Vertex interpolation in one-shot learning for object classification

Abstract

A computer implemented method for generating synthetic training data to train a convolutional neural network is described. The method consists of steps including receiving a source image depicting an object for identification. The type and shape of the depicted object is determined. The source image is overlayed with a N×M grid of vertices, the grid including horizontal and vertical edges and being fit to the shape of the depicted object. For each vertex in the grid, perturbations are added to the (x,y) coordinates of the vertex and the pixel values in a range between the original and final (x,y) coordinates are interpolated, resulting in the generation of an item of synthetic training data. The method is repeated to generate multiple items of synthetic training data which are then used to train a neural network to identify the object in an image.