Circle Detection Method Research Articles

An Approach for Detecting Regular Hexagon

Regular hexagon is a kind of common geometric figure. This paper proposes a vision detection method based on geometric characteristics to detect regular hexagon. First, line segments are detected in the image by hough transform. The line equations are constructed according to the detected line segments in the image space. Second, the line equations are utilized to compute the intersection points of adjacent two lines. The lengths of every side of regular hexagon are gotten by computing the distances between adjacent two intersection points. The angles between adjacent lines are calculated. Then the circle detection method based hough transform is employed to inspect whether all intersection points locate at the same circumference. Finally, the detection experiment of hexagon bolt is constructed to illustrate the performance of the presented method. The measurement errors of angles between adjacent two sides and lengths of sides of regular hexagon are 0.38% and 0.85% respectively. The result shows that the presented method is effective and can precisely detect regular hexagon in the image.

Circle detection using discrete differential evolution optimization

This paper introduces a circle detection method based on differential evolution (DE) optimization. Just as circle detection has been lately considered as a fundamental component for many computer vision algorithms, DE has evolved as a successful heuristic method for solving complex optimization problems, still keeping a simple structure and an easy implementation. It has also shown advantageous convergence properties and remarkable robustness. The detection process is considered similar to a combinational optimization problem. The algorithm uses the combination of three edge points as parameters to determine circle candidates in the scene yielding a reduction of the search space. The objective function determines if some circle candidates are actually present in the image. This paper focuses particularly on one DE-based algorithm known as the discrete differential evolution (DDE), which eventually has shown better results than the original DE in particular for solving combinatorial problems. In the DDE, suitable conversion routines are incorporated into the DE, aiming to operate from integer values to real values and then getting integer values back, following the crossover operation. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images. Experimental results on several synthetic and natural images with varying range of complexity validate the efficiency of the proposed technique considering accuracy, speed, and robustness.

Fast detection of multi-circle with randomized Hough transform

Randomized Hough transform (RHT) is an effective method for circle detection. But when dealing with multi-circle complex images, the random sampling will bring lots of invalid accumulations and result in a large number of calculations. In this paper, by selecting three points of the candidate circle, a fast detection algorithm of multi-circle with randomized Hough transform is presented. Experimental results demonstrate that the proposed scheme can quickly detect multiple circles, and has a strong robustness.

하이브리드 실루엣 기반 인간의 강인한 특징 점 추출

In this paper, we propose the robust features extraction method of human by using the skeleton model and hybrid silhouette model. The proposed feature extraction method is divided by hands, shoulder line and elbow region extraction. We use the peer`s color information to find the position of hands and propose the circle detection method to extract the shoulder line and elbow. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

一种基于圆内接直角三角形的圆检测方法

一种基于圆内接直角三角形的圆检测方法

Circle detection on images using genetic algorithms

In this paper, we present a circle detection method based on genetic algorithms. Our genetic algorithm uses the encoding of three edge points as the chromosome of candidate circles ( x, y, r) in the edge image of the scene. Fitness function evaluates if these candidate circles are really present in the edge image. Our encoding scheme reduces the search space by avoiding trying unfeasible individuals, this results in a fast circle detector. Our approach detects circles with sub-pixellic accuracy on synthetic images. Our method can also detect circles on natural images with sub-pixellic precision. Partially occluded circles can be located in both synthetic and natural images. Examples of the application of our method to the recognition of hand-drawn circles are also shown. Detection of several circles in a single image is also handled by our method.

An effective voting method for circle detection

The drawbacks of the standard Hough transform (SHT) are the large computation and the large storage requirement. Moreover, the voting method of SHT plays an important role to affect the pattern detection accuracy. In this paper, a new voting method is proposed to reduce the computation and the storage requirements of SHT for circle detection. This method improves the efficiency of circle detection by letting each pixel only belong to one candidate of circle parameters. Synthetic images and real images are used to show the capability of the proposed method.

A new algorithm for ball recognition using circle Hough transform and neural classifier

A large number of methods for circle detection have been studied in the last years for several image processing applications. The context application considered in this work is the soccer game. In the sequences of soccer images it is very important to identify the ball in order to verify the goal event. This domain is a challenging one as a great number of problems have to be faced, such as occlusions, shadows, objects similar to the ball, real-time processing and so on. In this work a visual framework trying to solve the above-stated problems, mainly considering real-time computational aspects, has been developed. The ball detection algorithm has to be very simple in terms of time processing and also has to be efficient in terms of false positive rate. Our framework consists of two sequential steps for solving the ball recognition problem: the first step uses a modified version of the directional circle Hough transform to detect the region of the image that is the best candidate to contain the ball; in the second step a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found. Some tricks like background subtraction and ball tracking have been applied in order to maintain the search of the ball only in limited areas of the image. Different light conditions have been considered as they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light, as the light source is strictly directional, the ball, due to self-shades, appears as a spherical cap; this case has been taken in account and the search of the ball has been modified in order to manage this situation. A large number of experiments have been carried out showing that the proposed method obtains a high detection score.

Size invariant circle detection

The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Various modifications to the basic CHT operation have been suggested which include: the inclusion of edge orientation, simultaneous consideration of a range of circle radii, use of a complex accumulator array with the phase proportional to the log of radius, and the implementation of the CHT as filter operations. However, there has also been much work recently on the definition and use of invariance filters for object detection including circles. The contribution of the work presented here is to show that a specific combination of modifications to the CHT is formally equivalent to applying a scale invariant kernel operator. This work brings together these two themes in image processing which have herewith been quite separate. Performance results for applying various forms of CHT filters incorporating some or all of the available modifications, along with results from the invariance kernel, are included. These are in terms of an analysis of the peak width in the output detection array (with and without the presence of noise), and also an analysis of the peak position in terms of increasing noise levels. The results support the equivalence between the specific form of the CHT developed in this work and the invariance kernel.

Efficient technique for circle detection using hypothesis filtering and Hough transform

A fast circle detection method using a variant of Hough-like technique is reported. The proposed technique is simple in implementation, efficient in computation and robust to noise. In general, to evaluate circle parameters for all possible point triplets in an edge image containing n points, /sub n/C/sub 3/ enumerations of the points have to be examined. However, if specific relations of the circle points are sought, the required number of enumerations can be reduced. The authors propose one such scheme of detection with point triplets possessing right angle property and the required enumerations can be reduced to /sub n/C/sub 2/. Moreover, a novel processing strategy known as hypothesis filtering is introduced. The strategy includes two hypothesis constraints termed consistency checking with gradient angles and neighbouring points validation. Experimental results are demonstrated to reveal the performance of the method in detecting circles in both synthetic and real images. Since the proposed method adopts a right angle criterion for hypothesis, circles occluded or broken by more than one half may not be detected. Test results show that the limitation of the proposed method appears to be acceptable. When compared with established Hough transform techniques, the main strengths of the proposed detection method are its attractively computational and memory complexities and good accuracy of detection.

Comparative study of Hough Transform methods for circle finding

A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The five methods considered are the standard Hough Transform, the Fast Hough Transform of Li et al. 1, a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein 2. The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.