验,提出了一种基于多方向对称和匀质约束的SUSAN棋盘格角点检测算法。通过在原有SUSAN算法的基础上添加对称约束算子和匀质约束算子滤除SU- SAN检测结果中的边缘点,最终确定角点的坐标。实验结果表明,该算法在角点检测的有效性和精度方面优于已有算法。     

基于改进Harris算法的汉字图像角点检测

根据汉字图像的特点,借鉴加速分割检测特征算法的思想,提出一种改进的Harris算法对汉字图像进行角点检测。首先,计算像素值初步判断出非角点并排除;然后,通过计算传统Harris算法中的角点响应函数对剩余的像素进行角点检测;最后,借鉴加速分割检测特征算法的思想对伪角点进行删除。最终检测出的角点是汉字笔画的起点和末端的角点,为下一步特征提取中确定线段的位置和计算线段的长度提供有利的技术基础。通过对一定数量的汉字图像的实验仿真,将本文方法与几种常用的角点检测方法进行比较,本文方法在检测正确率方面有所提高,但在运行时间上没有达到最短,综合考虑正确率和运行时间,本文方法较其他几种方法有所提高。      

基于有向面积的角点分类算法

在3维场景重构、运动估计、机器视觉等领域,不仅希望能检测出图像中的角点,而且还希望获得角点附近更多的信息,即能对角点进行进一步分类。为了能对图像中检测出的角点进行分类,提出了一种基于有向面积的角点分类方法,该方法首先采用基于协方差矩阵特征向量的小波变换角点检测原理检测出图像边缘上的角点;然后根据角点两侧的边缘信息定义了6种类型的角点;最后通过计算角点附近边缘上顺序排列的3个有向面积,实现对角点的分类。实验表明,基于有向面积的角点分类,具有较高的准确性。     

A Fast Firing Binaural System for Ultrasonic Pattern Recognition

A binaural sonar configuration with capability to detect and identify walls, edges and corners on real-time is presented in this work. A new multi-echo ultra-fast firing method increases the sonar acquisition rate, and provides crossed measurements without interference. A feature map is built on-line using Bayesian updating and classification rules. Three classifiers are implemented and analyzed: minimum risk (MR), maximum a posteriori (MAP), and minimum distance (MD). Experimental results of ultrasonic reflector recognition, using data collected in a specular indoor environment are presented in the paper.     

基于角点采样的多目标跟踪方法

为了跟踪互遮挡的多个目标,提出了一种基于角点采样的多目标跟踪方法。该方法以遮挡发生前的各目标区域中的Harris角点信息为样本,在遮挡发生情况下,采取K近邻分类器对目标区域中提取的角点信息进行分类,以有效区分遮挡在一起的多个目标。在角点特征提取过程中,还提出了双阈值Harris角点检测算法,用于自适应、准确地提取运动区域中角点信息。实验结果表明,该方法能有效区分遮挡在一起,且没有分裂之前的多个目标。     

BLAID: Boundaries from Locally Adaptive Isotropic Detection

Detection of corners is an important task in computer vision to capture discontinuous boundaries of objects of interest. Present operators designed to detect boundaries having sharp corners often produce unsatisfactory results because the points detected could also be an isolated point, ending of a thin line or a maximum curvature region of a planar curve. A novel corner detection operator, capable of detecting corner points that exist only on the boundary of an object, is presented in this paper. Initially, candidate corner points are detected by exploiting intensity information of the local neighborhood and associated connectivity patterns around the center point within a local window. Further verification is done to confirm whether the detected corner point is on the boundary of the targeted object. As the proposed operator is isotropic, it covers all the orientations and corner angles by performing a single computation step within the local window. The performance of the operator is tested with both synthetic and real images and the results are compared with other major corner detectors.