Energy Optimization vs. Deep Learning: Segmentation of Underwater Robot Imagery

N. Shivaswamy


Image segmentation of underwater images is an important and yet challenging field of research technique for machine vision. Due to the underwater environment, the segmentation of these images is more complex than that of common images. Our dataset consists of such deep-sea images of hydrothermal vent, black smokers, captured from multiple viewing angles. This master thesis explores novel Image Segmentation techniques to segment the black smoker images. Initially, we highlight the preparation of the dataset using the estimated depth maps of the images. Later, we discuss the use and modifications of convolutional neural networks which gives us the segmented mask of the images. These masks could be used to generate better and precise 3D model.