A positioning method for maize seed laser-cutting slice using linear discriminant analysis based on isometric distance measurement

Abstract

It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput. Each of trivial maize seeds should be recognized and positioned in a certain posture. Correlation area ratio (CAR) is defined as the metric of pixel attribute. A large template of round mask is adopted for seed morphological detection to measure the CAR values. We get the feature points extracted from the seed image through the isometric mapping operation. Iterative processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly separable. Linear discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize seed. The labeling partition operation is applied for picking out the scattered pieces to be finely clustered. Without denoising process, the feature region could be recognized with accuracies by the synthetical methods. Extensive experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.