Systems and methods are described for retexturing portions of surface in a 2-D image, where the surface is an image of a 3-D object. The systems and methods analyze, with user input, the 2-D image and then, in computer memory, generate a 3-D model of the imaged surface. The surface may then be retextured, that is, for example, artwork may be added to the 2-D image in a realistic manner, taking into account a 3-D geometry in the scene of the 2-D image.

An improved method for examining an article by using a vision system is presented. Also presented is a vision system for use within such a method.

Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

A media sequence includes media items arranged in a sequence. A graph is generated to represent animations available for the media items in the media sequence. The graph includes nodes that represent the available animations. The animations to be used in generating the media sequence is selected via selection of a path through the graph, and the media sequence is generated using the selected animations.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

A system and method for tracking differences between a last state seen by a user and a current state is provided. A user views a graphical user interface (GUI) wind that displays one or more states. Should the user's focus shift from the GUI and return after one or more states displayed therein have been modified, the system highlights the change between the current state and the user's last seen state.

A tool allows a user to create new designs for apparel and preview these designs before manufacture. Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. Based on a laser input file with a pattern, a laser will burn the pattern onto apparel. With the tool, the user will be able to create, make changes, and view images of a design, in real time, before burning by a laser. Input to the tool includes fabric template images, laser input files, and damage input. The tool allows adding of tinting and adjusting of intensity and bright point. The user can also move, rotate, scale, and warp the image input.

There is a need to accurately and dynamically evaluate an individual's risk associated with the transmission or contraction of a disease. This need can be addressed, for example, by generation of a real-time or near real-time predicted disease score for an associated user. In one example, a method includes receiving a video stream data object depicting a visual representation of a target user; processing the video stream data object to generate a protective covering indication with respect to the target user; processing the video stream data object to generate a spatial proximity determination score with respect to the target user; processing the protective covering indication and spatial proximity determination score to generate a predicted disease score associated with the target user; and providing an augmented reality video stream data object configured to depict the visual representation of the target user and the predicted disease score.

A computer-implemented method for generating a color of an object displayed on a GUI. The method includes displaying on a graphical user interface a set of icons, each icon of the set being associated with a color, detecting a first user interaction on a first icon of the set, detecting a second user interaction that comprises at least a slide, modifying a value of a parameter of a first color associated with the first icon, the modification of the value being performed with the second user interaction, and computing a first new color that is the first color with the modified value of a parameter.

A device is provided for adjusting brightness of a plurality of images each including a plurality of pixels. The device may include a memory configured to store instructions. The device may also include a processor configured to execute the instructions to determine overall luminance values of the images. The processor may also be configured to determine, from the images, a reference image and a reference overall luminance value based on the overall luminance values. The processor may further be configured to determine adjustment factors for the images based on the reference overall luminance value of the reference image, determine weighting factors for the pixels in an image to be adjusted, and adjust luminance values of the pixels in the image to be adjusted based on an adjustment factor for the image and the weighting factors for the pixels.