Hough Transform Technique Research Articles

An Efficient Way to Detect and Recognize the Overlapped Coins using Otsu's Algorithm based on Hough Transform Technique

The competency to detect and to recognize the different denomination of coins is an essential expertise in the human beings life. This paper represents different algorithms for the detection and the recognition of coins which are highly overlapped. The proposed system includes three principal steps detection, extraction and recognition. This system is efficient in detecting and recognizing the coins from both the sides. The system uses Otsu’s algorithm for segmentation process, for detecting overlapping uses hough transform and recognizing the coin uses radius thresholding. General Terms Segmentation, Coin Detection, Coin Recognition.

Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision

This paper presents a new method of value identification of pointer-type pressure gauges based on machine vision, by which an automatic calibration system can be designed. Hough Transformation, image subtraction and image thinning technique are employed to obtain the coordinate of the pointer rotating center. Through removing the pixels data outside of the circumference of the pointer in the single panel image, the linear equation of the pointer is achieved after the processing of image thinning and Hough transformation. Experiments are carried out on a pressure calibration device. The results show that the accuracy and speed of the automatic identification of the pressure gauge is improved.

A Multi-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Weak Targets in Strong Clutter Backgrounds

The Hough transform (HT) algorithm detects straight-line features in two-dimensional data. This correspondence extends the HT to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -dimensional data to efficiently combine multiple first-threshold crossings from moving targets. The data dimensions can be the target position, its range and range-rate, and/or the first-threshold crossing times. This multi-dimensional HT (MHT) technique can be applied to enhance the detection of targets in random clutter backgrounds through the application of track-before-detect (TBD) processing.

Unmanned aerial vehicle navigation and mapping

The unmanned aerial vehicle (UAV) has significant advantages over the ground vehicle, where it can achieve a high degree of manoeuvrability, high speed response time and the ability of large area coverage. The problem considered here is that of an UAV localization and mapping using an extended Kalman filter (EKF), interval analysis (IA), covariance intersection (CI) and Hough transform (HT) for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are known to some extent. The vehicle is localized with respect to the known obstacles and to recognise the unknown obstacles to update the map of the environment. The focus is to develop an approach which can give a guaranteed performance of sensor-based localization and mapping that would increase the safety of the aerial vehicle and to produce a better performance in building the map of the environment. The guaranteed performance is quantified by explicit bounds of the position estimate of the vehicle. Generally, the UAVs carry the required sensors such as inertial sensors, accelerometers, and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and the map-making is carried out with time of flight sensors such as ultrasonic or laser sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. This task of data fusion is accomplished by combining the measurements from different sensors that are obtained from two different methods namely EKF and IA, and by processing these measurements with a data fusion algorithm using the CI principle. This fused information is used with the measurements from the time of flight sensors such as laser sensor to update the map of the environment by applying the HT technique. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components, which in turn, improves the accuracy and richness in updating the map of the partially known environment. This proposed intelligent sensor system can provide a mathematically provable performance guarantees that are achievable in practice.

Using the Hough Transform to Segment Ultrasound Images of Longitudinal and Transverse Sections of the Carotid Artery

Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. In this paper, the Hough transform (HT) was used to automatically extract straight lines and circles from sequences of B-mode ultrasound images of longitudinal and transverse sections, respectively, of the carotid artery. In 10 normal subjects, the specificity and accuracy of HT-based segmentation were on average higher than 0.96 for both sections, whereas the sensitivity was higher than 0.96 in longitudinal and higher than 0.82 in transverse sections. The intima-media thickness (IMT) was also estimated from images of longitudinal sections; the corresponding validation parameters were generally higher than 0.90. To further validate the results, arterial distension waveforms (ADW) were estimated from sequences of images using the HT technique as well as motion analysis using block matching (BM). In longitudinal sections, diastolic and systolic diameters and relative diameter changes using HT and BM were not significantly different. In transverse sections, diastolic and systolic diameters were significantly lower using the HT technique; the differences were <7%. Relative diameter changes in transverse sections were not significantly different from BM-estimated ones. The HT technique was also applied to four subjects with atherosclerosis, in which sensitivity, specificity and accuracy were comparable to those of normal subjects; the low values of sensitivity in transverse sections may reflect departure from the circular model because of the presence of plaque. In conclusion, the HT technique provides a reliable way to segment ultrasound images of the carotid artery and can be used in clinical practice to estimate indices of arterial wall physiology, such as the IMT and the ADW. (E-mail: knikita@cc.ece.ntua.gr)

Locating Reinforcing Bars in Concrete Structures Using Generalized Hough Transform of Radar Image

Locating reinforcing bars, in particular to know their accurate depths, is very important in radar inspection of concrete structures. By the way, an accurate depth estimation of reinforcing bars in concrete structures by the radar is not easy because the microwave propagation velocity in test area is generally unknown. This problem can be solved by generalized Hough transformation technique. Using this technique, the microwave propagation velocity in test area cen be detected from the radar image, which appear as hyperbolas conveying the velocity information in their shape. A developed speed-up technique for the computation of the Generalized Hough transformation is also investigated in this study. As a result, although it becomes difficult to locate reinforcing bars when multiple parallel bars lying too close together, there is a possibility of detecting accurate depths of reinforcing bars in test area by the proposed method

Circle recognition through a 2D Hough Transform and radius histogramming

We present a two-step algorithm for the recognition of circles. The first step uses a 2D Hough Transform for the detection of the centres of the circles and the second step validates their existence by radius histogramming. The 2D Hough Transform technique makes use of the property that every chord of a circle passes through its centre. We present results of experiments with synthetic data demonstrating that our method is more robust to noise than standard gradient based methods. The promise of the method is demonstrated with its application on a natural image and on a digitized mammogram.

