Abstract: Computing machine(s) access an input image of footwear, the input image comprising a pixel or voxel based image. The computing machine(s) generate, based on the input image and using machine learning engines, a 3D (three-dimensional) CAD (computer-aided design) digital asset comprising a NURBS (non-uniform rational basis spline) model of the footwear. Generating the 3D CAD digital asset comprises: identifying, using a trained classification engine, a visual component of the footwear and iteratively fitting, for the visual component of the footwear, a spline using a trained reinforcement learning engine until the spline is within a predefined average distance from the visual component. The 3D CAD digital asset comprises the spline. The machine learning engines comprise the trained classification engine and the trained reinforcement learning engine. The computing machine(s) provide an output representing the 3D CAD digital asset.

Abstract: A method for correcting the date/time values associated with digital images captured by a digital camera includes using a digital camera to capture and store a plurality of digital images and to store an associated initial date/time value for each of the plurality of digital images provided by a real-time clock in the digital camera; and establishing communications between the digital camera and a separate device providing a current date/time value. The method further includes determining a current date/time value in the digital camera, and a difference between the current date/time value in the digital camera and the current date/time value in the separate device; and modifying the initial date/time values associated with each of the plurality of digital images to compensate for the difference between the current date/time value in the digital camera and the current date/time value in the separate device in order to correct the date/time values associated with each of the plurality of digital images.