Histogram Layers for Synthetic Aperture Sonar Imagery
Computer Vision and Pattern Recognition
2023-05-04 v1 Artificial Intelligence
Machine Learning
Image and Video Processing
Abstract
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much success in SAS analysis; however, the features extracted by these approaches may not be suitable for capturing certain textural information. To address this problem, we present a novel application of histogram layers on SAS imagery. The addition of histogram layer(s) within the deep learning models improved performance by incorporating statistical texture information on both synthetic and real-world datasets.
Cite
@article{arxiv.2209.03878,
title = {Histogram Layers for Synthetic Aperture Sonar Imagery},
author = {Joshua Peeples and Alina Zare and Jeffrey Dale and James Keller},
journal= {arXiv preprint arXiv:2209.03878},
year = {2023}
}
Comments
7 pages, 9 Figures, Accepted to IEEE International Conference on Machine Learning and Applications (ICMLA) 2022