Hough transform for feature detection in panoramic images

Omni-directional sensors are useful in obtaining a 360° field-of-view. With a radially symmetric mirror and conventional lens system this can be achieved with a single camera. There are several proposed profiles for the mirror, but most violate the single viewpoint (SVP) criteria necessary to allow functional equivalence to the standard perspective projection, posing challenges that have not yet been addressed in the literature. Such a imaging system with a non-SVP optical system do not benefit from the affine quality of straight line features being represented as collinear points in the image plane. To utilize these non-SVP mirrors, a new method to recognize such features is required. This work describes an approach to detecting features in panoramic non-SVP images using a modified Hough transform. A mathematical model for this feature extraction process is given. Experimental results are presented to validate this model and show robust performance in identifying line features with only estimated calibration.     

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.     

A maximum noise fraction transform with improved noise estimation for hyperspectral images

Feature extraction is often performed to reduce spectral dimension of hyperspectral images before image classification. The maximum noise fraction(MNF) transform is one of the most commonly used spectral feature extraction methods. The spectral features in several bands of hyperspectral images are submerged by the noise. The MNF transform is advantageous over the principle component(PC) transform because it takes the noise information in the spatial domain into consideration. However,the experiments describ...     

Detection of circular geological features using the Hough transform

Abstract

The Hough transform is a technique commonly used in the field of computer vision for detecting linesand shapes in digital imagery. The application of this method to the detection of circular geological structures in Landsat MultispectralScannerimageryis described. The method wassuccessful in identifying most of those features apparent to the human analyst, in addition to a number of errors of commission. It is proposed that the error rate is tolerable for this particular application.     

Accurate and efficient curve detection in images: the importance sampling Hough transform

The Hough transform is a well known technique for detecting parametric curves in images. We place a particular group of Hough transforms, the probabilistic Hough transforms, in the framework of importance sampling. This framework suggests a way in which probabilistic Hough transforms can be improved: by specifying a target distribution and weighting the sampled parameters accordingly to make identification of curves easier. We investigate the use of clustering techniques to simultaneously identify multiple curves in the image. We also use probabilistic arguments to develop stopping conditions for the algorithm. Results from applying our method and two popular versions of the Hough transform to both simulated and real data are shown.     

基于Hough圆卷积变换的孔组检测

为降低孔组测量的成本,提高测量的精确度,提出一种基于Hough国卷积的孔组检测方法.对孔组图像进行中值滤波、拉普拉斯锐化增强和边缘检测;根据孔组半径大小的变化范围,对Hough圆锥进行改进;在此基础上,利用改进的Hough圆锥与边缘检测后的图像进行三维卷积,得到Hough参数空间,计算出孔组的坐标和半径值;采用快速傅立叶变换法代替三维卷积,减少算法的计算时间.实验结果表明,该方法的检测精度高、实时性强,可以用于孔组的自动化检测.     

Tool wear monitoring using a fast Hough transform of images of machined surfaces

The texture of machined surfaces provides reliable information regarding the extent of tool wear. In this paper, we propose a structure-based approach to analyzing machined surfaces. The original surface images are first preprocessed by a Canny edge detector. A new connectivity-oriented fast Hough transform is then applied to the edge image to detect all the line segments. The distributions of the orientations and lengths of the line segments are used to determine tool wear. Through our experiments, we found a strong correlation between tool wear and features. The computational complexity of the fast Hough transform is also analyzed.Received: 6 November 2002, Accepted: 18 December 2003, Published online: 13 May 2004 Correspondence to: A.A. Kassim     

基于Hough变换的输送带纵向撕裂检测方法

针对现有输送带纵向撕裂检测方法存在敏感度低、操作不便、非可视化等问题,提出了一种基于Hough变换的输送带纵向撕裂检测方法。该方法使用线光源辅助照射,利用CCD实时采集输送带下表面图像,通过Hough变换提取图像斜率差特征来判断输送带是否发生纵向撕裂。测试结果验证了该方法的有效性。     

Application of the Hough transform to correct for linear variation of background illumination in images

The Hough transform has been formulated to detect areas of linear brightness variation within an image. The formulation leads to significant computational advantage involving a restricted parameter search space. Two applications are included to demonstrate the efficacy of this technique.     

