基于改进Hough变换的直线检测算法

针对传统Hough变换算法检测直线时容易出现误检的缺点,提出一种基于改进Hough变换的直线检测算法.首先根据种子点的参数化结果,对图像中的点实现聚类,划分图像区域,然后对划分在同一区域的点进行特殊区间的详细参数化,确定直线参数,最终将该直线周围的点置为背景点,继续区域划分和直线检测,直到图像中不再存在种子点.该算法实现了对不同区域的点进行特殊的、有针对性的参数化,同一区域的点只允许对一条直线的检测投票.实验表明,与传统的Hough变换算法和文献中算法相比,该算法减小了误检率,并且对断裂边缘具有较好的鲁棒性.     

一种遥感图像中的道路检测方法

道路是遥感图像的重要特征。提出一种从遥感图像中检测道路目标的方法。首先,进行图像预处理,提取边缘,然后进行图像分块并利用Hough变换进行道路检测。分析了Hough变换的特点,并研究了Hough变换域极值点参数的特点,改进了Hough变换在道路目标检测中的应用。该方法不仅快速地检测机场等严格的线状道路目标,而且对具有一定曲率的道路目标同样有效。     

On using the CAM concept for parametric curve extraction

A parallel algorithm, in order to extract parametric curves from a two-dimensional (2-D) image space, is proposed. It is based on the Hough transform (HT) and uses the content addressable memory (CAM) as the main processor. A set of simulated results for circular shape extraction are presented in order to demonstrate its merit. Hence, voting, thresholding, and three-dimensional (3-D) peak extraction are efficiently performed within the CAM. In addition, and in order to reduce the quantization errors, a weighted AT algorithm (WHT), which uses a weighted voting is proposed. Experimental results indicate that a real-time shape extraction for an image 256/spl times/256 can be achieved within a small amount of hardware. Therefore, CAM-based HT can be considered as a promising attraction for next generation pattern recognition platforms.     

Accelerated Hough transform using rectangular image decomposition

A novel fast method for evaluating the Hough transform is proposed, which can be used to accelerate detection of prevalent linear formations in binary images. An image is decomposed using rectangular blocks and the contribution of each whole block to the Hough transform space is evaluated, rather than the contribution of each image point. The resulting acceleration in the calculation of the Hough transform field is demonstrated in two image processing experiments related to object axis identification and skew detection of digitised documents     

改进的Hough变换在PCB实时初检中应用

在图像处理和计算机视觉中，Hough变换是一种应用非常广泛的图像边缘检测技术．针对传统H0ugh变换算法所需的存储容量大、计算量大、速度慢、效率低，实时应用性差的同题，本论文运用了一种改进的Hough变换方法，分两步来完成目检测同题中的图心定位和半径测定，并通过针对PCB在线检测的具体应用情况加以改进并应用，进一步有效地减少存储容量，提高运行效率。实验证明算法满足PCB在线检测的实时性。     

改进的Hough变换圆检测算法

针对标准Hough变换在圆检测中对时间空间需求高的问题,提出一种改进的Hough变换圆检测算法。该算法将标准Hough变换检测圆时的三维空间转换为一维空间,利用圆为中心对称图形的几何特征计算圆心,然后通过一维Hough变换进行半径累积,得到圆半径。实验证明,该算法运行速度快,占用内存小,抗噪性能好,对单个圆和多个圆同样有效,具有较高的实用价值。     

基于霍夫变换椭圆检测的两种改进算法

针对一维参数空间霍夫变换椭圆检测算法不能检测长轴端点缺损的椭圆这一缺陷,提出了两种改进算法.两种改进算法都是先确定椭圆中心,采用分步计算参数的方法,以降低霍夫变换参数空间的维数,从而降低算法整体的时间复杂度.改进算法1可以看成是一维霍夫变换椭圆检测的一个补充算法,它通过改为最后对长轴进行一维累积,来解决长轴端点缺损的问题.改进算法2则是在椭圆中心确定后,利用椭圆的对称性,计算出其倾斜角,再进一步确定其他参数.实验表明,两种改进算法都能在较复杂的图中较快较准确地检测出残缺椭圆.     

