High‐order non‐linear split paths construction algorithm for clustered axisymmetric agriculture products

Abstract

A special high-order touched pattern was found when performing quality detection, counting and classification for clustered axisymmetric agriculture products. A modified strategy of single-circle segmentation algorithm based on symmetrization and multiple-circle fitting was proposed to split this pattern. Firstly, to obtain region of interest from original image, vision saliency detection algorithm was used to extract contour of object image. Then, a circular mask based central point extraction algorithm was applied into the contour, get a set of points. After that, pairwise of optimization concavity points were found and filtered according to concaveness feature and four restrict acceptance conditions. Furthermore, proper linear segmentation lines were formed by connecting the pairwise of optimization concavity points. Low-order non-linear segmentation lines can also be built by finding a new point based on single-circle fitting from our previous studies. To construct the high-order non-linear segmentation lines especially existed in axisymmetric agriculture products, major new points were found and built by using the unique symmetry and geometry properties of the research target. Finally, linear, low-order non-linear and highorder non-linear segmentation lines were integrated in an original image to achieve final segmentation results. Experimental results showed that average segmentation accuracy with 95.46%, 91.95 and 92.63% were respectively achieved across three clustered image datasets-peanut, kidney bean and green soy bean. The segmentation results confirm that this method has capacity to segment axisymmetric agricultural products successfully.