基于分水岭和梯度的蝴蝶兰图像分割方法

利用图像识别技术采集设施蝴蝶兰生长参数,从而对花期进行调控,是提高蝴蝶兰种植效益的重要手段,而如何把蝴蝶兰图像与自然背景图像进行分割与提取,是图像识别的关键。该文利用彩色梯度算法,提取出蝴蝶兰自然图像的梯度图像,利用阈值找出梯度图像的显著部分(即图像中的显著边缘),同时利用水域分割方法对源图像进行分割,产生过分割图像,然后利用显著边缘图像对过分割图像进行判断,去除不显著的"水坝"并令其两边水域相融合,从而达到极大的抑制过分割的目的,最后再根据区域合并准则合并相似的区域,得到蝴蝶兰目标物图像。针对20幅蝴蝶兰图像,通过与人工分割的方法进行对比试验,结果表明,该文提出的基于梯度和分水岭的分割算法能够很好地从自然背景中提取出蝴蝶兰图像,分割正确率达到了92.6%。

Image segmentation algorithm for Phalaenopsis amabilis based on watershed algorithm and gradient

It is an important method to improve the planting profit of Phalaenopsis ababilis by the control of its anthesis through image identification technology to acquire the growing parameters of greenhouse Phalaenopsis ababilis. How to segment and extract the image of Phalaenopsis amabilis from the background image is the key point of image identification. This paper firstly uses color gradient algorithm to extract the Phalaenopsis amabilis natural images gradient image. It adopts the threshold value to find out the significant part of gradient image (e.g. the significant edge in the image), and the watershed segmentation method to segment the source image. It uses the significant edge image to determine the watershed segmentation image so as to remove the insignificant "dam" and make its both sides converge, to restrain over-segmentation effect. Finally, the target image of Phaleaenopsis amabilis will be obtained through merging the similar areas according to area merging standards. By comparing the segmentation effect with artificial method of the 20 Phalaenopsis ababilis images, it proves that the segmentation algorithm based on gradient and watershed method has good effect to extract the Phalaenopsis amabilis image from its natural background and the segmentation rate is up to 93.6%.