{
    "identifier": "ark:\/88434\/mds2-2155",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Peter Bajcsy"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2155",
    "title": "2D Segmentation of Concrete Samples for Training AI Models",
    "description": "This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "accessURL": "https:\/\/isg.nist.gov\/deepzoomweb\/data\/concreteScoring",
            "format": "TIFF file format",
            "description": "his web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes.",
            "title": "2D Segmentation of Concrete Samples for Training AI Models"
        },
        {
            "accessURL": "https:\/\/github.com\/usnistgov\/WIPP-unet-train-plugin",
            "title": "UNet CNN Semantic-Segmentation Training plugin"
        },
        {
            "accessURL": "https:\/\/github.com\/usnistgov\/WIPP-unet-inference-plugin",
            "title": "UNet CNN Semantic-Segmentation Inference plugin"
        },
        {
            "accessURL": "https:\/\/doi.org\/10.18434\/M32155",
            "title": "DOI Access for 2D Segmentation of Concrete Samples for Training AI Models"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2019-11-18 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Computational science"
    ],
    "keyword": [
        "CS-MET computational metrology"
    ]
}