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

A non-transitory computer-readable storage medium, storing one or more computer instructions which, when executed by one or more processors, cause the one or more processors to perform:

• 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;
• and
• in 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.

A device, comprising:

• an image retrieval unit coupled to one or more memory units and configured to acquire data of a digital image which depicts one or more objects;
• and
• an image analysis unit configured to:
• determine 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;
• determine an in-focus cumulative probability based on the plurality of in-focus probabilities;
• determine 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:
• determine 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;
• determine slightly-out-of-focus cumulative probability based on the plurality of slightly-out-of-focus probabilities;
• and
• in response to determining that the slightly-out-of-focus cumulative probability is equal to, or above, a slightly-out-of-focus threshold, determine a particular object, of the one or more objects, that is depicted in the digital image slightly-out-of-focus.