基于分水岭和改进MRF的马铃薯丁粘连图像在线分割

Online Segmentation of Clustering Diced-potatoes Using Watershed and Improved MRF Algorithm

摘要:

针对马铃薯丁粘连图像分割问题，提出一种融合分水岭和改进马尔科夫随机场（MRF）的分割方法。分水岭方法可以将粘连图像分割为若干一致性较好的区域，恰好有利于MRF进行标记，同时，针对实际应用中区域势团势能不一致的情况，通过改进势函数确定MRF的条件概率，使其在全局上具有一致性，从而解决粘连分割问题。用分水岭方法对图像进行初始分割，将图像转化为块状表示。综合考虑初始分割区域的相对高度和面积，用改进的MRF标记正确分割区域和过分割区域。计算过分割区域与邻域的紧密度，选择紧密度最大的邻域并与之合并。试验结果表明，该方法在继承了分水岭方法优点的前提下，解决了过分割的问题，正确率为95%。

关键词:马铃薯丁;粘连;图像分割;马尔可夫随机场;分水岭;过分割

Abstract:

To solve the unsupervised segmentation problem of clustering diced-potatoes, a watershed and improved Markov random field (MRF) algorithm was proposed. The original image was easily transformed from pixel based to region based by watershed algorithm, which was good for labeling by MRF. At the same time, the ISING model was improved to make the consistent of probability of MRF. Firstly the original image was transformed from pixel based to region-based by watershed algorithm. Secondly the improved MRF was applied to distinguish over segmentation regions from right segmentation regions by fusing the relative height and area of the original segmentation regions. Finally the most compactness adjoining over segmentation regions were connected into bigger ones. Using this algorithm, 95% of the test clusters were correctly segmented in potatoes preparations.

Key words:Diced-potato;Clustering;Image segmentation;Markov random field;Watershed;Over-segmentation