Optimising the application of the Hough transform for automatic feature extraction from geoscientific images

We have adapted the Hough transform to extract linear features successfully from geoscientific datasets. The Hough transform is used in an automatic technique, which makes use of a parameter space to describe features of interest in images. This method has been widely applied in machine vision for recognition of features in highly structured images. Geoscientific data is more demanding. Features of interest within scenes of natural environments exist on all scales, are often partially obscured and the images are usually noisy. Pre-processing of images before the HT is essential. Adaptations of the HT to cope with particular properties of geoscientific data include: optimising the dimensions of the discrete transform domain; using feature-modelling to cancel lines found; transforming multi-scale tiles of the original image and correcting amplitudes in the transformed domain to account for the position of features. These specific adaptations produce a method for automatic feature detection which requires the user to select only two parameters. Output of the procedure is rich in feature content and accurate, leaving a clean result for statistical analysis. This optimised HT is robust for natural scenes, coping in particular with short line-segments.     

Detection of linear and planar structures in 3D subsurface images by iterative dimension reduction

A unified framework for detecting both linear and planar structures in three-dimensional (3D) images is developed. The method uses an iterative detection and removal strategy. The dimension reduction scheme reduces the search space for lines by first finding 2D planes and then searching for lines in the selected planes only. Thus the computational time of the method is lower than the 3D Hough Transform (HT) for lines. The proposed method is tested using experimental Ground Penetrating Radar (GPR) data taken over buried pipes, however the method is general enough to be applied to any situation where linear or planar structures need to be identified in 3D data.     

Location of pupil contour by Hough transform of connectivity components

A method for determining the pupil boundary in the image of eye is proposed. The method is based on image binarization followed by a search of the pupil as one of the connectivity components. The pupil boundary is determined as a part of boundary of the connectivity component. Hough transform is used for separating pupil in the case of its merging in one connectivity component with other objects, as well as to verify the likelihood of solution.     

American sign language (ASL) recognition based on Hough transform and neural networks

The work presented in this paper aims to develop a system for automatic translation of static gestures of alphabets and signs in American sign language. In doing so, we have used Hough transform and neural networks which is trained to recognize signs. Our system does not rely on using any gloves or visual markings to achieve the recognition task. Instead, it deals with images of bare hands, which allows the user to interact with the system in a natural way. An image is processed and converted to a feature vector that will be compared with the feature vectors of a training set of signs. The extracted features are not affected by the rotation, scaling or translation of the gesture within the image, which makes the system more flexible.The system was implemented and tested using a data set of 300 samples of hand sign images; 15 images for each sign. Experiments revealed that our system was able to recognize selected ASL signs with an accuracy of 92.3%.     

Multiresolution Hough transform-an efficient method of detectingpatterns in images

A new multiresolution coarse-to-fine search algorithm for efficient computation of the Hough transform is proposed. The algorithm uses multiresolution images and parameter arrays. Logarithmic range reduction is proposed to achieve faster convergence. Discretization errors are taken into consideration when accumulating the parameter array. This permits the use of a very simple peak detection algorithm. Comparative results using three peak detection methods are presented. Tests on synthetic and real-world images show that the parameters converge rapidly toward the true value. The errors in ρ and &thetas;, as well as the computation time, are much lower than those obtained by other methods. Since the multiresolution Hough transform (MHT) uses a simple peak detection algorithm, the computation time will be significantly lower than other algorithms if the time for peak detection is also taken into account. The algorithm can be generalized for patterns with any number of parameters     

Detection of an ellipse by use of a recursive least-squares estimator

A method for detecting an ellipse in an image by use of a recursive least-squares estimator is described. This method is based on classifying the behavior of parameters in a quadratic equation estimated by the estimator. The segmentation of an image is executed using a statistical measure called the adjacent separability factor. Noise elimination is performed by the expansion-contraction method and a contour for each region is extracted. Points on the contour are supplied to a recursive least-squares estimator in succession and the parameters in a quadratic equation are estimated recursively. It is possible to decide whether the contour is an ellipse by analyzing the behavior of an estimated parameter. A main feature of this method is the generality and the simplicity of the procedure.     

