發(fā)布時(shí)間：2018-08-14 11:05

【摘要】：為了提高復(fù)雜圖像中橢圓/類橢圓的檢測(cè)效率及精度,提出了一種基于特征弦約束的隨機(jī)Hough變換(RHT)思想的橢圓檢測(cè)改進(jìn)方法,借助特征弦的幾何約束及特征弦端點(diǎn)的法向約束,大幅度降低RHT的無(wú)效采樣和累積次數(shù)。通過(guò)對(duì)邊緣圖像中像素點(diǎn)的有效分布進(jìn)行分析,建立用于存儲(chǔ)特征弦的端點(diǎn)信息的二維數(shù)組累加器,在邊界點(diǎn)提取之前利用橢圓冪剔除虛假橢圓中心的干擾,不僅能夠提高空間采樣點(diǎn)的可靠性,同時(shí)降低無(wú)效采樣點(diǎn)的累積概率。實(shí)驗(yàn)表明,該算法具有更高的運(yùn)行速度和檢測(cè)精度,同時(shí)對(duì)于形變較大,輪廓缺失嚴(yán)重及噪聲具有較強(qiáng)的抵抗能力。
[Abstract]:In order to improve the efficiency and accuracy of ellipse/quasi-ellipse detection in complex images, an improved method of ellipse detection based on the idea of random Hough transform (RHT) with feature string constraints is proposed. By means of geometric constraints of feature string and normal constraints of feature string endpoints, the number of invalid sampling and accumulation of RHT is greatly reduced. By analyzing the effective distribution of pixels, a two-dimensional array accumulator is built to store the endpoint information of feature chords. Elliptic power is used to eliminate the interference of false ellipse center before extracting boundary points, which not only improves the reliability of spatial sampling points, but also reduces the cumulative probability of invalid sampling points. Higher operating speed and detection accuracy, and for large deformation, serious contour loss and strong resistance to noise.
【作者單位】： 長(zhǎng)春理工大學(xué)光電工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61605016) 吉林省科技發(fā)展計(jì)劃(20160520018JH)項(xiàng)目資助
【分類號(hào)】：TP391.41

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本文編號(hào)：2182651