Object classification in 3-D images using alpha-trimmed mean radialbasis function network

We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.     

基于噪声方差估计的红外弱小目标检测与跟踪方法

针对红外弱小多目标图像背景杂波干扰严重、弱 小目标检测率低和目标跟踪困难的问题,提出一种 基于噪声方差估计的红外弱小目标快速检测与目标跟踪算法。首先采用改进的形态学 滤波抑制背景噪声, 对处理后的多帧图像进行方差估计初步突出目标像素;然后对其进行信噪比(SNR)估计得到整个图像序列像素得 分,图像中像素SNR高的被标记为目标像素;再对标记过的图像进行分块分析 ,准确提取出连续图 像序列中的目标像素;将检测出的目标像素作为Hough变换的目标跟踪算法的输入,设置双 阈值实现目标 的有效跟踪。实验结果表明,在复杂背景下的红外弱小目标提取中,基于噪声方差估计的目 标检测拥有较 高的检测概率和较低的虚警概率,将其获得的目标像素作为Hough变换的输入,不仅可以有 效跟踪目标, 而且简化了算法的复杂度,实现目标的快速提取和跟踪,具有很高的应用价值。     

一种改进的红外弱小目标快速检测方法

针对复杂背景下红外弱小目标检测率低、目标跟踪困难的问题,提出一种改进的红外弱小目标快速检测方法。该方法采用改进的形态学滤波抑制背景噪声,对处理后的多帧图像进行方差估计初步突出目标像素,然后对其进行信噪比估计得到整个图像序列像素得分,图像中像素信噪比高的被标记为目标像素,再对标记过的图像进行分块分析,最终准确提取出连续图像序列中的目标像素。检测出的目标像素作为Hough变换的目标跟踪算法的输入,设置双阈值实现目标的有效跟踪。实验结果表明,在复杂背景下的红外弱小目标提取中,基于噪声方差估计的目标检测拥有较高的检测概率和较低的虚警概率,将其获得的目标像素作为Hough变换的输入,不仅可以有效跟踪目标,而且简化了算法的复杂度,实现目标的快速提取和跟踪,具有很高的应用价值。     

基于多尺度-局部方向轮廓综合的鲁棒虹膜定位算法

针对现有虹膜外边缘定位算法鲁棒性差的不足,本文提出多尺度-局部方向轮廓综合实现鲁棒虹膜定位的思路.该算法首先利用Gabor分解提取虹膜图像的多尺度-局部方向轮廓,在此基础上选择多个近垂直局部方向的子图进行综合而增强近垂直局部方向的虹膜边缘轮廓;在已有虹膜内边缘定位基础上根据先验知识选择有效的关键虹膜外边缘区域(消除干扰),再采用标准Hough确定虹膜外边缘形状参数,最后利用Hough变换进行小范围精确定位.仿真实验结果表明本文提出的算法在速度、稳定性方面优于现有文献报道的虹膜定位算法.     

Hough积累及其在搜索雷达中的应用

概述了Hough变换的原理及其在图像处理中的应用,示出其实现过程。在图像分析的基础上,讨论了将Hough变换用于搜索雷达长时间积累的问题。文中以典型的机械扫描雷达为例,对该算法进行了详细的仿真验证,给出了仿真结果,进行了简要的分析。最后总结了该算法的特点及其在搜索雷达中的应用。     

基于PF-TBD的双基地微多普勒提取

时频分布-Hough变换是目前单/双基地微多普勒参数化估计的主要方法。该文首先介绍了进动钝头双锥弹头双基地微多普勒模型,然后针对时频图中散射中心的幅值分布特性未知导致无法确定Hough变换的参数域空间的检测门限这一问题,该文引入了PF-TBD算法来检测散射中心存在性,思想是将时频图中散射中心微多普勒曲线视为目标的运动轨迹,利用PF-TBD算法实现对微多普勒曲线的检测前跟踪,再利用Hough变换提取微多普勒曲线,逐步检测和提取所有微多普勒曲线。最后针对某双基地下的进动钝头双锥弹头微多普勒时频图,提取了图中所有微多普勒曲线,验证了算法的可行性。      

