基于Hough变换的煤岩界面识别方案

根据煤层与岩石层分界明显且呈线状的煤岩图像特征,提出一种基于Hough变换的煤岩界面识别方案。首先对煤岩图像进行去噪与增强预处理,然后去除煤与岩石边缘的孤立点与虚假边缘,最后采用Hough变换识别煤岩分界线。实验结果表明,该方案能够较快、较准确地检测出煤岩分界线。     

基于Hough变换的椭圆检测方法

椭圆检测在图像、图形识别与计算机视觉领域中有着重要的应用,而基于Hough变换的椭圆检测是其中常见的方法之一。本文对基于Hough变换的椭圆检测算法进行了综述,其中包括利用三点确定椭圆的提取方法、基于弦中心Hough变换的提取算法和基于椭圆几何对称性算法等,着重对不同方法的原理、实现和优缺点进行了讨论。     

基于Hough变换检测直线的研究

Hough变换是图像处理中的一种常用的检测算法,能够有效地在较大的噪声环境中提取图像中的特定信息。本文主要介绍了标准Hough变换算法以及为了降低标准Hough变换算法的时间复杂度而提出的快速Hough变换算法。     

基于随机Hough变换的道路边界识别算法研究

道路边界识别是基于机器视觉的智能车辆关键技术之一,提出了一种基于直线道路模型的道路边界识别和跟踪算法,该算法包括道路边界初始识别算法和跟踪算法两部分。在初始道路边界识别阶段,对预处理后的图像进行逐行搜索道路边界候选点,并结合相关的道路约束条件,采用Hough变换对候选边界点进行拟合,以提高鲁棒性。该方法的创新点是在道路跟踪算法中,建立目标搜索区域（OSA）,OSA的宽度根据道路边界识别的拟合可信度的变换进行动态调整,并采用随机Hough变换的算法对OSA中的边界点进行拟合,这样显著减少了运算时间和存储空间。试验结果表明,该方法不仅满足道路边界识别的实时性要求,还具有较好的鲁棒性。     

基于2D Hough变换的生物芯片自动识别

在分析生物苡片图像特点的基础上,提出了对图像分块进行2D Hough变换识别芯片斑点并自动调整圆心半径的方法。同已有技术相比,该方法不需要人工介入,并且算法中采用了学习的方法,大大减少了计算时间。实验结果表明,该方法有着较高的识别率。     

基于深度图像与骨骼数据的行为识别

为了充分利用深度图像与骨骼数据进行人体行为识别,提出了一种基于深度图形与骨骼数据的多特征行为识别方法。该算法的多特征包括深度运动图（DMM）特征与四方形骨骼特征（Quad）。深度图像方面,将深度图像投影到一个笛卡尔坐标系的三个平面获得深度运动图特征。骨骼数据方面,提出四方形骨骼特征,它是骨骼坐标的一种标定方式,得到的结果只与骨骼姿态有关。同时提出一种多模型概率投票的分类策略,减小了噪声数据对分类结果的影响。所提方法在MSR-Action3D和DHA数据库进行实验,实验结果表明,所提算法有着较高的识别率与良好的鲁棒性。     

基于Hough变换的圆检测方法

总结了圆检测的几种常用方法,如经典HT、随机HT和广义HT.结合几种方法的优缺点,提出了一种基于经典HT的改进Hough变换圆检测方法.该方法先对图像进行预处理,如灰度化、去噪滤波、边缘检测以及运用数学形态学等,然后进行Hough变换.其主要思想是用多维数组来代替经典的循环过程.把Hough变换应用到织物防水性能自动测试的真实图像中,通过对经典Hough变换与改进后的Hough变换的比较,可以看出检测速度有所提高,检测精度也达到了令人满意的程度.     

