结合LTS和Hough变换的直线检测算法

为提高Hough变换检测直线的精度,提出一种结合Hough变换与截断最小二乘法的直线检测算法。利用Hough变换确定图像中直线所在的大致区域,提取候选区域内的特征点集,采用截断最小二乘法拟合得到精确的直线参数。实验结果表明,该算法的检测率和检测精度较高,对Hough变换的分辨率要求较低,整体空间开销较小。     

结合Hough变换与改进最小二乘法的直线检测

提出了一种结合Hough变换与改进的最小二乘法的直线检测方法。分析了Hough变换与最小二乘法在直线检测及检测精度上的优缺点;利用Hough变换鲁棒且不需启发式信息的特点进行初步检测,确定存在直线的大致区域;利用最小二乘法确定直线区域内特征点回归直线的精确参数。为克服最小二乘法对强噪声点敏感的缺点,提出了双点移除的p最小二乘法,通过同时剔除具有最大正负误差的一对数据点,保证数据集中的正常点能得到可靠保留以获得精确的回归结果。实验结果表明,所提方法提高了直线检测的检测率与检测精度,且降低了对Hough变换的分辨率要求,可以减小算法整体的空间开销。     

基于多粒度数据融合的直线检测算法

本文探讨了粒度计算思想和Hough变换之间的联系，结合小波分析中尺度伸缩和平移的思想，提出了一种基于多粒度数据融合的直线检测算法。在Hough变换中，较大的参数离散间隔（△ρ，△θ）会降低检测精度，而较小的离散间隔则导致峰值扩散和伪峰的出现。该算法改善了（△ρ，△θ）的选择问题，基本实现了以各种合适的粒度（△ρ，△θ）对各种粗细宽度直线的检测。另外，和传统的Hough变换相比，该算法具有更高的计算效率。     

霍夫变换在幂函数型曲线检测中的应用

利用霍夫变换抗噪声能力强和能分离出属于不同直线附近点的特性,研究离散数据点集M中xi,yi满足幂函数关系时的曲线检测问题。首先,对离散数据点集M中的数据xi,yi作对数变换,得到新的数据点集M(xi,yi),此时,xi,yi具有线性关系;其次,用霍夫变换检测M中的直线,可得直线参数;然后,利用霍夫变换所得的直线参数,计算图象中的边缘点到直线的距离dki,并与给定阈值dk比较,从而将分布在不同直线附近的点分离出来,同时剔除数据点集M中的干扰点或噪声;最后,用最小二乘法拟合直线,得到剔除干扰点或噪声后的拟合曲线方程参数a和b。     

复杂工况下视觉AGV导航标识带中心线的提取研究

针对光照不均匀、导航标识带破损、少量杂物干扰的复杂工况条件下,对视觉AGV采集到的路面导航标识带图片进行特征提取,采用灰度化、中值滤波进行了图像预处理,研究了Otsu法和迭代法等动态阈值分割算法以及形态学在图像分割中的应用,并对预处理图像作了分割对比实验;然后对分割后的图像进行边缘提取,分析了基于最小二乘法和Hough变换算法的直线拟合原原理,提出了Hough算法下基于边缘线的中心线拟合算法,并作了直线拟合对比试验;实验表明,复杂工况下,采用基于形态学和Otsu算法相结合的方法对图像进行分割,得到的二值图像边界更完整,效果更好,基于Hough变换算法较最小二乘法能更精确有效地提取出导航标识带中心线及其方程。     

霍夫变换在指数函数型曲线检测中的应用

利用了霍夫变换抗噪声能力强和能分离出属于不同直线附近点的特性，研究离散数据点集M中xi，yi满足指数函数关系时的曲线检测问题。首先，对离散数据点集M中的数据xi，yi做半对数变换，得到新的数据点集M*(xi*，yi*)，此时，xi*，yi*具有线性关系；其次，用霍夫变换检测M*中的直线，可得直线参数；然后，利用霍夫变换所得的直线参数，计算图像中的点到直线的距离dki，并与给定阈值dk比较，从而将分布在不同直线附近的点分离出来，同时剔除数据点集M*中的干扰点或噪声；最后，用最小二乘法拟合直线，再经过反变换，得到剔除干扰点或噪声后的拟合曲线方程参数a和b。该方法能够很好地检测出数据集中的数据点按指数关系分布时的曲线，特别是能够检测出数据集中存在多条曲线的情况，解决了最小二乘法拟合曲线时存在的3个问题，同时又对霍夫变换的精度要求不高。     

