Peter J. Edmunds

@article { 556, title = {Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation}, journal = {PloS one}, year = {2015}, month = {July}, abstract = {Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50\% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys.}, URL = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130312}, author = {Oscar Beijbom and Peter J. Edmunds and Chris Roelfsema and Jennifer Smith and David I. Kline and Benjamin P. Neal and Matthew J Dunlap and Vincent Moriarty and Tung-Yung Fan and Chih-Jui Tan and Stephen Chan and Tali Treibitz and Anthony Gamst and B. Greg Mitchell and David Kriegman} }

@inproceedings { 440, title = {Automated Annotation of Coral Reef Survey Images }, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012}, month = {June}, address = {Providence, Rhode Island}, abstract = {With the proliferation of digital cameras and automatic acquisition systems, scientists can acquire vast numbers of images for quantitative analysis. However, much image analysis is conducted manually, which is both time consum- ing and prone to error. As a result, valuable scientific data from many domains sit dormant in image libraries awaiting annotation. This work addresses one such domain: coral reef coverage estimation. In this setting, the goal, as de- fined by coral reef ecologists, is to determine the percent- age of the reef surface covered by rock, sand, algae, and corals; it is often desirable to resolve these taxa at the genus level or below. This is challenging since the data ex- hibit significant within class variation, the borders between classes are complex, and the viewpoints and image quality vary. We introduce Moorea Labeled Corals, a large multi- year dataset with 400,000 expert annotations, to the com- puter vision community, and argue that this type of ecologi- cal data provides an excellent opportunity for performance benchmarking. We also propose a novel algorithm using texture and color descriptors over multiple scales that out- performs commonly used techniques from the texture clas- sification literature. We show that the proposed algorithm accurately estimates coral coverage across locations and years, thereby taking a significant step towards reliable au- tomated coral reef image annotation. }, author = {Oscar Beijbom and Peter J. Edmunds and David I. Kline and B. Greg Mitchell and David Kriegman} }