Automated image analysis strategic Initiative Publications

 

  1. 1.Beijbom, O., Edmunds, P. J., Roelfsema, C., Smith, J., Kline, D. I., Neal, B. P., Dunlap, M. J., Moriarty, V., Fan, T.-Y., Tan, C.-J., Chan, S., Treibitz, T., Gamst, A., Mitchell, B. G., & Kriegman, D. (2015). Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation. PloS One, 10(7).

  2. 2.Beijbom, O., Treibitz, T., Kline, D. I., Eyal, G., Khen, A., Neal, B. P., Loya, Y., Mitchell, B. G., & Kriegman, D. (2016). Improving Automated Annotation of Benthic Survey Images Using Wide-Band Fluorescence. Nature Scientific Reports.

  3. 3.Chang, J. H., Hart, D. R., Shank, B. V., Gallager, S., Honig, P., & York, A. D. (2016). Combining Imperfect Automated Annotations of Underwater Images With Human Annotations to Obtain Precise and Unbiased Population Estimates. 17, 169-186.

  4. 4.Chuang, M.-C., Hwang, J.-N., Kua, F.-F., Shan, M.-K., & Williams, K. (2014). Recognizing Live Fish Species by Hierarchical Partial Classification Based on the Exponential Benefit. Proceedings of the 2014 IEEE International Conference on Image Processing.

  5. 5.Chuang, M.-C., Hwang, J.-N., & Williams, K. (2014). Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition. Proceedings of the 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery.

  6. 6.Chuang, M.-C., Hwang, J.-N., & Williams, K. (2016). A Feature Learning and Object Recognition Framework for Underwater Fish Images. IEEE Transactions on Image Processing, 25(4), 1862-1872.

  7. 7.Dawkins, M., Sherrill, L., Fieldhouse, K., Hoogs, A., Richards, B. L., Zhang, D., Prasad, L., Williams, K., Lauffenburger, N., & Wang, G. (2017). An Open-Source Platform for Underwater Image and Video Analytics. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision.

  8. 8.Kresimir, W., Nathan, L., Meng-Che, C., Jenq-Neng, H., & Rick, T. (2016). Automated Measurements of Fish Within a Trawl Using Stereo Images From a Camera-Trawl Device (Camtrawl). Methods in Oceanography, 17, 138 - 152. doi:10.1016/j.mio.2016.09.008

  9. 9.Piacentino, M., & Zhang, D. (2017). Automated Image Analysis and Classification Tool Based on Computer Vision Deep Learning Technologies. Proceedings of the 2017 American Fisheries Society Symposium.

  10. 10.Prasad, L., Singh, H., & Gallager, S. (2016). Edge-Based Cuing for Detection of Benthic Camouflage. Methods in Oceanography, 15-16, 35-48.

  11. 11.Shafait, F., Mian, A., Shortis, M., Ghanem, B., Culverhouse, P. F., Edgington, D., Cline, D., Ravanbakhsh, M., Seager, J., & Harvey, E. S. (2016). Fish Identification From Videos Captured in Uncontrolled Underwater Environments. ICES Journal of Marine Science: Journal du Conseil. doi:10.1093/icesjms/fsw106

  12. 12.Treibitz, T., Neal, B. P., Kline, D. I., Beijbom, O., Roberts, P. L. D., Mitchell, B. G., & Kriegman, D. (2015). Wide Field-of-view Fluorescence Imaging of Coral Reefs. Nature Scientific Reports.

  13. 13.Wang, G., Hwang, J.-N., Williams, K., Wallace, F., & Rose, C. S. (2016). Shrinking Encoding with Two-Level Codebook Learning for Fine-Grained Fish Recognition. Proceedings of the 2016 IEEE International Conference on Pattern Recognition. 31-36.

  14. 14.Zhang, D., Kopanas, G., Desai, C., Chai, S., & Piacentino, M. (2016). Unsupervised Underwater Fish Detection Fusing Flow and Objectiveness. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision.

  15. 15.Zhang, D., & Piacentino, M. (2017). Advances in Automated Stock Assessment Based on Computer Vision Deep Learning Technologies. Proceedings of the 2017 American Fisheries Society Symposium.