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Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives

Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives

Prasanna Kannappan, Herbert G. Tanner, Arthur C. Trembanis, Justin H. Walker
Copyright: © 2016 |Pages: 19
ISBN13: 9781466694354|ISBN10: 1466694351|EISBN13: 9781466694361
DOI: 10.4018/978-1-4666-9435-4.ch002
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MLA

Kannappan, Prasanna, et al. "Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives." Computer Vision and Pattern Recognition in Environmental Informatics, edited by Jun Zhou, et al., IGI Global, 2016, pp. 22-40. https://doi.org/10.4018/978-1-4666-9435-4.ch002

APA

Kannappan, P., Tanner, H. G., Trembanis, A. C., & Walker, J. H. (2016). Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives. In J. Zhou, X. Bai, & T. Caelli (Eds.), Computer Vision and Pattern Recognition in Environmental Informatics (pp. 22-40). IGI Global. https://doi.org/10.4018/978-1-4666-9435-4.ch002

Chicago

Kannappan, Prasanna, et al. "Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives." In Computer Vision and Pattern Recognition in Environmental Informatics, edited by Jun Zhou, Xiao Bai, and Terry Caelli, 22-40. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9435-4.ch002

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Abstract

A large volume of image data, in the order of thousands to millions of images, can be generated by robotic marine surveys aimed at assessment of organism populations. Manual processing and annotation of individual images in such large datasets is not an attractive option. It would seem that computer vision and machine learning techniques can be used to automate this process, yet to this date, available automated detection and counting tools for scallops do not work well with noisy low-resolution images and are bound to produce very high false positive rates. In this chapter, we hone a recently developed method for automated scallop detection and counting for the purpose of drastically reducing its false positive rate. In the process, we compare the performance of two customized false positive filtering alternatives, histogram of gradients and weighted correlation template matching.

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