In this paper, a novel mathematical morphological approach is proposed, which is combined with an active threshold-based method for the identification of morphological features from images with poor qualities. The alg...In this paper, a novel mathematical morphological approach is proposed, which is combined with an active threshold-based method for the identification of morphological features from images with poor qualities. The algorithm is very fast and needs low computing power.First, a mixed smooth filtering is designed to remove background noises. Second, an active threshold-based method is discussed to create a binary image to achieve rough segmentation. Third, some simple morphological operations, such as opening, closing, filling, and so on, are designed and applied to get the final result of segmentation. After morphological analysis, morphological features, such as contours, areas, numbers, locations, and so on, are obtained. Finally, the comparisons with other conventional methods validate the effectiveness, and an additional experimental result proves the repeatability of the proposed method.展开更多
基金This research is partly supported by the innovative research fund of aerospace, research fund for the program of new century excellent talents in Heilongjiang provincial university No. 1155-ncet-008 and the Natural Science Foundation of Heilongjiang Province under grant No. QC2015084, F201132.
文摘In this paper, a novel mathematical morphological approach is proposed, which is combined with an active threshold-based method for the identification of morphological features from images with poor qualities. The algorithm is very fast and needs low computing power.First, a mixed smooth filtering is designed to remove background noises. Second, an active threshold-based method is discussed to create a binary image to achieve rough segmentation. Third, some simple morphological operations, such as opening, closing, filling, and so on, are designed and applied to get the final result of segmentation. After morphological analysis, morphological features, such as contours, areas, numbers, locations, and so on, are obtained. Finally, the comparisons with other conventional methods validate the effectiveness, and an additional experimental result proves the repeatability of the proposed method.
