C#实现运动物体的检测方法

文章在VS .NET 2003环境下,利用C对图像及视频流的处理能力,实现对运动物体进行检测的方法.方法主要是在"背景帧"基础上,使用滤波技术,对运动物体进行自动检测,该方法简单、有效,有助于对运动物体的进一步识别.     

基于学习向量量化网络的指定颜色物体的识别

为解决计算机视觉中已知颜色属性的物体的识别问题,文章提出了一种基于HSV模型,以H、V参数特征值作为特征向量,应用基于LVQ学习算法的神经网络分类器进行颜色识别的的方法,很好地解决了指定颜色物体的识别问题。通过实验,对指定颜色的目标物体的识别效果比较理想,表明该算法是确实可行的。     

复杂运动目标的学习与识别

针对复杂运动目标识别问题,提出了一个基于反馈型随机神经网络的运动认脸与物体的自动识别系统,该系统具有强大学习能力,运动目标检测与识别快速准确等特点,在对该的核心－反馈型二元网络进行深入分析的基础上,提出了一种适合于该神经网络模型的高效渐进式Boltzmann学习算法,实验结果表明,该识别系统性能优异,在几个方面超过了eTrue公司的TrueFace人脸识别系统。     

五自由度立体视觉平台--目标物体的识别与定位

从立体视觉与机器人控制集成的角度出发,建立了一个主动立体视觉跟踪和定位系统,用于柔性装配线中装配零件的运动跟踪和装配工位的精确定位。讨论了使用立体视觉进行快速目标识别和定位的方法,该方法将颜色分割与基于形状特征相结合,提出采用基于局部图像的HSV阈值分割和形状识别相结合的图像处理方法,该方法经过HSV阈值和形状判据,准确地识别出物体,得到物体的边界和质心。能快速准确地识别出物体并进行定位,满足柔性生产线小规模定制产品装配的要求。最后给出的实验结果验证了该方法的有效性。     

无表观特征小目标检测与跟踪

检测跟踪模糊的小目标是计算机视觉领域中难度极大,富有挑战的任务。由于被跟踪的目标过小或过于模糊,难以提取合适的可用于检测和跟踪的表观特征,使得现有的目标检测和跟踪算法不能解决上述问题。前景运动物体区别于背景随机噪声的一个重要特征是运动物体具有一定的运动规律,基于这个假设提出一种新方法,根据物体的运动规律对其进行跟踪。首先,提出利用运动物体的时空域关联性,对视频中的运动目标进行分割和去噪;其次,提出了利用动态规划得出并优化物体的运动轨迹。各种条件下的实验结果表明了上述方法的精确性和鲁棒性。     

基于嵌入式系统的移动机器人目标识别方法

基于S3C2410嵌入式系统平台,提出一种高效实用的目标识别方法,把视觉辨识应用于移动机器人的目标识别中。从目标识别的流程上,首先采用一种全整数运算的RGBHSV颜色转换空间模型;然后以HSV颜色空间中H值为主,S值为辅的方式对图像进行二值化处理;接着采用一种快速的行扫描标记聚类算法找到二值化图像中的目标物体存在的连通区域。从而从颜色和形状上识别目标物体。经实验证明,谊方法在移动机器人运动场合中能较好的完成对目标物体的识别,实用性强。     

基于在线拓扑模板更新的目标跟踪

目标跟踪是人工智能的研究热点之一。传统方法中,基于颜色直方图的目标跟踪易受背景相似颜色的影响。利用边缘方向直方图（EOH）方法对运动物体进行跟踪时,在复杂背景下其效果也会受到影响。文章对传统跟踪方法进行了改进,提出了一种利用拓扑模板进行跟踪的方法,对目标特征表示、参考模板更新部分做了相应改进。分块拓扑在保留传统跟踪方法对物体微小形变鲁棒性的同时,对被遮挡物体和形变物体有了更好的分辨能力。     

基于DSP的足球机器人目标识别

目标识别是足球机器人视觉子系统的关键,论文对M i r o S o t中型组足球机器人视觉子系统的预处理和目标识别提出了改进的算法。基于D S P提出了改进的提取目标物体几何特征进行模式匹配的方法,实现了对复杂环境中目标物体的识别,并通过Code Composer Studio(CCS)进行了仿真测试,有较高的识别率。     

