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.

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).

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.

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.