Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Abstract: System and method for image detection that include collecting image data; at a processor, over a plurality of support regions of the image data, computing a dimensionality component of a support region of the image data, wherein the, non-nucleus pixels of a support region; calculating a normalizing factor of the dimensionality component; for at least one weighted pattern of a pattern set, applying a weighted pattern to the dimensionality component to create a gradient vector, mapping the gradient vector to a probabilistic model, and normalizing the gradient vector by the normalizing factor; condensing probabilistic models of the plurality of support regions into a probabilistic distribution feature for at least one cell of the image data; applying a classifier to at least the probabilistic distribution feature; and detecting an object in the image data according to a result of the applied classifier.

Abstract: A system and method that includes detecting an application change within a multi-application operating framework; updating an application hierarchy model for gesture-to-action responses with the detected application change; detecting a gesture; according to the hierarchy model, mapping the detected gesture to an action of an application; and triggering the action.

Abstract: Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.

Abstract: Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.