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Machine Learning for Detecting Scallops in AUV Benthic Images: Targeting False Positives
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kannappan, Prasanna Tanner, Herbert G. Trembanis, Arthur C. Walker, Justin H. |
| Copyright Year | 2017 |
| 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. |
| Starting Page | 2523 |
| Ending Page | 2541 |
| Page Count | 19 |
| File Format | PDF HTM / HTML |
| DOI | 10.4018/978-1-5225-1759-7.ch103 |
| Alternate Webpage(s) | https://www.igi-global.com/viewtitlesample.aspx?id=173433&ptid=165183&t=machine+learning+for+detecting+scallops+in+auv+benthic+images:+targeting+false+positives |
| Alternate Webpage(s) | http://www.igi-global.com/viewtitlesample.aspx?id=139585&ptid=132467&t=machine+learning+for+detecting+scallops+in+auv+benthic+images:+targeting+false+positives |
| Alternate Webpage(s) | https://doi.org/10.4018/978-1-5225-1759-7.ch103 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
