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Preface

Welcome to Computer Vision Across the Marine Sciences
Getting Started
License
Acknowledgements

Chapter 1 - Introduction to Marine Imaging

1. Marine Imaging
2. Imagery Fundamentals
3. Ocean Image Data
4. Artificial Intelligence in Marine Science

Chapter 2 - Introduction to Computer Vision

5. Image Annotation for Computer Vision
6. Image Manipulation in Python with PIL and OpenCV
7. Creating and Augmenting Datasets
8. Understanding CV Metrics and Graphs

Chapter 3 - Computer Vision Development

9. Image Classification with Keras
10. Training YOLO Models: A Guide to Understanding Tasks and Modes
11. Training and Deploying Object Detection with YOLO
12. Training and Deploying Many Class Classification with YOLO
13. Introduction to FathomNet
14. Using Fathomnet Models on a ROV Transect with YOLO
15. Pulling Data from Fathomnet
16. Object and Multi-Object Tracking with Fathomnet and YOLO
17. Object in Zone Detection with Fathomnet and YOLO
18. Keypoint Detection With YOLO
19. Instance Segmentation with YOLO
20. K-means Segmentation
21. Image Super-Resolution and Enhancement with SRGAN in TensorFlow

Chapter 4 - Synthesis Project

22. Dataset Preparation and Selection
23. Model Selection and Development
24. Training and Evaluation
25. Data Visualization and Figure Creation
26. Usecase Development
27. Tying it all together

Appendices

Bibliography
Survey Results
The Math Behind Convolutional Neural Networks (CNNs)

Repository
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Introduction to FathomNet

Contents

13.1. Overview
- 13.1.1. Learning Objectives

13. Introduction to FathomNet#

13.1. Overview#

13.1.1. Learning Objectives#

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Contents

13.1. Overview
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By Atticus Carter

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