Rapid auto-focus using classifier chains, MEMS and/or multiple object focusing
First Claim
Patent Images
1. An object-based auto-focus method, comprising:
- (a) acquiring a digital image including data corresponding to one or more faces or other specific objects of interest, or both (hereinafter an “
object” and
an “
object data”
) that appears to be out of focus;
(b) applying one or more focus condition classifier programs to the object data, each of the one or more focus condition classifier programs being tuned to a different non-zero degree of out of focus blur, wherein said out of focus classifier programs each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers;
(c) identifying the object data as corresponding to said object within the digital image;
(d) determining an out of focus condition for the object also as a result of the applying of the one or more focus condition classifier programs, wherein said out of focus condition corresponds to a particular degree of out of focus blur at which one of the programs is tuned;
(e) correcting the out of focus condition of the object based on the determining, to thereby generate a corrected object image appearing to be sharply focused; and
(f) electronically capturing, combining with another image, storing, transmitting, applying an object recognition program to, editing, or displaying the corrected object image, or combinations thereof.
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Accused Products
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.
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Citations
45 Claims
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1. An object-based auto-focus method, comprising:
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(a) acquiring a digital image including data corresponding to one or more faces or other specific objects of interest, or both (hereinafter an “
object” and
an “
object data”
) that appears to be out of focus;(b) applying one or more focus condition classifier programs to the object data, each of the one or more focus condition classifier programs being tuned to a different non-zero degree of out of focus blur, wherein said out of focus classifier programs each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (c) identifying the object data as corresponding to said object within the digital image; (d) determining an out of focus condition for the object also as a result of the applying of the one or more focus condition classifier programs, wherein said out of focus condition corresponds to a particular degree of out of focus blur at which one of the programs is tuned; (e) correcting the out of focus condition of the object based on the determining, to thereby generate a corrected object image appearing to be sharply focused; and (f) electronically capturing, combining with another image, storing, transmitting, applying an object recognition program to, editing, or displaying the corrected object image, or combinations thereof. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An object detection method, comprising:
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(a) acquiring a digital image; (b) extracting a sub-window from said image; (c) applying two or more shortened face detection classifier cascades, each trained to be selectively sensitive to a different focus condition of one or more faces or other specific objects of interest, or both (hereinafter “
object”
), wherein each different focus condition corresponds to a different degree of out of focus blur of the object;(d) based on the applying, determining a probability that a specific object with a certain focus condition, corresponding to a certain degree of out of focus blur of the is present within the sub-window; (e) based on the determining, applying an extended object detection classifier cascade trained for sensitivity to said certain focus condition, wherein the certain focus condition is determined by applying one or more out of focus classifier cascades which each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (f) providing a final determination that the specific object exists within the image sub-window; and (g) repeating steps (b)-(e) one or more times for one or more further sub-windows from the image or one or more further focus conditions, or both. - View Dependent Claims (12, 13, 14, 15)
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16. One or more non-transitory processor-readable media having code embedded therein for programming one or more processors to perform an auto-focus method, wherein the method comprises:
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(a) acquiring a digital image including data corresponding to one or more faces or other specific objects of interest, or both (hereinafter an “
object” and
an “
object data”
) that appears to be out of focus;(b) applying one or more focus condition classifier programs to the object data, each of the one or more focus condition classifier programs being tuned to a different non-zero degree of out of focus blur, wherein said out of focus classifier programs each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (c) identifying the object data as corresponding to said object within the digital image; (d) determining an out of focus condition for the object also as a result of the applying of the one or more focus condition classifier programs, wherein said out of focus condition corresponds to a particular degree of out of focus blur at which one of the programs is tuned; (e) correcting the out of focus condition of the object based on the determining, to thereby generate a corrected object image appearing to be sharply focused; and (f) electronically capturing, combining with another image, storing, transmitting, applying an object recognition program to, editing, or displaying the corrected object image, or combinations thereof. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25)
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26. One or more non-transitory processor-readable media having code embedded therein for programming one or more processors to perform an object detection method, wherein the method comprises:
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(a) acquiring a digital image; (b) extracting a sub-window from said image; (c) applying two or more shortened face detection classifier cascades, each trained to be selectively sensitive to a different focus condition of one or more faces or other specific objects of interest, or both (hereinafter “
object”
), wherein each different focus condition corresponds to a different degree of out of focus blur of the object;(d) based on the applying, determining a probability that a specific object with a certain focus condition, corresponding to a certain degree of out of focus blur of the is present within the sub-window; (e) based on the determining, applying an extended object detection classifier cascade trained for sensitivity to said certain focus condition, wherein the certain focus condition is determined by applying one or more out of focus classifier cascades which each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (f) providing a final determination that the specific object exists within the image sub-window; and (g) repeating steps (b)-(e) one or more times for one or more further sub-windows from the image or one or more further focus conditions, or both. - View Dependent Claims (27, 28, 29, 30)
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31. A digital image acquisition device, comprising:
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a lens and image sensor for acquiring digital images; a processor; and one or more non-transitory processor-readable media having code embedded therein for programming one or more processors to perform an auto-focus method, wherein the method comprises; (a) acquiring a digital image including data corresponding to one or more faces or other specific objects of interest, or both (hereinafter an “
object” and
an “
object data”
) that appears to be out of focus;(b) applying one or more focus condition classifier programs to the object data, each of the one or more focus condition classifier programs being tuned to a different non-zero degree of out of focus blur, wherein said out of focus classifier programs each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (c) identifying the object data as corresponding to said object within the digital image; (d) determining an out of focus condition for the object also as a result of the applying of the one or more focus condition classifier programs, wherein said out of focus condition corresponds to a particular degree of out of focus blur at which one of the programs is tuned; (e) correcting the out of focus condition of the object based on the determining, to thereby generate a corrected object image appearing to be sharply focused; and (f) electronically capturing, combining with another image, storing, transmitting, applying an object recognition program to, editing, or displaying the corrected object image, or combinations thereof. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40)
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41. A digital image acquisition device, comprising:
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a lens and image sensor for acquiring digital images; a processor; and one or more non-transitory processor-readable media having code embedded therein for programming one or more processors to perform an object detection method, wherein the method comprises; (a) acquiring a digital image; (b) extracting a sub-window from said image; (c) applying two or more shortened face detection classifier cascades, each trained to be selectively sensitive to a different focus condition of one or more faces or other specific objects of interest, or both (hereinafter “
object”
), wherein each different focus condition corresponds to a different degree of out of focus blur of the object;(d) based on the applying, determining a probability that a specific object with a certain focus condition, corresponding to a certain degree of out of focus blur of the object, is present within the sub-window; (e) based on the determining, applying an extended object detection classifier cascade trained for sensitivity to said certain focus condition, wherein the certain focus condition is determined by applying one or more out of focus classifier cascades which each comprises one or more slightly out of focus classifiers or one or more significantly out of focus classifiers; (f) providing a final determination that the specific object exists within the image sub-window; and (g) repeating steps (b)-(e) one or more times for one or more further sub-windows from the image or one or more further focus conditions, or both. - View Dependent Claims (42, 43, 44, 45)
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Specification