Detecting Circles Using a Two-Stage Approach

We propose a two-stage method for detecting circular objects in this paper. In the first stage, curves are divided as linear segments or nonlinear segments. A least square estimator is used to find the estimated centers and radii of the nonlinear segments in the second stage. The found centers and radii are then evaluated to see if there exist circles in the nonlinear segments. Both of the broken and occluded circular objects are evaluated for the proposed method. From the experimental results, it is seen that the proposed method is efficient.

CIRCLE DETECTION USING FAST FINDING AND FITTING (FFF) ALGORITHM

This paper describes a Fast Finding and Fitting (FFF)Algorithm based on the geometric symmetry of the circle. A comparison with other circle detection approaches, in par- ticular the Hough Transform and its extensions is presented. A method to determine a rele- vant estimation of accuracy is also given. Experiments show that FFF possesses advantages in memory usage, high speed, reliability and accuracy are also presented.

TRIPLET CIRCULAR HOUGH TRANSFORM FOR CIRCLE DETECTION

A new method, triplet circular Hough transform, is proposed for circle detection in image processing and pattern recognition. In the method, a curve in an image is first detected. Next, a sequence of three points on the curve are selected, a sequence of parameters (a,b,r) corresponding to the three points are calculated by solving the circle equation of the curve, and two 2-D accumulators A(a,b) and R(a,b) are accumulated with 1 and r, respectively. Then the parameters {(a, b, r)} of the circles fitting the curve are determined from A(a,b) and R(a,b) by searching for the local maximum over A(a,b). Because no computation loops over center (a, 6) and/or radius r are needed, the method is faster than the basic and directional gradient methods. It needs also much smaller memory for accumulation.

An Improved Randomized Circle Detection Algorithm Using in Printed Circuit Board Locating Mark

This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).

Circle Detection Based on HSI Color Space

A method based on HSI color space is presented to solve the problem of circle detection from color images. In terms of the evaluation to the edge detection method based on intensity, the edge detection based on hue is chosen to process the color image, and the simplified calculation of hue transform is discussed. Then the algorithm of circle detection based on Canny edge detection is proposed. Due to the dispersive distribution of the detected result, Hough transformation and template smooth are used in circle detection, and the proposed method gives a quite good result.

Study on circle detection algorithm based on data dispersion

To reduce time-consuming,a new algorithm is proposed for circle detection based on the theory of data dispersion. The center coordinates and radius can be detected with the following steps in this algorithm precisely and quickly. Firstly,image processing is needed to extract the boundary of the primary image,which is almost like a circle in shape,and after that,the original circle is reduced to a single-pixel width circle by image processing. Secondly,the center coordinates are calculated by three selected points on the circle. There might be a deviation between the calculated center and real center. Thirdly,a square area is determined for the center coordinates computing with an experimental range and each pixel inside the square is a potential center. Fourthly,the center is computed with distance criterion and the center coordinate is determined when the variance reaches the minimum. Lastly,the radius is equal to the means of the distance vector with minimum variance.Experiments are conducted and the results show that the proposed algorithm gets the same accuracy and better real-time performance in comparison with traditional Hough transform.

Efficient circle detection by midpoint circle validation

Circular objects detection in digital images, a vital and recurring problem in image processing and computer vision, has many applications especially aiding vectorization of line drawing images, pupil and iris detection, circular traffic sign detection, and so on. In this paper, we lever Midpoint Circle Algorithm to speed up circle validation with sub-pixel precision. At the same time, we combine the least square method to improve the accuracy of circle detection. Experimental results from tests on synthetic and natural images validate that the proposed technique is efficiency regarding accuracy, speed and robustness.

Position detection of component aperture based on integral imaging technology

Aiming at the problem that it is difficult to locate all the aperture positions of the large size component using Houghcircle detection method,this article presents a non-contact measurement method combining the integral imaging technology withHough circle detection algorithm.Firstly,a set of integral imaging information acquisition algorithms were proposed accordingto the classical imaging theory.Secondly,the camera array experiment device was built by using two-dimensional translationstage and charge coupled device(CCD)camera.When the system is operating,element image array captured with the camera isused to achieve the positioning of the component aperture using Hough circle detection and coordinate acquisition algorithm.Based on the above theory,a verification experiment was carried out.The results show that the detection error of the componentaperture position is within0.3mm,which provides effective theoretical support for the application of integral imagingtechnology in high precision detection

New method for recognition of circular traffic sign based on radial symmetry and pseudo-zernike moments

Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.

On-orbit real-time robust cooperative target identification in complex background

Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm for cooperative target identification in complex background using the features of circle and lines. It first extracts only the interested edges in the target image using an adaptive threshold and refines them to about single-pixel-width with improved non-maximum suppression. Adapting a novel tracking approach, edge segments changing smoothly in tangential directions are obtained. With a small amount of calculation, large numbers of invalid edges are removed. From the few remained edges, valid circular arcs are extracted and reassembled to obtain circles according to a reliable criterion. Finally, the target is identified if there are certain numbers of straight lines whose relative positions with the circle match the known target pattern. Experiments demonstrate that the proposed algorithm accurately identifies the cooperative target within the range of 0.3 1.5 m under complex background at the speed of 8 frames per second, regardless of lighting condition and target attitude. The proposed algorithm is very suitable for real-time visual measurement of space robot arm because of its robustness and small memory requirement.