Sati M, Laroye G J
l'Institut du Génie Biomédical, Montréal, Québec, Canada.
Anal Quant Cytol Histol. 1994 Aug;16(4):269-83.
The algorithm described here identifies concave and convex segments of a closed contour by vector algebra, thereby defining the shape of a two-dimensional object in multiple ways: (1) as a ratio of total concave vs. total convex periphery length, (2) as the number of convexity changes per unit of perimeter length (or number of lobes per contour), (3) as the mean +/- SD of convex and/or concave segment lengths, and (4) as a measure of the discrete curvature from a finite number of points on the contour. The algorithm can be used, in its capacity to distinguish regularity and amplitude of indentations ("wrinkles" and "lobes"), in studies where the number, regularity and magnitude of surface fluctuations are important differentiating morphologic characteristics of the object. This short algorithm can easily be integrated among other classic algorithms measuring periphery, area, shortest and longest diameter, and form factors derived therefrom. The possibility of automating this method makes it possibly useful for the discrimination of shapes by artificial vision.
这里描述的算法通过向量代数识别闭合轮廓的凹凸部分,从而以多种方式定义二维物体的形状:(1)作为总凹边与总凸边周长的比值;(2)作为每单位周长长度的凸度变化数量(或每个轮廓的叶数);(3)作为凸段和/或凹段长度的均值±标准差;(4)作为来自轮廓上有限数量点的离散曲率的度量。该算法能够区分凹陷(“皱纹”和“叶”)的规则性和幅度,可用于研究表面波动的数量、规则性和大小是物体重要形态特征差异的情况。这种简短的算法可以很容易地集成到其他测量周长、面积、最短和最长直径以及由此得出的形状因子的经典算法中。自动化此方法的可能性使其可能在人工视觉形状判别中有用。