Fuzzy cell Hough transform for curve detection

In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour point location which is increased when noisy images are used. By using fuzzy cells, each contour point in the spatial domain contributes in more than one fuzzy cell in the parameter space. The array that is created after the fuzzy voting process is smoother than in the crisp case and the effect of noise is reduced. The curves can now be detected with better accuracy. The computation time that is slightly increased by this method, can be minimized in comparison with classical Hough Transform, by using recursively the fuzzy voting process in a roughly split parameter space, to create a multiresolution fuzzily split parameter space.     

A shape-based voting algorithm for pedestrian detection and tracking

This paper presents the MOUGH (mixture of uniform and Gaussian Hough) Transform for shape-based object detection and tracking. We show that the edgels of a rigid object at a given orientation are approximately distributed according to a Gaussian mixture model (GMMs). A variant of the generalized Hough transform is proposed, voting using GMMs and optimized via Expectation-Maximization, that is capable of searching images for a mildly-deformable shape, based on a training dataset of (possibly noisy) images with only crude estimates of scale and centroid of the object in each image. Further modifications are proposed to optimize the algorithm for tracking. The method is able to locate and track objects reliably even against complex backgrounds such as dense moving foliage, and with a moving camera. Experimental results indicate that the algorithm is superior to previously published variants of the Hough transform and to active shape models in tracking pedestrians from a side view.     

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。     

Generalizing the Hough transform to detect arbitrary shapes

The Hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves^(1,2) and non-analytic curves,⁽³⁾ but these methods were restricted to binary edge images. This work was generalized to the detection of some analytic curves in grey level images, specifically lines,⁽⁴⁾ circles⁽⁵⁾ and parabolas.⁽⁶⁾ The line detection case is the best known of these and has been ingeniously exploited in several applications.^(7,8,9)We show how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space. Such a mapping can be exploited to detect instances of that particular shape in an image. Furthermore, variations in the shape such as rotations, scale changes or figure ground reversals correspond to straightforward transformations of this mapping. However, the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes. This makes the generalized Hough transform a kind of universal transform which can be used to find arbitrarily complex shapes.     

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.     

A circle-detection algorithm simulating wave propagation

Based on the analogy of the Hough transform and Huygens's principle, we present a circle-detection algorithm that numerically solves a two-dimensional wave equation using neighbor-based operations only, that is, Laplacian, frame addition, and multiplication of constants with frame contents, all basic functions of standard image processors. Because it does not use edge extraction, the algorithm detects circles even from low-contrast and blurred images. A comparison of point spread functions shows the algorithm to be equivalent to the weighted Hough transform but requiring much less computation. We applied the algorithm to disk-surface inspection of low-contrast and blurred microscopic images.     

Detection of vehicles from traffic scenes using fuzzy integrals

This paper presents an improved vehicle detection algorithm for traffic scene interpretation. In our previous high-level dynamic traffic scene interpretation systems (Dance et al., Picture Interpretation: A Symbolic Approach, World Scientific, New Jersey, 1995; Liu et al., IEEE Trans. Syst. Man Cybernet. Part B (2001) to appear), we used a simple background-subtraction procedure (referred to as Method 0) for vehicle detection, which, although adequate for well-defined image sequences, was not applicable to slightly displaced and noisy images. Our new method (referred to as Method 1) uses the Hough transform to extract the contour lines of the vehicles and morphological operations to reduce noise and improve the shape of the object regions. Finally, in the detection process we compute fuzzy integrals based on the evidence gathered. We have carried out extensive experiments. For all vehicles in the images, including the ones partially occluded and cut off at the image boundary, we were able to achieve a detection rate of 80% (Method 1) compared to 13% (Method 0). For the vehicles that are almost completely visible, the detection rate was 90%. In addition, for a set of 492 images, our new method reduces the number of false alarms from 427 (by Method 0) to only 9.     

一种新颖的基于边缘检测的图像分割方法

针对家庭数字照片的特点以及应用范围，提出了带有衰减因子的Robert微分算子与动态的自适应阈值相结合的边缘检测方法，并利用了边缘检测后边缘点的方向信息，作为Hough变换的方向角，可以较快提取出边缘线段，从而通过边缘跟踪获得无噪声点的相似区域，这为进一步提取图像的颜色特征或形状特征提供了良好的基础。     

基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法

为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的 离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。 然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最 后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清 晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策 略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已 知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原, 通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。     

一种强噪声背景下图像标定符号的定位方法

图像中标定符号的定位与识别是进一步计算数字图像对应的空间距离的依据。本文应用Hough变换对图像标定符号进行定位,克服了强噪声背景下单独采用灰度特征定位的缺陷。定位后对图像进行分割,并与传统方法比较。实验证明,本方法对在图像中所占比例较小的标定符号有较好的定位效果,并结合标定符号的灰度特征对强噪声背景下的标定符号进行准确分割。     

基于Hough变换的图象检测对偶点法

提出了图形边界对偶点的概念,证明了对对称图形对偶点的存在性及其关于平移、缩放和旋转组合变换的不变性;结合Hough变换,建立了一种快速的图象检测方法,详细给出圆和椭圆检测的对偶点算法,并补充了文献[1]中公式的不足,实验结果表明,该对偶点法除保留了Hough变换法的容错性和鲁棒性外,还具有很快的计算速度,而且可以灵活地应用于解决一些较难的图象检测问题。     

