Fast circle detection algorithm using sequenced Hough transform

Feng, 叶峰 Ye; Can-jie, 陈灿杰 Chen; Yi-zong, 赖乙宗 Lai; Jian-dong, 陈剑东 Chen

doi:10.3788/ope.20142204.1104

Cited by 6 publications

(3 citation statements)

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“…At the same time, to speed up the accumulation, the cumulative range is limited in Eqs. ( 9) and (10). At last, the center position ðx cent ; y cent Þ is obtained through the first-level HT.…”

Section: Tsht Algorithm For Detecting the Circle Centermentioning

confidence: 99%

“…8 Xia et al 9 put forward a new algorithm, called the offset Hough transform (OHT). This obtains the mapping coordinate set of all edge points by translating the mapping coordinate set of the datum point in the parameter space only using HT, 10 which greatly improves the speed of single target detection but not the recognition accuracy. Objects that are coherent in the Euclidean geometry input images are clearly obvious when examined in Hough parameter space because they give curves that meet at a single point.…”

Section: Introductionmentioning

confidence: 99%

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Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Wang

Chen

et al. 2022

J. Electron. Imag.

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A highly efficient circle positioning algorithm, called the two-step optimization Hough transform (TSHT), based on multi-resolution segmentation is proposed to solve the problems of the offset Hough transform, namely, its large memory overhead, long time consumption, and low recognition accuracy. First, using the image feature of the printed circuit board (PCB) circular identifier, the target circle is obtained using adaptive image preprocessing, and then, images of an acceptable quality are separated by shape quality inspection to improve their robustness. Second, using effective interval sampling strategies and gradually controlling the accumulative interval of parameters, the TSHT algorithm reduces the memory overhead and quickly locates the center at the pixel level. Finally, the center at the sub-pixel level is found by the least-squares method for circle fitting. The experiments prove that TSHT, as a result of its high robustness, strong anti-noise capability, fast recognition speed, and accuracy, can be successfully applied to a vision positioning system of a solder paste printing machine.

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Section: Tsht Algorithm For Detecting the Circle Centermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%