基于Hough变换和Fisher准则的垄线识别算法

为了提高农业机械自主作业视觉导航的精度,基于田间作物垄行的特点,首先选择作物的绿色为特征提取垄行结构;然后针对Hough变换原理提取垄线存在的问题,根据垄线点空间关系,运用Fisher准则函数进行反压缩处理,并将Fisher准则函数值作为垄线样本点疏密程度和方向势大小的度量,优化了Hough变换识别多垄线的条件,得出了多垄识别统一模型。试验结果表明,作物垄线定位的准确性、适应性均得到提高,而且能够避免较大面积杂草等影响,从而克服了传统Hough变换提取多垄线的不足,对农田机器视觉导航应用具有一定参考价值。     

基于小波分析和概率Hough变换的书脊视觉识别

针对无人图书馆中机器人利用视觉进行书脊识别问题展开研究。根据书脊图像本身较复杂、单本书轮廓难以提取的特点,提出了运用小波分析做书脊轮廓增强的图像预处理方案;通过实验比较了运用Sobel算子和Canny算子做书脊轮廓检测的效果,用累计概率霍夫变换法做书脊线段查找,用VC++和OpenCV开发了应用程序;借鉴模糊计算思想,针对厚、较厚、中等厚度、较薄、薄、混合等几种类型的书脊进行了分类实验和研究,再经辅助优化处理,基本可有效识别出不同情况下每本书的书脊轮廓,检测出每本书的厚度,为机械手利用视觉进行图书取放操作奠定了基础。     

基于骨骼时空图的人体行为识别

为进一步提升基于骨骼信息的人体行为识别准确率,对人体姿态的骨骼信息进行研究,分析图卷积(GCN)的理论基础,提出一种多尺度时空图卷积(multi scale ST GCN)的方法对骨骼的动态信息进行建模,不同于传统的手工对人体进行自上而下或者自下而上的遍历规则设计方法,而是通过构建网络模型对人体行为的时间空间信息进行自...     

基于小波分析和概率Hough变换的书脊视觉识别

针对无人图书馆中机器人利用视觉进行书脊识别问题展开研究。根据书脊图像本身较复杂、单本书轮廓难以提取的特点,提出了运用小波分析做书脊轮廓增强的图像预处理方案;通过实验比较了运用Sobel算子和Canny算子做书脊轮廓检测的效果,用累计概率霍夫变换法做书脊线段查找,用VC++和OpenCV开发了应用程序;借鉴模糊计算思想,针对厚、较厚、中等厚度、较薄、薄、混合等几种类型的书脊进行了分类实验和研究,再经辅助优化处理,基本可有效识别出不同情况下每本书的书脊轮廓,检测出每本书的厚度,为机械手利用视觉进行图书取放操作奠定了基础。     

An improved generalized Hough transform for the recognition of overlapping objects

The generalized Hough transform (GHT) is a powerful method for recognizing arbitrary shapes as long as the correct match accounts for both much of the model and much of the sensory object. For moderate levels of occlusion, however, the GHT can hypothesize many false solutions. In this paper, we present an improved two-stage GHT procedure for the recognition of overlapping objects. Each boundary point in the image is described by three features including the concavity, radius and normal direction of the curve segment in the neighborhood of the point. The first stage of the voting process determines the rotational angle of the sensory object with respect to the model by matching those points that have the same concavity and radii. The second stage then determines the centroid of the sensory object by matching those points that have the same concavity, radii and rotational angles. The three point features remove the false contribution of votes in the vote generation phase. Experimental results have shown that the proposed algorithm works well for complex objects under severely overlapping conditions.     

基于视频时空特征的行为识别方法

针对双流网络提取运动信息需要预先计算光流图,从而无法实现端到端的识别以及三维卷积网络参数量巨大的问题,提出了一种基于视频时空特征的行为识别方法。该方法能够高效提取视频中的时空信息,且无需添加任何光流计算和三维卷积操作。首先,利用基于注意力机制的运动信息提取模块捕获相邻两帧之间的运动位移信息,从而模拟双流网络中光流图的作用;其次,提出了一种解耦的时空信息提取模块代替三维卷积,从而实现时空信息的编码;最后,在将两个模块嵌入二维的残差网络中后,完成端到端的行为识别。将所提方法在几个主流的行为识别数据集上进行实验,结果表明在仅使用RGB视频帧作为输入的情况下,在UCF101、HMDB51、Something-Something-V1数据集上的识别准确率分别为96.5%、73.1%和46.6%,与使用双流结构的时间分段网络（TSN）方法相比,在UCF101数据集上的识别准确率提高了2.5个百分点。可见,所提方法能够高效提取视频中的时空特征。     

