Abstract: Various embodiments describe systems and methods for utilizing a reduced amount of processing capacity for incoming data over time, and, in response to detecting a scene-change-event, notify one or more data processors that a scene-change-event has occurred, and cause incoming data to be processed as new data. In some embodiments, an incoming frame can be compared with a reference frame to determine a difference between the reference frame and the incoming frame. The reference frame may be correlated to a latest scene-change-event. In response to a determination that the difference does not meet one or more difference criteria, a user interface or at least one processor of the computing device can be notified to reduce processing of incoming data over time. In response to a determination that the difference meets the one or more difference criteria, the user interface or the at least one processor can be notified that a scene-change-event has occurred.

Abstract: Approaches enable the projection of one or more visual elements, such as one or more dynamically changing graphical elements, that can substantially bound, or otherwise at least partially surround or identify, an object recognized by a computing device. The computing device can project the graphical elements to collectively appear as a bounding element for the recognized/actionable object or object portion. As such, the graphical elements can appear as a bounding element that adorns, decorates, highlights, and/or emphasizes, etc., the recognized/actionable object or object portion. The graphical elements to be dynamic. For example, the graphical elements can be projected to move around individually over time, while still appearing to at least partially surround the recognized/actionable object or object portion. Further, the graphical elements can be used to improve various object recognition approaches.

Abstract: Embodiments of the subject technology provide methods and systems of image pre-processing for improving the accuracy of optical character recognition (OCR) and reducing the power consumption on a given computing device (e.g., mobile computing device). The subject technology, in some examples, classifies an image received from a camera of a mobile computing device into one or more classes: 1) normal background, 2) textured background, 3) image with text, 4) image with barcode, 5) image with QR code, and/or 6) image with clutter or “garbage.” Based on the classes associated with the image, the subject technology may forgo certain image processing operations, when the image is not associated with a particular class, in order to save resources (e.g., CPU cycles, battery power, memory usage, etc.) on the mobile computing device.