An efficient two-scan algorithm for computing basic shape features of objects in a binary image

An efficient two-scan algorithm for computing basic shape features of objects in a binary image
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一种计算二值图像中物体基本形状特征的高效两次扫描算法

DOI:
10.1007/s11554-016-0626-7
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发表时间:
2019-08
影响因子:
3
通讯作者:
姚斌
姚斌
中科院分区:
计算机科学4区
文献类型:
--
作者:
何立风;任喜伟;赵晓;姚斌

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二值图像中目标的基本形状特征,即面积、周长、圆度和质心,对于图像分析和模式识别具有重要意义。在传统算法中,为了计算二值图像中对象的基本形状特征,通常需要首先执行连通分量标记以生成标记图像(中间图像),其中每个图像对象被分配唯一的标记以便可以区分它。使用标记的图像,然后可以计算对应于每个标签的对象的基本形状特征。当使用两次扫描标记算法时,需要三次扫描。提出了一种计算二值图像中目标形状特征的有效算法。该算法不使用已标记的图像,而是使用有效的两次扫描标记算法第一次扫描生成的代表性标签表和图像来计算对象的基本形状特征。因此,我们可以使用两次扫描来计算形状特征。实验表明,该算法在计算二值图像中目标的基本形状特征时,比传统算法具有更高的效率。
The basic shape features of an object in a binary image, i.e., the area, perimeter, circularity, and centroid, are important for image analysis and pattern recognition. In conventional algorithms, to calculate the basic shape features of objects in a binary image, it is usually necessary to first perform connected-component labeling to generate a labeled image (intermediate image), in which every image object is assigned a unique label so that it may be distinguished. Using the labeled image, the basic shape features of the object corresponding to each label can then be calculated. When a two-scan labeling algorithm is used, three scans are necessary. This paper proposes an efficient algorithm for calculating the shape features of objects in a binary image. Instead of a labeled image, our proposed algorithm calculates the basic shape features of objects using the image and the representative label table generated by the first scan of an efficient two-scan labeling algorithm. Thus, we can compute shape features using two scans. Experiments demonstrate that our proposed algorithm is much more efficient than conventional algorithms for calculating the basic shape features of objects in a binary image.
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