Method for generating user interface that indicates medication changes in medication starts with a processor detecting a medication change event. Processor retrieves medication information based on the medication change event including images of two medications. Processor generates color difference output using a color neural network, image of first medication and second medication. Color difference output comprises information on a difference in hue, saturation or color distribution. Processor generates medication appearance difference output using medication appearance neural network, image of first medication and second medication. Medication appearance difference output comprises information on a difference in shape, segmentation or form. Processor generates a differential record using the color difference output and medication appearance difference output. Processor causes medication change user interface to be displayed that comprises medication images and color and appearance descriptions of the medication which are displayed to emphasize differences identified in the differential record. Other embodiments are disclosed herein.

A method for producing digital ink in a collaboration session between a first computing device and a second computing device that presents a digital canvas. In some embodiments, the method includes capturing a raster image of content using a camera operably coupled to the first computing device, deriving first image vectors and second image vectors based on first and second portions, respectively, of the raster image, sending the first image vectors to the second computing device for displaying a first digital ink object based on the first image vectors, and sending the second image vectors to the second computing device for displaying a second digital ink object based on the second image vectors after the displaying of the second digital ink object.

An approach for generating a coding schema for identifying a spatial location of an event within video image data is provided. In one embodiment, there is a spatial representation tool, including a compression component configured to receive trajectory data of a trajectory of an object for an event within video image data; generate a lossless compressed contour-coded blob to encode the trajectory data of the trajectory of the object for the event within video image data; generate a lossy searchable code of the trajectory of the object for the event within the video image data; convert a region of interest within the video image data to a lossy query code, the region of interest corresponding to a sub-section of a visual display output of the video image data; and compare the lossy query code to the lossy searchable code within a relational database to identify a corresponding lossless trajectory data of the trajectory of the object for the event within the video image data.

A method is provided for determining patterns related to an environment of a host vehicle. Characteristics are detected by a perception system of the host vehicle within the environment of the host vehicle. At least two processing levels having different scales are defined. For each processing level, respective current input data associated with the characteristics for a current point in time is combined with respective memory data related to the characteristics for previous points in time in order to generate joint spatial-temporal data for the respective processing level. An attention algorithm is applied to the joint spatial-temporal data of all processing levels for generating an aggregated data set, and at least one pattern related to the environment is determined from the aggregated data set.

An automated segmentation and identification system/method for identifying geographic atrophy (GA) phenotypic patterns in fundus autofluorescence images. A hybrid process combines a supervised pixel classifier with an active contour algorithm. A trained, machine learning model (e.g., SVM or U-Net) provides initial GA segmentation/classification, and this is followed by Chan-Vese active contour algorithm. The junctional zones of the GA segmented area are then analyzed for geometric regularity and light intensity regularity. A determination of GA phenotype is made, at least in part, from these parameters.

Image recognition and report generation is described. A camera component of a mobile device captures image frames of a physical environment. Each frame is run through an image recognition library to identify object(s) of interest. An object of interest is determined and it is an item associated with periodic maintenance. An identification of the identified object of interest is transmitted to a server. The mobile device receives information about the identified object of interest from the server that is based on a location of the mobile device, the information including a maintenance plan for the identified object of interest.

The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify document sub-images within digital images. Certain of the parameters constrain contour identification and document-subimage-hypothesis generation. The currently described methods and systems identify contours within the digital image, partition the identified contours into four contour sets corresponding to four different regions of the original digital image, construct hypotheses based on these contours for the edges or boundaries of a digital sub-image, and evaluate the hypotheses in order to select a most highly scored hypotheses as a representation of the borders of a digital sub-image within the original received digital image.

A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

A mobile terminal including a touch screen configured to switch between an inactivated state in which illumination is not applied to the touch screen and an activated state in which illumination is applied to the touch screen, and a controller configured to release a locked state of the mobile terminal and switch the touch screen to the activated state, when a plurality of touch inputs is input in the inactivated state and the plurality of touch inputs matches a predefined pattern. Further, the predetermined pattern is initially set using a quadrant image displayed on the touch screen in the activated state, touch inputs for setting the predetermined pattern are applied to at least one of quadrant I, II, III and IV included in the quadrant image displayed on the touch screen in the activated state of the touch screen, a first touch input included in the plurality of touch inputs in the inactivated state is applied to the touch screen, and quadrant areas on the touch screen for receiving the plurality of touch inputs in the inactivated state change based on a first touch position of the first touch input and a second touch position of a second touch input of the plurality of touch inputs in the inactivated state.

A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.