小波变换在信号降噪中的应用研究

由于小波变换具有良好的时-频特性,从而为其在信号降噪中的应用提供了广阔的前景.本文通过傅立叶变换和小波变换在信号降噪中的对比研究可以看出,小波变换在信号降噪应用方面具有得天独厚的优越性.本文同时对小波降噪过程、小波域阈值选取原则和小波基选取原则进行了讨论,得出了一些有价值的规律和原则.     

Efficient pan-sharpening of satellite images with the contourlet transform

Recent studies show that hybrid panchromatic sharpening (pan-sharpening) methods using the non-sub-sampled contourlet transform (NSCT) and classical pan-sharpening methods such as intensity, hue and saturation (IHS), principal component analysis (PCA), and adaptive principal component analysis (APCA) reduce spectral distortion in pan-sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on non-sub-sampled pyramid (NSP) decomposition and non-sub-sampled directional filter banks (NSDFBs). We compare the performance of the APCA–NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on SPOT 4 data with a spatial resolution ratio of 1:2, and Quickbird data with a spatial resolution ratio of 1:4. Experimental results show that the quality of pan-sharpening of remote-sensing images of different spatial resolution ratios using the APCA–NSCT method is affected by NSCT parameters. For the NSP, the ‘maxflat’ filters have the best quality, while the ‘sk’ filters give the best quality for the NSDFB. Changing the number of orientations at the same level of decomposition in the NSCT has a small effect on both the spectral and spatial qualities. The spectral and spatial qualities of pan-sharpened images mainly depend on the number of decomposition levels. Too few decomposition levels result in poor spatial quality, while excessive levels of decomposition result in poor spectral quality. Two levels of decomposition in the case of SPOT 4 data with a spatial resolution ratio of 1:2 achieve the best results. Also, three levels of decomposition in the case of QuickBird data with a spatial resolution ratio of 1:4 show the best results.     

Enclosing the solution set of linear systems with inaccurate data by iterative methods based on incomplete LU-decompositions

We present a class of iterative methods to enclose the solution set by an interval vector;A is varying in ann×n intervalH-Matrix andb is varying in an interval vector . The algorithm taken into consideration generalizes an iterative method of Meijerink/van der Vorst based on an incompleteLU-decomposition of anM-MatrixA. Theorems concerning the feasibility of the algorithm, its rate of convergence and its quality of enclosure are given. Since the original method of Meijerink/van der Vorst is a special case of our algorithm we have thus shown its applicability to the larger class ofH-matrices. Furthermore we relate theR ₁-factor (as defined in Ortega/Rheinboldt [9]) of the original method to the underlying setP of indices.     

Hough变换在表面污渍检测中的应用

针对具体芯片图像的形状特征,提出了基于改进Hough变换的几何配准算法.该算法利用边缘点的梯度方向信息,以及通过圆上任意一点并沿着该点法线方向的直线必定经过该圆的圆心这一几何性质,将三维Hough变换空间简化为二维空间,较好地解决了传统广义Hough变换运算时间长、存储空间大的问题.实验表明,可以有效地对两幅图像进行配准,从而检测出芯片表面的污渍.     

变换域SAR图像边缘检测

针对合成孔径雷达(SAR)图像含有大量相干斑噪声,采用变换域中多尺度乘积进行边缘提取.该方法利用信噪比最大自动选取阈值,在非下采样Contourlet变换(NSCT)域中,进行不同方向高频系数多尺度乘积以达到SAR图像检测边缘.实验结果显示,与传统的边缘检测方法相比,在非下采样Contourlet变换域中多尺度积的信噪比最大的边缘检测方法得到更好的结果.