基于改进的Hough变换提取影像边缘

当影像中的边缘点较为密集或者噪声点较多时,直接应用Hough变换提取边缘线将会面临两个困难:一是虚假的峰值很多;二是边缘提取不准确.这两个问题往往会导致边缘提取的失败.针对这种情形,提出一种改进的Hough变换新方法,首先对分块影像类的边缘点进行Radon变换以获取局部直线特征;根据局部直线段的斜率、截距对其进行分类,同时计算出后续的Hough变换时所需要的ρ、θ取值范围,在此基础上进行经典的Hough变换.实验证明新方法具有很好的效果.     

改进随机Hough变换在心肌纤维图像直线检测中的应用

利用改进的随机Hough变换被用来对心肌纤维方向进行了检测。针对心肌纤维边缘的特点,采用窗口对投票点对加以筛选,同时利用模糊映射对Hough变换投票在变换空间加以修正,使随机Hough变换能够较好的用于复杂非规则直线段的检测,提高了利用Hough变换检测心肌纤维方向的正确率。     

关于Hough变换最小化参数空间的一点讨论

Hough变换因其固有的鲁棒性在众多视觉问题中得到了广泛应用.文中探讨了当Hough变换的参数空间均匀离散化后,如果要求不同的直线对应不同的计数器,则对应的离散参数空间所需的最少计数器个数的问题.以Hough变换的标准参数(ρ,θ)空间为例进行探讨,相关方法完全可以推广到Hough变换的其他参数化形式.文中的结果从一个侧面揭示了图像均匀噪声在Hough变换参数空间分布的固有的不均匀性现象,这种不均匀性必然导致Hough变换在直线检测时直线位置和朝向的依赖性对噪声的不同敏感程度,该结果对从事Hough变换研究的相关人员具有一定的参考作用.     

基于DSM影像的建筑物边缘直线特征提取

吸取了Hough变换的“投票”方法,提出一种直线提取的方法,并对DSM影像进行了实验。由于两个点可以确定一条直线,固定一个点,与另一个边缘点共同可以得到一条直线的倾角,通过对倾角进行“投票”,可以得到符合条件的直线以及线段的两个端点。以已确定的一条直线的终点为起点,重复以上步骤,直至到达最后一个点。整个计算过程都是对两个已知坐标的点进行计算,计算简单,计算量较Hough变换明显减少,且精度较高。     

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.     

Detecting line segments in an image – a new implementation for Hough Transform

The conventional Hough Transform is a technique for detecting line segments in an image. The conventional Hough Transform transforms image points into lines in the parameter space. If there are collinear image points, the lines transformed from the points intersect at a point in the parameter space. Determining the intersection is generally carried out through the “voting method”, which partitions the parameter space into squared meshes. A problem with the voting method involves determining the resolution required for partitioning the parameter space. In this paper, we present a solution to this problem. We propose to transform an image point into a belt, whose width is a function of the width of a line in the image. We then determine the intersection of numerous belts to detect a line segment. An iterated algorithm based the transformation for detecting line segments is presented in this paper.     

A decomposable parameter space for the detection of ellipses

Hough transform is a well-known method for detecting parametric curves in binary images. One major drawback of the method is that the transform requires time and memory space exponential in the number of parameters of the curves. An effective approach to reduce both the time and space requirement is the parameter space decomposition. In this paper, we present two methods for the detection of ellipses based on the straight line Hough transform (SLHT).

The SLHT of a curve in the θ-π space can be expressed as the sum of two terms, namely, the translation term, and the intrinsic term. One useful property of this representation is that it allows the translation, rotation and intrinsic parametersof the curve be separated easily. Timing performance of the proposed methods compares favorably with the other Hough-based methods.     

