# US 2014/0193088 A1 · Rapid Auto-Focus Using Classifier Chains, Mems And Multiple Object Focusing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0193088 A1 - Document ID: US20140193088A1 - Status: Published application - Kind: A1 - Application number: 14/174,821 - Filed: 2014-02-06 - Published: 2014-07-10 - Earliest claimed: 2010-11-11 - Assignees: DigitalOptics Corporation Europe Limited - Applicants: DigitalOptics Corporation Europe Limited - Inventors: ADRIAN CAPATA; STEFAN PETRESCU; PETRONEL BIGIOI; PETER CORCORAN - Web page: https://patentgrep.com/patent/US20140193088A1 ## CPC - G06V 40/166: {using acquisition arrangements} - G06F 18/24: Classification techniques - G06T 5/73: Deblurring; Sharpening - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract 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. ## Claims ### Claim 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; 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; and wherein the method is performed by one or more computing devices. ### Claim 2 (depends on 1) The method of claim 1, wherein an in-focus classifier set of the plurality of in-focus classifier sets comprises an in-focus-program configured to determine an in-focus probability that any of the one or more objects is depicted in-focus in the digital image; wherein a slightly-out-of-focus classifier set of the one or more slightly-out-of-focus classifier sets comprises a slightly-out-of-focus-program configured to determine a slightly-out-of-focus probability that any of the one or more objects is depicted slightly-out-of-focus in the digital image; and wherein the slightly-out-of-focus cumulative probability is computed from one or more slightly-out-of-focus probabilities determined by the one or more slightly-out-of-focus classifier sets. ### Claim 3 (depends on 2) The method of claim 2, further comprising: determining whether the slightly-out-of-focus cumulative probability is below a slightly-out-of-focus threshold; and in response to determining that the slightly-out-of-focus cumulative probability is below, the slightly-out-of-focus threshold, applying one or more significantly-out-of-focus classifier sets to determine a significantly-out-of-focus cumulative probability. ### Claim 4 (depends on 3) The method of claim 3, wherein a significantly-out-of-focus classifier set of the one or more significantly-out-of-focus classifier sets comprises a significantly-out-of-focus-program configured to determine a significantly-out-of-focus probability that any of the one or more objects is depicted significantly-out-of-focus in the digital image; and wherein the significantly-out-of-focus cumulative probability is computed from one or more significantly-out-of-focus probabilities determined by the one or more significantly-out-of-focus classifier sets. ### Claim 5 (depends on 4) The method of claim 4, further comprising: determining whether the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold, determining the particular object, of the one or more objects, that is depicted in the digital image significantly-out-of-focus. ### Claim 6 (depends on 5) The method of claim 5, further comprising: determining whether the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold, determining that none of the one or more objects is depicted in the digital image. ### Claim 7 (depends on 6) The method of claim 6, further comprising: in response to determining that the in-focus cumulative probability is above, or equal to, the in-focus threshold, determining the particular object, of the one or more objects, that is depicted in-focus face in the digital image. ### Claim 8 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. ### Claim 9 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein an in-focus classifier set of the plurality of in-focus classifier sets comprises an in-focus-program configured to determine an in-focus probability that any of the one or more objects is depicted in-focus in the digital image; wherein a slightly-out-of-focus classifier set of the one or more slightly-out-of-focus classifier sets comprises a slightly-out-of-focus-program configured to determine a slightly-out-of-focus probability that any of the one or more objects is depicted slightly-out-of-focus in the digital image; and wherein the slightly-out-of-focus cumulative probability is computed from one or more slightly-out-of-focus probabilities determined by the one or more slightly-out-of-focus classifier sets. ### Claim 10 (depends on 9) The non-transitory computer-readable storage medium of claim 9, storing additional instructions which, when executed, cause the one or more processors to perform: determining whether the slightly-out-of-focus cumulative probability is below a slightly-out-of-focus threshold; and in response to determining that the slightly-out-of-focus cumulative probability is below, the slightly-out-of-focus threshold, applying one or more significantly-out-of-focus classifier sets to determine a significantly-out-of-focus cumulative probability. ### Claim 11 (depends on 10) The non-transitory computer-readable storage medium of claim 10, wherein a significantly-out-of-focus classifier set of the one or more significantly-out-of-focus classifier sets comprises a significantly-out-of-focus-program configured to determine a significantly-out-of-focus probability that any of the one or more objects is depicted significantly-out-of-focus in the digital image; and wherein the significantly-out-of-focus cumulative probability is computed from one or more significantly-out-of-focus probabilities determined by the one or more significantly-out-of-focus classifier sets. ### Claim 12 (depends on 11) The non-transitory computer-readable storage medium of claim 11, storing additional instructions which, when executed, cause the one or more processors to perform: determining whether the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold, determining the particular object, of the one or more objects, that is depicted in the digital image significantly-out-of-focus. ### Claim 13 (depends on 12) The non-transitory computer-readable storage medium of claim 12, storing additional instructions which, when executed, cause the one or more processors to perform: determining whether the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold, determining that none of the one or more objects is depicted in the digital image. ### Claim 14 (depends on 13) The non-transitory computer-readable storage medium of claim 13, storing additional instructions which, when executed, cause the one or more processors to perform: in response to determining that the in-focus cumulative probability is above, or equal to, the in-focus threshold, determining the particular object, of the one or more objects, that is depicted in-focus face in the digital image. ### Claim 15 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. ### Claim 16 (depends on 15) The device of claim 15, wherein an in-focus classifier set of the plurality of in-focus classifier sets comprises an in-focus-program configured to determine an in-focus probability that any of the one or more objects is depicted in-focus in the digital image; wherein a slightly-out-of-focus classifier set of the one or more slightly-out-of-focus classifier sets comprises a slightly-out-of-focus-program configured to determine a slightly-out-of-focus probability that any of the one or more objects is depicted slightly-out-of-focus in the digital image; and wherein the slightly-out-of-focus cumulative probability is computed from one or more slightly-out-of-focus probabilities determined by the one or more slightly-out-of-focus classifier sets. ### Claim 17 (depends on 16) The device of claim 16, wherein the image analysis unit is further configured to: determine whether the slightly-out-of-focus cumulative probability is below a slightly-out-of-focus threshold; and in response to determining that the slightly-out-of-focus cumulative probability is below, the slightly-out-of-focus threshold, apply one or more significantly-out-of-focus classifier sets to determine a significantly-out-of-focus cumulative probability. ### Claim 18 (depends on 17) The device of claim 17, wherein a significantly-out-of-focus classifier set of the one or more significantly-out-of-focus classifier sets comprises a significantly-out-of-focus-program configured to determine a significantly-out-of-focus probability that any of the one or more objects is depicted significantly-out-of-focus in the digital image; and wherein the significantly-out-of-focus cumulative probability is computed from one or snore significantly-out-of-focus probabilities determined by the one or more significantly-out-of-focus classifier sets. ### Claim 19 (depends on 18) The device of claim 18, wherein the image analysis unit is further configured to: determine whether the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is equal to, or above, a significantly-out-of-focus threshold, determine the particular object, of the one or more objects, that is depicted in the digital image significantly-out-of-focus. ### Claim 20 (depends on 19) The device of claim 19, wherein the image analysis unit is further configured to: determine whether the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold; and in response to determining that the significantly-out-of-focus cumulative probability is below the significantly-out-of-focus threshold, determine that none of the one or more objects is depicted in the digital image.