{ "id": 290632, "title": "Papers on Similar topic: Object Detection + Crown of Throns Starfish Detection", "url": "/competitions/tensorflow-great-barrier-reef/discussion/290632", "author_name": "Shivam Bansal", "post_date": "2021-11-25T15:15:56.485000", "votes": 21, "comment_count": 0, "views": 0, "content": "
    \n

  1. Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning
    \nFrom the Paper: We used a modified Mask R-CNN which works by classifying and localizing the region of interest (RoI).

    2. \n

  2. Toward Robust Image Detection of Crown-of-Thorns Starfish for Autonomous\nPopulation Monitoring
    \nFrom the Paper: the complete technique consists of the 6 following steps: 1. Top Hat filtering with disc structuring element 2. Grey scale conversion 3. Local binary pattern created 4. Histograms created 5. Log-likelihood measure performed on image blocks 6. Count number of detected COTS ‘blobs’

    3. \n
", "messages": [ { "id": 1595295, "postDate": "2021-11-25T15:15:56.487Z", "content": "
    \n

  1. Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning
    \nFrom the Paper: We used a modified Mask R-CNN which works by classifying and localizing the region of interest (RoI).

    2. \n

  2. Toward Robust Image Detection of Crown-of-Thorns Starfish for Autonomous\nPopulation Monitoring
    \nFrom the Paper: the complete technique consists of the 6 following steps: 1. Top Hat filtering with disc structuring element 2. Grey scale conversion 3. Local binary pattern created 4. Histograms created 5. Log-likelihood measure performed on image blocks 6. Count number of detected COTS ‘blobs’

    3. \n
", "rawMarkdown": "1. [Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning](https://www.frontiersin.org/articles/10.3389/fmars.2020.00429/full)\nFrom the Paper: We used a modified Mask R-CNN which works by classifying and localizing the region of interest (RoI). \n\n\n2. [Toward Robust Image Detection of Crown-of-Thorns Starfish for Autonomous\nPopulation Monitoring](https://www.researchgate.net/publication/252429707_Toward_Robust_Image_Detection_of_Crown-of-Thorns_Starfish_f_or_Autonomous_Population_Monitoring)\nFrom the Paper: the complete technique consists of the 6 following steps: 1. Top Hat filtering with disc structuring element 2. Grey scale conversion 3. Local binary pattern created 4. Histograms created 5. Log-likelihood measure performed on image blocks 6. Count number of detected COTS ‘blobs’", "votes": 21 } ], "comments": [], "raw_markdown_by_id": { "1595295": "1. [Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning](https://www.frontiersin.org/articles/10.3389/fmars.2020.00429/full)\nFrom the Paper: We used a modified Mask R-CNN which works by classifying and localizing the region of interest (RoI). \n\n\n2. [Toward Robust Image Detection of Crown-of-Thorns Starfish for Autonomous\nPopulation Monitoring](https://www.researchgate.net/publication/252429707_Toward_Robust_Image_Detection_of_Crown-of-Thorns_Starfish_f_or_Autonomous_Population_Monitoring)\nFrom the Paper: the complete technique consists of the 6 following steps: 1. Top Hat filtering with disc structuring element 2. Grey scale conversion 3. Local binary pattern created 4. Histograms created 5. Log-likelihood measure performed on image blocks 6. Count number of detected COTS ‘blobs’" } }