Clustering Method of Positioning for Maize Seed Laser-cutting Slices Combined with Spatial Constrains

Abstract:

Maize seeds are of different shapes and sizes. It is the bottleneck that the maize seed lasercutting slices are orientated and positioned accurately for the molecular breeding genotype analysis to achieve high throughput with automation. The machine vision system means to recognize the maize feature regions for positioning lasercutting slices accurately in a single seed image. An area correlation filter was presented for describing the pixel with spatial constrain information. The definition of the round mask template was proposed for the seed morphological measurement. The round template size was determined by the area of a single maize seed. Some of pixel coordinate data were extracted to be classified from the target domains by the filtration of the area correlation filter. Through the bisectingmeans clustering with the specific initial clustering centers, the extracted data were divided into thin part class and thick part class. Also, their clustering centers were got, which were corresponded to the thin part class and two arc corner classes, respectively. The labeling partition operation was applied to the connected domains for finely adjusting and marking the centers of tip part and two arc corners. Finally, the coordinates of two interpolated pointpairs near the thick part were calculated. Through linking two pairs of interpolated points, the lasercutting lines were located with high precision. The seed clamping pose was determined according to the tip part center and the centroid of seed. Compared with SUSAN, SUSAN detector cannot be directly applied to locate the feature region of maize seed. The experimental results verified the effectiveness of the proposed method on yellow and white maize seeds.