Object recognition using shape growth pattern

This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.