Automatic Segmentation and Detection
Digital imaging can be a useful non-invasive tool in many biological studies. This research focuses on the challenge of manually analyzing large image sets and attempts to satisfy the need for rapid classification and measurement in changing environments using automatic segmentation and detection. This research demonstrates methods for training CNN automatic semantic segmentation algorithms using sparsely labeled data, for using transfer learning for automatic detection in large datasets and using multimodal image input (RGB + fluorescence) for segmentation. The applications of these solutions are focused on the marine environment, mostly for coral segmentation and marine animal detection.