Real-time object recognition using a modified generalized Hough transform

A technique for real-time object recognition in digital images is described. On the one hand, our approach combines robustness against occlusions, clutter, arbitrary illumination changes, and noise with invariance under rigid motion, i.e., translation and rotation. On the other hand, the computational effort is small in order to fulfill requirements of real-time applications. Our approach uses a modification of the generalized Hough transform (GHT) to improve the GHT's performance: A novel efficient limitation of the search space in combination with a hierarchical search strategy is implemented to reduce the computational effort. To meet the demands for high precision in industrial tasks, a subsequent refinement adjusts the final pose parameters. An empirical performance evaluation of the modified GHT is presented by comparing it to two standard 2D object recognition techniques.     

基于区域生长与Hough变换的虹膜定位方法

快速、准确地定位虹膜是虹膜识别系统的关键。在分析了传统虹膜定位算法的基础上,提出了一种把区域生长与Hough变换相结合的虹膜定位方法,利用区域生长搜索虹膜的内边缘,根据图像的灰度变化规律,搜索位于外边缘的若干个点,利用Hough变换找出外边缘所在位置。实验结果表明,该方法易于实现,减少搜索的盲目性,可提高定位速度。     

A Hough transform based line recognition method utilizing both parameter space and image space

Hough Transform (HT) is recognized as a powerful tool for graphic element extraction from images due to its global vision and robustness in noisy or degraded environment. However, the application of HT has been limited to small-size images for a long time. Besides the well-known heavy computation in the accumulation, the peak detection and the line verification become much more time-consuming for large-size images. Another limitation is that most existing HT-based line recognition methods are not able to detect line thickness, which is essential to large-size images, usually engineering drawings. We believe these limitations arise from that these methods only work on the HT parameter space. This paper therefore proposes a new HT-based line recognition method, which utilizes both the HT parameter space and the image space. The proposed method devises an image-based gradient prediction to accelerate the accumulation, introduces a boundary recorder to eliminate redundant analyses in the line verification, and develops an image-based line verification algorithm to detect line thickness and reduce false detections as well. It also proposes to use pixel removal to avoid overlapping lines instead of rigidly suppressing the N×N neighborhood. We perform experiments on real images with different sizes in terms of speed and detection accuracy. The experimental results demonstrate the significant performance improvement, especially for large-size images.     

基于改进Hough变换的圆形物体检测

圆形(包括弧线)检测是数字图像处理过程中的经典问题之一,有着广泛的应用背景。基本的Hough变换方法是将图像中的每一边缘点映射到参数空间的一个区域,选取累积最多的参数。在现实生活中,由于噪音、数字化错误和图形变异等因素真实的图形经常被曲解,因此,图像在应用Hough变换后,很难找到单一的峰值,这也就造成了检测的难度。在Hough变换的原理基础上利用圆的几何特征提出了改进算法。理论和实验证明能获得较好的检测结果。     

Extended Hough transform for linear feature detection

Improving the accuracy of line segment detection reduces the complexity of subsequent high-level processing common in cartographic feature detection. We developed a new extension to the Hough transform and reported on its application to building extraction. We expanded the Hough space by a third parameter, the horizontal or vertical coordinate of the image space, to provide incremental information as to the length of the lineal feature being sought. Using this extended HT transform allowed us to more accurately detect the true length of a line segment. In addition, we used a Bayesian probabilistic approach to process our extended Hough space that further increased the accuracy of our extended Hough transform.     

Analysis of textual images using the Hough transform

The analysis of images of printed pages of text is considered. Since printed text can be viewed as textured line, the use of the Hough transform for detecting straight lines is proposed as an analysis tool. Methods for handling several discretization problems that arise in mapping the rectangular image space to the (, ) accumulator array are described. Several applications of analyzing the accumulator array are proposed. They include detecting the text skew angle, determining the signature of a text line so as to accept or reject a block as containing only text, using profile analysis to segment text into lines, and determining whether a textual block is rightside-up or otherwise.