Application of the Nonlinear Wave Metric for Image Segmentation in Neural Networks

The application of neural networks and modern machine learning techniques opened up possible applications for image segmentation, where instead of bounding box detection a pixel level segmentation of the input images can be created. Algorithms designed for image segmentation in applications such as medical imaging, surveillance, gesture control, tracking etc. require the definition of a loss function for the comparison between images. While the brain can compare complex objects with ease, the same is usually a very difficult task for algorithm designers. Comparison between objects requires a properly defined metric that determines the distance, similarity between them. In this paper we will show how the application of a topographic metric can increase the accuracy of traditional segmentation algorithms.