Determining Fiber Orientation Distribution in Nonwovens with Hough Transform Techniques

This research explores the application of an image transform technique, the Hough transform (ht), to measuring fiber orientation distribution in nonwoven fabrics. In this paper, various forms of the ht for straight lines and the implementing algorithms are presented, the factors that may influence the accuracy of ht results are discussed, and the way to determine fiber orientation distribution from ht data for a nonwoven is explained. The usefulness of the ht method is tested using simulated nonwoven images with known dominant fiber orientations, and nonwoven fabrics differing in fiber content, weight, and other characteristics. Fiber orientation distributions generated by the ht method are also compared with those obtained from the zero-span tensile testing method. The principal advantages of using the ht are that it is robust in suppressing image noise and is able to deal with fibers containing gaps and breaks. Measurements with the ht method show good correlation with those obtained from other methods. The ht appears to be an effective and efficient image analysis technique for extracting information about fiber orientation from images of nonwoven fabrics.

A three-dimensional non-contact measurement system

A simple three-dimensional (3D) non-contact measurement system was proposed which consisted of a 1D laser displacement sensor and a servo control coordinate system. The laser sensor was installed on theZ-axis perpendicular to theX, Y-holding table. A filter was designed to remove the noise in the measured data. An extraction technique of characteristic points was proposed to catch the edge data points of the measured objects. Then the Hough-transform technique of image processing was employed to calculate the position and dimension of the edge of measured objects based on the extracted characteristic points. The experimental results show that this simple structure can effectively measure the position and dimension of 3D objects with reasonable accuracy.

AUTOMATED MEASUREMENT OF AGGREGATE INDICES OF SHAPE

ABSTRACT Aggregate angularity is a significant factor in determining pavement resistance to rutting. Determination of aggregate angularity by direct means allows a more meaningful study of the mechanisms of pavement deformation. However, direct techniques have not been developed sufficiently to allow a more advanced study of angularity effects on pavement. Utilizing modern digital imaging hardware and image analysis techniques, we report here the development of an automated system for measuring aggregate angularity. Combining high resolution video, image capture hardware, sample transport, and computerized analysis, an instrument was constructed and used to compare various aggregate samples. Differentiation of known good quality aggregate from poorer grades was possible. Shape indices were developed from basic linear measurements, and the Hough Transform technique. These indices appear to have value in quantifying aggregate shape, and in studying the mechanisms of pavement deformation, and possibly the prediction of pavement longevity.

Houghtool — A software package for the use of the Hough transform

The Hough Transform (HT) is a popular method for detecting curve segments in an image. A software package, the Houghtool, is proposed to calculate the HT. Several methods for line detection are implemented in the package. These include new HT techniques such as probabilistic approaches which use random sampling of the input points. The aim of the Houghtool is to provide general tools for the design of new algorithms and to provide solutions to specific applications. The software design is flexible to allow user extension of the tools provided. A graphical user interface, the XHoughtool, is provided for the visualization of the HT calculation.

Computer vision based morphometric characterization of neural cells

This article reports on the application of computer vision techniques, implemented on an IBM PC-compatible machine, as a means of obtaining morphometric characterization of neural cells. The features considered include projection polar histograms, orientation polar histograms, and the determination of the convex hull of dendritic (or axonal) arborizations. As will be discussed in this work, the Hough transformation technique has proven to be of great help for the determination of all the above-mentioned parameters. Two actual application examples respective to the analysis of retinal ganglion cells are also presented and discussed.

Hough techniques for fast optimization of linear constant velocity motion in moving influence fields

Consider a projectile that must be launched at a given time from a given point into linear constant velocity motion, facing possible encounters with other objects moving at known constant speeds. The cost (or utility) of interaction between the projectile and each object is quantified by an essentially arbitrary, possibly object-specific, function of proximity. It is shown that this constrained problem framework allows fast approximation of the globally optimal projectile velocity using Hough-Transform techniques.

Robust statistical methods of 2D and 3D image description

In this paper the problem of image feature extraction is considered with emphasis on developing methods which are resilient in the presence of data contamination. The issue of robustness of estimation procedures has received considerable attention in the statistics community [1–3] but its results are only recently being applied to specific image analysis tasks [4–7]. In this paper we show how the design of robust methods applies to image description tasks posed within a statistical hypothesis testing and parameter estimation framework. The methodology is illustrated by applying it to finding robust, optimal estimation kernels for line detection and edge detection. We then discuss the relationship of these optimal solutions to both the well established Hough Transform technique and the standard estimation kernels developed in the statistics literature. The application of standard robust kernels to image analysis tasks is illustrated by two examples which involve circular arc detection in gray-level imagery and planar surface segmentation in depth data. Robust methods are found to be effective general tools for generating 2D and 3D image descriptions.

一般化Hough変換による地中探査画像の解析

一般化Hough変換による地中探査画像の解析

Determining orientation, location and size of primitive surfaces by a modified hough transformation technique

This paper proposes a new computational approach to determining parameters that represent the orientation, location and size of primitive surfaces. The method, which is motivated from the Hough transformation method, estimates these parameters by transforming the image's needle maps into the parameter space of the surfaces. In order to reduce the great computational expense of the Hough transform method, we firstly divide the parameter space into three subspaces on the basis of the relationships between the image's needle maps and the parameters. Then the Hough transformation method is iteratively used and the parameters are derived sequentially by clustering in each subspace. This procedure has been successfully applied to a number of real images, two of which we present in the paper.

Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns

Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recognition, grammatical inference, and syntax analysis. The seismic patterns are automatically recognized and reconstructed.