基于Hough变换改进的直线检测与定位

利用标准的Hough变换检测空间的直线,研究各种改进的Hough变换算法,设计一种新的Hough变换检测算法,通过实验对比,从而获得更高的精度。     

一种基于图像分割和归一化灰度Hough变换的SAR图像舰船尾迹CFAR检测算法

该文提出了一种新的SAR图像舰船尾迹的恒虚警检测算法。该方法首先检测出舰船并将舰船部分用图像灰度均值代替;分别以舰船重心为中心分割出一定尾迹长度的正方形区域并以舰船重心为中心将图像分割成4个子图像,对各子图像进行归一化灰度Hough变换,分割后的尾迹在子图像中的对比度得到了提高。在各子图像变换域依概率模型进行统计建模实现了CFAR检测,最后将4个子图像的检测结果进行融合得到最终的检测结果。检测结果更好地提取出了尾迹,并且实现了尾迹的CFAR检测。仿真结果验证了算法的有效性。     

Detection of spicules on mammogram based on skeleton analysis

Existence of spicules is one of important clues of malignant tumors. This paper presents a new image processing method for the detection of spicules on mammogram. Spicules can be recognized as line patterns radiating from the center of tumor. To detect such characteristic patterns, line skeletons and a modified Hough transform are proposed. Line skeleton processing is effective in enhancing spinal axes of spicules and in reducing the other skeletons. The modified Hough transform is applied to line skeletons and radiating line structures are obtained. Experiments were made to test the performance of the proposed method. The system was designed using 19 training images, for which one normal case was recognized to be star-shaped. The other case were recognized correctly. Another experiments using 34 test images were also performed. The correct classification rate was 74%. These results shows the effectiveness of the proposed method.     

Exploiting Inherent Parallelisms for Accelerating Linear Hough Transform

Accelerating Hough transform in hardware has been of interest due its popularity in real-time capable image processing applications. In most existing linear Hough transform architectures, an m times medge map is serially read for processing, resulting in a total computation time of at least m² cycles. In this paper, we propose a novel parallel Hough transform computation method called the Additive Hough transform (AHT), wherein the image is divided using a k times k grid to reduce the total computation time by a factor of k². We have also proposed an efficient implementation of the AHT consisting of a look-up table (LUT) and two-operand adder arrays for every angle. Techniques to condense the LUT size have also been proposed to further reduce area utilization by as much as 50%. Our investigations based on employing an 8 times 8 grid shows a 1000 times speedup compared to existing architectures for a range of image sizes. Area-time trade-off analysis has been presented to demonstrate that the area-time product of the proposed AHT-based implementation is at least 43% lower than other implementations reported in the literature. We have also included and characterized a hierarchical addition step in order to generate a global accumulation space equivalent to that of the conventional HT. It is shown that the proposed implementation with the hierarchical addition step remains superior to other methods in terms of both performance and area-time product metrics. Finally, we show that the proposed solution is equally efficient when applied on rectangular images.     

A fast Hough transform for segment detection

The authors describe a new algorithm for the fast Hough transform (FHT) that satisfactorily solves the problems other fast algorithms propose in the literature-erroneous solutions, point redundance, scaling, and detection of straight lines of different sizes-and needs less storage space. By using the information generated by the algorithm for the detection of straight lines, they manage to detect the segments of the image without appreciable computational overhead. They also discuss the performance and the parallelization of the algorithm and show its efficiency with some examples.     

大鱼际掌纹图像边缘提取算法

主要研究大鱼际掌纹图像边缘提取算法。介绍几种经典的边缘检测算子以及Hough变换方法,重点讨论了小波模极大值多尺度边缘检测方法。构造了高斯多尺度边缘检测算子,根据噪声和图像边缘的小波变换模值跨尺度传递的不同特征,研究小波模极大值多尺度边缘检测方法,对大鱼际掌纹图像进行边缘提取。实验结果表明该方法检测到的边缘细节丰富,定位较准确,有效降低了噪声,不足之处是连续性较差。