Randomized or probabilistic Hough transform: unified performance evaluation

Rapid computation of the Hough transform is necessary in very many computer vision applications. One of the major approaches for fast Hough transform computation is based on the use of a small random sample of the data set rather than the full set. Two different algorithms within this family are the randomized Hough transform (RHT) and the probabilistic Hough transform (PHT). There have been contradictory views on the relative merits and drawbacks of the RHT and the PHT. In this paper, a unified theoretical framework for analyzing the RHT and the PHT is established. The performance of the two algorithms is characterized both theoretically and experimentally. Clear guidelines for selecting the algorithm that is most suitable for a given application are provided. We show that, when considering the basic algorithms, the RHT is better suited for the analysis of high quality low noise edge images, while for the analysis of noisy low quality images the PHT should be selected.     

一种利用形状片段的物体检测方法

针对物体检测中传统方法计算量大, 对复杂背景敏感, 且大部分物体检测方法只能得到物体所在区域而不能精确定位物体边缘等问题, 本文提出一种基于形状片段特征的物体检测方法. 该方法首先根据训练集得到具有多尺度特性的物体形状片段模型; 对测试图像按照和模型中边缘片段提取一致方法, 根据形状片段间的相似度, 选取出候选形状片段; 然后根据候选形状片段估计出模型中的片段与测试图像中片段之间的旋转角; 最后结合具有全局约束的概率Hough变换, 将物体检测问题转换为Hough空间概率问题; 根据Hough空间求解出的物体中心位置, 对候选形状片段验证, 得到实际物体轮廓片段. 理论分析和实验结果表明, 本文提出的算法具有较好的效果.     

A fast digital Radon transform—An efficient means for evaluating the Hough transform

A fast digital Radon transform based on recursively defined digital straight lines is described, which has the sequential complexity of N² log N additions for an N × N image. This transform can be used to evaluate the Hough transform to detect straight lines in a digital image. Whilst a parallel implementation of the Hough transform algorithm is difficult because of global memory access requirements, the fast digital Radon transform is vectorizable and therefore well suited for parallel computation. The structure of the fast algorithm is shown to be quite similar to the FFT algorithm for decimation in frequency. It is demonstrated that even for sequential computation the fast Radon transform is an attractive alternative to the classical Hough transform algorithm.     

Improved localization in a generalized Hough scheme for the detection of straight edges

Hough transforms are widely used for the location of straight edges in digital images, yet most common line parametrization schemes give no information on longitudinal localization. The generalized Hough transform goes some way to overcoming this problem. This paper studies how to improve the situation further. A trade-off between sensitivity and localization is found; in practical situations this results in significantly greater accuracy, but the important gain is a reduction in the number of ambiguities introduced by interactions between the transforms of unrelated straight edges.     

一种基于Hough变换的文档图像倾斜纠正方法

在对文本扫描输入的过程中,文本图像不可避免地会发生倾斜,倾斜校正将为图文分割、文字识别等后续处理工作创造良好的条件。提出了一种基于Hough变换的检测图像倾斜度的方法,为了克服Hough变换计算量大的缺点,该方法首先选取局部代表性子区域并提取其图像水平边缘,然后对提取的水平边缘进行两级Hough变换,从而实现了准确性与快速性的很好结合。     

A novel Hough transform based on eliminating particle swarm optimization and its applications

Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is employed to improve the speed of a HT. The parameters of the solution after Hough transformation are considered as the particle positions, and the EPSO algorithm searches the optimum solution by eliminating the “weakest” particles to speed up the computation. An accumulation array in Hough transformation is utilized as a fitness function of the EPSO algorithm. The experiments on numerous images show that the proposed approach can detect curves or contours of both noise-free and noisy images with much better performance. Especially, for noisy images, it can archive much better results than that obtained by using the existing HT algorithms.     

宽线段Hough变换及其在箭靶识别上的应用

Hough变换是用于检测图像中直线段的有力工具。论文提出的宽线段Hough变换针对传统Hough变换进行了改进,使之适用于多条宽线段同时存在的情况,并且解决了端点提取的问题。该方法应用于箭靶识别取得了很好的效果,实验表明对比传统方法具有较大优势。     

Hough变换在数码印花飞线检测中的应用

针对织物数码印花过程中出现的飞线问题, 以Hough变换理论为基础, 结合图像处理技术, 构建了一套数码印花飞线自动检测系统. 通过Hough变换提取出图像的直线特征信息后, 利用互相关系数检测出数码印花过程中出现的飞线问题, 最后通过人工检测和检测结果比较发现, 该系统较好地完成了数码印花飞线的自动检测.     

Perceptual primitives from an extended 4D Hough transform

Directional features extracted from Gabor wavelets responses were used to train a structure of self-organising maps, thus classifying each pixel in the image within a neuron-map. Resulting directional primitives were grouped into perceptual primitives introducing an extended 4D Hough transform to group pixels with similar directional features. These can then be used as perceptual primitives to detect salient structures. The proposed method has independently fixed parameters that do not need to be tuned for different kind or quality of images. We present results in application to noisy FLIR images and show that line primitives for complex structures, such as bridges, or simple structures, such as runways, can be found by this approach. We compare and demonstrate the quality of our results with those obtained through a parameter-dependent traditional Canny edge detector and Hough line finding process.