Abstract: A method for quick and easy identification of layer thickness and uniformity of entire large-area graphene samples on arbitrary substrates utilizing fluorescence quenching microscopy in which a polymer mixed with fluorescent dye is applied onto the graphene, then viewing the sample under a fluorescence microscope. A large-scale, high-resolution montage image of the sample is obtained for histogram-based segmentation based on contrast relative to the substrates.

Abstract: Embodiments of the invention are directed to a computer-implemented system and method of identifying human settlements in imagery comprising receiving an image, segmenting the image into a plurality of superpixels, analyzing statistical parameters of at least two or more of the plurality of superpixels, where the statistical parameters includes entropy data, and identifying groups of superpixels having at least a predetermined cluster density and a predetermined entropy. Some embodiments further include clipping the image to only include the identified groups of superpixels having the predetermined cluster density and entropy, analyzing statistical parameters of the clipped image, analyzing geometric factors of the clipped image, determining one or more settlements based on the statistical parameters and geometric factors of the superpixels, and identifying a shape and area of the one or more settlements based on the statistical parameters and geometric factors of the clipped image.

Abstract: Certain embodiments of the invention relate to a computer-implemented method that comprises analyzing an image over a plurality of different scales, where the analyzing includes determining spectral characteristics of the image at each of the plurality of different scales, determining spatial characteristics of the image at each of the plurality of different scales, and determining a segmentation pattern (i.e., superpixel) for the image at each of the plurality of different scales based on the spectral and spatial characteristics of that particular scale. The method further includes identifying objects in the image based on portions of the segmentation patterns that are scale-invariant over the plurality of different scales. In some cases, the method can include determining statistical, textural, and/or intensity characteristics of the image at each scale, where determining the segmentation patterns for the image at each scale is further based on one or more of these characteristics.

Abstract: Embodiments of the invention are directed to a computer-implemented system and method of identifying human settlements in imagery comprising receiving an image, segmenting the image into a plurality of superpixels, analyzing statistical parameters of at least two or more of the plurality of superpixels, where the statistical parameters includes entropy data, and identifying groups of superpixels having at least a predetermined cluster density and a predetermined entropy. Some embodiments further include clipping the image to only include the identified groups of superpixels having the predetermined cluster density and entropy, analyzing statistical parameters of the clipped image, analyzing geometric factors of the clipped image, determining one or more settlements based on the statistical parameters and geometric factors of the superpixels, and identifying a shape and area of the one or more settlements based on the statistical parameters and geometric factors of the clipped image.

Abstract: A method for quick and easy identification of layer thickness and uniformity of entire large-area graphene samples on arbitrary substrates utilizing fluorescence quenching microscopy in which a polymer mixed with fluorescent dye is applied onto the graphene, then viewing the sample under a fluorescence microscope. A large-scale, high-resolution montage image of the sample is obtained for histogram-based segmentation based on contrast relative to the substrates.

Abstract: Certain embodiments of the invention relate to a computer-implemented method that comprises analyzing an image over a plurality of different scales, where the analyzing includes determining spectral characteristics of the image at each of the plurality of different scales, determining spatial characteristics of the image at each of the plurality of different scales, and determining a segmentation pattern (i.e., superpixel) for the image at each of the plurality of different scales based on the spectral and spatial characteristics of that particular scale. The method further includes identifying objects in the image based on portions of the segmentation patterns that are scale-invariant over the plurality of different scales. In some cases, the method can include determining statistical, textural, and/or intensity characteristics of the image at each scale, where determining the segmentation patterns for the image at each scale is further based on one or more of these characteristics.