Rapid Auto-Focus Using Classifier Chains, Mems And Multiple Object Focusing

摘要

A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.

主权项

1. A method comprising:
acquiring data of a digital image which depicts one or more objects; determining a plurality of in-focus probabilities by applying a plurality of in-focus classifier sets to the data; wherein each in-focus probability indicates a likelihood that any of the one or more objects is depicted in focus in the digital image; determining an in-focus cumulative probability based on the plurality of in-focus probabilities; determining whether the in-focus cumulative probability is below an in-focus threshold; in response to determining that the in-focus cumulative probability is below the in-focus threshold, performing the steps of:
determining a plurality of slightly-out-of-focus probabilities by applying a plurality of slightly-out-of-focus classifier sets to the data;wherein each slightly-out-of-focus probability indicates a likelihood that any of the one or more objects is depicted slightly-out-of focus in the digital image;determining slightly-out-of-focus cumulative probability based on the plurality of slightly-out-of-focus probabilities; andin response to determining that the slightly-out-of-focus cumulative probability is equal to, or above, a slightly-out-of-focus threshold, determining a particular object, of the one or more objects, that is depicted in the digital image slightly-out-of-focus; and wherein the method is performed by one or more computing devices.