Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Disclosed are systems and methods for full body measurements extraction using a mobile device camera. The method includes the steps of receiving one or more user parameters; receiving at least one image containing the human and a background; identifying one or more body features associated with the human; performing body feature annotation on the identified body features for generating an annotation line on each body feature corresponding to a body feature measurement, the body feature annotation utilizing an annotation deep-learning network that has been trained on annotation training data, the annotation training data comprising one or more images for one or more sample body features and an annotation line for each body feature; generating body feature measurements from the one or more annotated body features utilizing a sizing machine-learning module based on the annotated body features and the one or more user parameters; and generating body size measurements by aggregating the body feature measurements for each body feature.

Disclosed are systems and methods for full body measurements extraction using a mobile device camera. The method includes the steps of receiving one or more user parameters; receiving at least one image containing the human and a background; identifying one or more body features associated with the human; performing body feature annotation on the identified body features for generating an annotation line on each body feature corresponding to a body feature measurement, the body feature annotation utilizing an annotation deep-learning network that has been trained on annotation training data, the annotation training data comprising one or more images for one or more sample body features and an annotation line for each body feature; generating body feature measurements from the one or more annotated body features utilizing a sizing machine-learning module based on the annotated body features and the one or more user parameters; and generating body size measurements by aggregating the body feature measurements for each body feature.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images, and/or to fusing data from high-elevation digital images having different spatial, temporal, and/or spectral resolutions. In various implementations, first and second temporal sequences of high-elevation digital images capturing a geographic area may be obtained. These temporal sequences may have different spatial, temporal, and/or spectral resolutions (or frequencies). A mapping may be generated of the pixels of the high-elevation digital images of the second temporal sequence to respective sub-pixels of the first temporal sequence. A point in time at which a synthetic high-elevation digital image of the geographic area may be selected. The synthetic high-elevation digital image may be generated for the point in time based on the mapping and other data described herein.

A method for separating an image can include: acquiring a foreground pixel value and a background pixel value, where the foreground pixel value and the background pixel value are configured to separate a target area from an original image; acquiring a foreground geodesic distance and a background geodesic distance, where the foreground geodesic distance is a distance between a pixel value of each of pixel points and the foreground pixel value in the original image, and the background geodesic distance is a distance between a pixel value of each of the pixel points and the background pixel value; determining a transparency based on the foreground geodesic distance and the background geodesic distance; and separating the target area based on the transparency.

A method for separating an image can include: acquiring a foreground pixel value and a background pixel value, where the foreground pixel value and the background pixel value are configured to separate a target area from an original image; acquiring a foreground geodesic distance and a background geodesic distance, where the foreground geodesic distance is a distance between a pixel value of each of pixel points and the foreground pixel value in the original image, and the background geodesic distance is a distance between a pixel value of each of the pixel points and the background pixel value; determining a transparency based on the foreground geodesic distance and the background geodesic distance; and separating the target area based on the transparency.

A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.