室内智能移动机器人规则物体识别与抓取

规则物体的识别与抓取往往比非规则物体的识别与抓取更加有规律性,因此本文以规则的圆形、长方形物体为例,对圆提出圆形度这种度量方法,对长方形基于角点检测,提出计算各个角点之间夹角,结合HSV模型对图像进行分割,提高物体识别度。为了使单目机器人更准确的抓取物体,需要比较准确的知道目标物体相对于机器人的位置信息,结合地平面约束测距模型算法计算圆形物体的具体世界坐标。在Visual Studio2010,Pioneer3-AT机器人平台上实验表明,基于圆形度的物体识别方法能够比较快速准确的识别圆形物体,进而准确抓取。     

基于宏块的Hs光流算法运动估计优化研究

光流场属于一种运动参数,它不仅能够为人们提供目标物体的运动信息,还能使人们对运动物体进行有效的识别与定位,从而使人们更加有效的对目标物体进行运动估计,这也使光流场在计算机视觉领域中有着非常重要的应用.Hs光流算法对于提高光流场质量有着决定性的影响,但其在对目标物体运动信息进行识别、跟踪与估计时,常常存在计算量过大、易受噪声影响等问题,这也使Hs光流算法难以满足人们的数据处理需求.为此,有必要对Hs光流算法进行相应的改进,以此提高光流场质量.本文通过对Hs光流算法在运动估计优化中存在的问题及其相关影响因素进行分析,提出了Hs光流算法的改进思路,在此基础上结合宏块运动估计算法对改进后的Hs光流算法运动估计优化进行深入的研究.     

Surface registration using a dynamic genetic algorithm

Robust and fast free-form surface registration is a useful technique in various areas such as object recognition and 3D model reconstruction for animation. Notably, an object model can be constructed, in principle, by surface registration and integration of range images of the target object from different views. In this paper, we propose to formulate the surface registration problem as a high dimensional optimization problem, which can be solved by a genetic algorithm (GA) (Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989). The performance of the GA for surface registration is highly dependent on its speed in evaluating the fitness function. A novel GA with a new fitness function and a new genetic operator is proposed. It can compute an optimal registration 1000 times faster than a conventional GA. The accuracy, speed and the robustness of the proposed method are verified by a number of real experiments.     

基于遗传算法模式匹配的机器人实时视觉伺服

对于机器人手臂来讲 ,对工作环境的识别是完成一个智能任务的最重要的问题之一 .因为这种智能可以使它工作在一个变化的环境中 .本文提出了一种新的机器人手臂的控制策略 ,可以利用视觉信息来指导机器人的手臂在它的工作空间中捡起一个已知形状但任意位置和方向的物体 .在对物体的搜索过程中 ,利用基于视觉闭环的视觉伺服来完成对机器人手臂的运动控制 .本系统利用遗传算法 (Genetic Algorithm ,GA)和模式匹配技术完成对搜索空间的搜索并获得了良好的结果 .本文完成了对带有两连杆手臂的视觉伺服系统的仿真 ,仿真结果证明了算法的有效性     

Research on a face recognition system by the genetic algorithm

Computer vision and recognition is playing an increasingly important role in modern intelligent control. Object detection is the first and most important step in object recognition. Traditionally, a special object can be recognized by the template matching method, but the recognition speed has always been a problem. In this article, an improved general genetic algorithm-based face recognition system is proposed. The genetic algorithm (GA) has been considered to be a robust and global searching method. Here, the chromosomes generated by GA contain the information needed to recognize the object. The purpose of this article is to propose a practical method of face detection and recognition. Finally, the experimental results, and a comparison with the traditional template matching method, and some other considerations, are also given. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

An autoregressive model approach to two-dimensional shape classification

In this paper, a method of classifying objects is reported that is based on the use of autoregressive (AR) model parameters which represent the shapes of boundaries detected in digitized binary images of the objects. The object identification technique is insensitive to object size and orientation. Three pattern recognition algorithms that assign object names to unlabelled sets of AR model parameters were tested and the results compared. Isolated object tests were performed on five sets of shapes, including eight industrial shapes (mostly taken from the recognition literature), and recognition accuracies of 100 percent were obtained for all pattern sets at some model order in the range 1 to 10. Test results indicate the ability of the technique developed in this work to recognize partially occluded objects. Processing-speed measurements show that the method is fast in the recognition mode. The results of a number of object recognition tests are presented. The recognition technique was realized with Fortran programs, Imaging Technology, Inc. image-processing boards, and a PDP 11/60 computer. The computer algorithms are described.     

