@InProceedings{10.1007/978-3-030-14984-0_36,
author="Donkor, Charles
and Sam, Emmanuel
and Basterrech, Sebasti{\'a}n",
editor="Mazal, Jan",
title="Analysis of Tensor-Based Image Segmentation Using Echo State Networks",
booktitle="Modelling and Simulation for Autonomous Systems",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="490--499",
abstract="In pixel classification-based segmentation both the quality of the feature set and the pixel classification technique employed may influence the accuracy of the process. Motivated by the potential of Structural Tensors in the extraction of hidden information about texture between neighboring pixels and the computational capabilities of Echo State Network (ESN), this study proposes a tensor-based segmentation approach using the standard ESN. The pixel features of the image are initially extracted by incorporating Structural Tensors in order to enrich it with information about image texture. Then the resulting feature set is fed into ESN and the output is trained to classify unseen pixels from the testing set. The effect of the two main parameters that impact the accuracy of ESN: reservoir size and spectral radius, was also evaluated. The results are promising when compared to recent state-of-the-art segmentation approaches.",
isbn="978-3-030-14984-0"
}