Towards Real-Time Detection of Discrete Straight Features with a Hybrid Technique Based on Preliminary Curve Segmentation and Zoomed-Adaptive Parametric Mapping

Atechnique capable of high performance, real-time detection of discrete straight line segments is described that allows major improvements to traditional alternative techniques such as the standard Hough transform. Such improved features result from the following three underlying principles: (a) the adoption of a precise mathematical-theoretical characterization of discrete lines and their mapping into parametric spaces; (b) the incorporation of adaptive parametric mapping that allows full congruency in the representation of discrete straight lines; and (c) the inclusion of a pre-processing stage that performs preliminary curve segmentation thus constraining the parameter space to be further investigated, which is done in a zoomed fashion. The high performance of the proposed technique has been corroborated, comparatively to three alternative techniques including the Hough transform, through a formal and quantitative performance assessment that is also discussed.     

Straight line extraction via multi-scale Hough transform based on pre-storage weight matrix

Considering the low efficiency and accuracy of existing straight line extraction methods from large-scale imagery, a multi-scale Hough transform (HT) method based on the pre-storage weight matrix is proposed. Improvements of the proposed HT include: using the pre-storage weight matrix to save storage space and improve the efficiency of the HT, applying a multi-scale method to enable the detection of smaller features from large-size images, using overlapping adjacent tiles to avoid the fragmented lines, optimizing the dimensions of the discrete transform domain, normalizing the accumulator to extract off-centred lines and using a dual threshold for distributed line detection. The experimental results show that the proposed algorithm is not only more efficient and robust but also can obtain richer and more accurate information about features, especially for large-size images.     

A neural implementation of the Hough transform and the advantages of explaining away

The Hough transform (HT) is widely used for feature extraction and object detection. However, during the HT individual image elements vote for many possible parameter values. This results in a dense accumulator array and problems identifying the parameter values that correspond to image features. This article proposes a new method for implementing the voting process in the HT. This method employs a competitive neural network algorithm to perform a form of probabilistic inference known as “explaining away”. This results in a sparse accumulator array in which the parameter values of image features can be more accurately identified. The proposed method is initially demonstrated using the simple, prototypical, task of straight line detection in synthetic images. In this task it is shown to more accurately identify straight lines, and the parameter of those lines, compared to the standard Hough voting process. The proposed method is further assessed using a version of the implicit shape model (ISM) algorithm applied to car detection in natural images. In this application it is shown to more accurately identify cars, compared to using the standard Hough voting process in the same algorithm, and compared to the original ISM algorithm.     

A real-time processor for the Hough transform

The Hough transform method for recognition can cope effectively with noisy backgrounds and gaps in boundaries. However, long computation time and large memory requirements have prevented it from being used in real-time applications. An architecture devised to solve those problems, with a focus on detecting straight lines, is presented. Examples are given of the use of an experimental hardware model for automatic inspection and measurement of objects in factories and laboratories. It took less than one second from picture input to straight line parameter reproduction for these examples     

文档图像中书写线的检测与去除

采用快速的Hough变换检测文档图像中的书写线,从图像中抽取少量特征点,将其分成两个子集,每次从两个子集中各取一个上点计算变换窨内的对应参数,当变换空间的累加值达到预先设定的阈值就认为已成功书写线,使Hough变换的速度大大加快,具有很强的实用价值,根据书写线与字符笔画的位置关系去除书写线,对书写线和相交的区域依据书写线的宽度和相交情形确定不同的结构元素,进行数学形态学的开运算,去除书写线的同时,较好地保持了字符笔画,实验结果表明,对信封图像上书写线的检测和去除有满意的处理效果。     

Autonomous Lane Keeping Solution Based on Adaptive Gray Threshold and Region of Interest

Autonomous lane keeping is an important technology in intelligent transportation,which is used to avoid unnecessary traffic accidents caused by lane departure.To adapt different lighting environment,and make up ordinary Hough transform’s shortcomings of tardiness and poor immunity,we propose an improved algorithm by using adaptive gray threshold and setting Region of interest(ROI),to do the quick Hough transform for tracking lane line,implementing autonomous lane keeping. The dynamic adaptive threshold method can be suitable with different lighting conditions and quickly,accurately remove most of the information not relative to lane line.Meanwhile setting ROI can let the program only care about the specific region which can provide useful information and further reduce the processing data.And then on the basic of identification,we put forward some efficient innovation strategy about the control logic of straight state,curve state and the transition state.The experiment proves that this solution greatly raises efficiency.