Kinect 下深度信息获取技术及其在三维目标识别中的应用综述

微软公司 2010 年推出的 Kinect 深度传感器能够同步提供场景深度和彩色信息,其应用的一个关键领域就是目标 识别。传统的目标识别大多限制在特殊的情形,如：手势识别、人脸识别,而大规模的目标识别是近年来的研究趋势。 通过 Kinect 得到的 RGB-D 数据集多为室内和办公环境下获取的多场景、多视角、分目标类型的数据集,为大规模的目标 识别算法设计提供了学习基础。同时,Kinect 获取的深度信息为目标识别提供了强有力的线索,利用深度信息的识别方法 较以前的方法具有无法比拟的优势,大大地提高了识别的精度。文章首先对 Kinect 的深度获取技术做了详细介绍;其次 对现有的 3D 目标识别方法进行综述,接着对已有的 3D 测试数据集进行分析和比较;最后对文章进行小结以及对未来 3D 目标识别算法和 3D 测试数据集的发展趋势作了简单的阐述。     

GA-pattern matching-based manipulator control system for real-time visual servoing

_—In robotic applications, tasks of picking and placing are the most fundamental ones. Also, for a robot manipulator, the recognition of its working environment is one of the most important issues to do intelligent tasks, since this aptitude enables it to work in a variable environment. This paper presents a new control strategy for robot manipulators, which utilizes visual information to direct the manipulator in its working space, to pick up an object of known shape, but with arbitrary position and orientation. During the search for an object to be picked up, vision-based control by closed-loop feedback, referred to as visual servoing, is performed to obtain the motion control of the manipulator hand. The system employs a genetic algorithm (GA) and a pattern matching technique to explore the search space and exploit the best solutions by this search technique. The control strategy utilizes the found results of GA-pattern matching in every step of GA evolution to direct the manipulator towards the target object. We named this control strategy step-GA-evnlution. This control method can be applied for manipulator real-time visual servoing and solve its path planning problem in real-time, i.e. in order for the manipulator to adapt the execution of the task by visual information during the process execution. Simulations have been performed, using a two-link planar manipulator and three image models, in order to find which one is the best for real-time visual servoing and the results show the effectiveness of the control method.     

Optimizing image focus for 3D shape recovery through genetic algorithm

Three-dimensional information of objects is advantageous and widely used in multimedia systems and applications. Shape form focus (SFF) is a passive optical technique that reconstructs 3D shape of an object using a sequence of images with varying focus settings. In this paper, we propose an optimization of the focus measure. First, Wiener filter is applied for noise reduction from the image sequence. At the second stage, genetic algorithm (GA) is applied for focus measure optimization. GA finds the maximum focus measurement under a fitness criterion. Finally, 3D shape of the object is determined by maximizing focus measure along the optical direction. The proposed method is tested with image sequences of simulated and real objects. The performance of the proposed technique is analyzed through statistical criteria such as root mean square error (RMSE) and correlation. Comparative analysis shows the effectiveness of the proposed method.     

Object recognition based on image sequences by using inter-feature-line consistencies

An image sequence-based framework for appearance-based object recognition is proposed in this paper. Compared with the methods of using a single view for object recognition, inter-frame consistencies can be exploited in a sequence-based method, so that a better recognition performance can be achieved. We use the nearest feature line (NFL) method (IEEE Trans. Neural Networks 10 (1999) 439) to model each object. The NFL method is extended in this paper by further integrating motion-continuity information between features lines in a probabilistic framework. The associated recognition task is formulated as maximizing an a posteriori probability measure. The recognition problem is then further transformed to a shortest-path searching problem, and a dynamic-programming technique is used to solve it.     

脑皮层视觉认知量化模型及其在目标检测定位中的应用

MIT大学的Rieseshuber和Poggio提出了脑颞叶皮层视觉认知系统的标准量化模型(QMVC),这种前馈式层级模型很好地仿效了脑皮层视觉从简单认知元到复杂认知元的识别机理.本文由QMVC模型提取出一组新的特征矢量,这组特征矢量具有对目标变换的不变性.基于QMVC模型的特征矢量建立了新的目标识别系统结构,新目标识别系统对各类目标具有不错的识别率和ROC特性.最后本文引入了尺度窗技术,将新特征应用于复杂场景中的目标检测和定位,实验结果说明本文的新目标检测方法是有效的.     

Structural pattern recognition using genetic algorithms

This paper presents a genetic algorithm (GA) based optimization procedure for the solution of structural pattern recognition problem using the attributed relational graph representation and matching technique. In this study, candidate solutions are represented by integer strings and the population is randomly initialized. The GA is employed to generate a monomorphic mapping. As all the mapping constraints are not enforced during the search phase in order to speedup the search, an efficient pose clustering algorithm is used to eliminate spurious matches and to determine the presence of the model in the scene. The performance of the proposed approach to pattern recognition by subgraph isomorphism is demonstrated using line patterns and silhouette images.