Oscar Beijbom

@inproceedings { 569, title = {A New Deep Learning Engine for CoralNet}, booktitle = {ICCV Workshop: Computer Vision in the Ocean}, year = {2021}, URL = {https://openaccess.thecvf.com/content/ICCV2021W/OceanVision/papers/Chen_A_New_Deep_Learning_Engine_for_CoralNet_ICCVW_2021_paper.pdf}, author = {Qimin Chen and Oscar Beijbom and Stephen Chan and Jessica Bouwmeester and David Kriegman} }

@article { 555, title = {Improving Automated Annotation of Benthic Survey Images Using Wide-band Fluorescence}, journal = {Nature}, year = {2016}, month = {March}, abstract = {Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22\% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38\%, 33\%, and 41\%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck.}, URL = {http://www.nature.com/articles/srep23166}, author = {Oscar Beijbom and Tali Treibitz and David I. Kline and Gal Eyal and Adi Khen and Benjamin Neal and Yossi Loya and B. Greg Mitchell and David Kriegman} }

@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 { 547, title = {Menu-Match: Restaurant-Specific Food Logging from Images}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, year = {2015}, URL = {http://research.microsoft.com/en-us/um/redmond/projects/menumatch/}, author = {Oscar Beijbom and Neel Joshi and Dan Morris and Scott Saponas and Siddharth Khullar} }

@article { 550, title = {Wide Field-of-View Fluorescence Imaging of Coral Reefs}, journal = {Scientific Reports}, year = {2015}, URL = {http://www.nature.com/srep/2015/150109/srep07694/full/srep07694.html}, author = {Tali Treibitz and Benjamin P. Neal and David I. Kline and Oscar Beijbom and Paul L. D. Roberts and B. Greg Mitchell and David Kriegman} }

@inproceedings { 498, title = {Guess Averse Loss Functions For Cost-Sensitive Multiclass Boosting}, booktitle = {International Conference on Machine Learning (ICML)}, year = {2014}, address = {Beijing}, author = {Oscar Beijbom and Mohammad Saberian and Nuno Vasconcelos and David Kriegman} }

@inproceedings { 457, title = {Efficient Large-Scale Structured Learning}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, month = {June}, address = {Portland, OR}, author = {Steve Branson and Oscar Beijbom and Serge Belongie} }

@inproceedings { 499, title = {Wide Field-of-View Daytime Fluorescence Imaging of Coral Reefs}, booktitle = {IEEE Oceans}, year = {2013}, URL = {http://www.mtsjournal.org/Papers/PDFs/130502-025.pdf}, author = {Tali Treibitz and Benjamin P. Neal and David I. Kline and Oscar Beijbom and Paul L. D. Roberts and B. Greg Mitchell and David Kriegman} }

@techreport { 448, title = {Domain Adaptation for Computer Vision Applications}, year = {2012}, month = {June, 2012}, institution = {University of California, San Diego}, author = {Oscar Beijbom} }

@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} }

@phdthesis { 410, title = { Single image focus level assessment using Support Vector Machines}, year = {2007}, month = {05/2007}, pages = {57}, school = {Lund University}, type = {Master thesis}, address = {Lund}, author = {Oscar Beijbom} }