Method, apparatus, and program for detecting objects in digital image
First Claim
1. A method of objects detection for detecting different objects in an input image, the method comprising the steps of:
- preparing a plurality of weak classifiers each of which selects a vector in an N−
1 (N≧
3) dimensional histogram, the vector corresponding to values of characteristic quantities related to distribution of luminance calculated from each of partial images of a predetermined size cut from the input image, the N−
1 dimensional histogram generated by;
obtaining N histograms of values of the characteristic quantities for predetermined objects of N types through calculation of the characteristic quantities for each of the N types from different sample images representing the predetermined objects;
converting values of frequency in the respective N histograms into vectors by linearly combining the values of frequency with predetermined N basis vectors corresponding to the N histograms one to one, the basis vectors having the same magnitude and isotropy in an N−
1 dimensional space; and
obtaining the N−
1 dimensional histogram by combining the vectors of frequency for the values of the characteristic quantities corresponding to each other between the N histograms;
cutting the partial images of the predetermined size at different positions in the input image; and
carrying out judgment as to which one of the N types of the predetermined objects each of the partial images represents by;
combining the vector or vectors selected by at least one of the weak classifiers by applying the classifier or classifiers on each of the partial images;
extracting components of the basis vectors of the combined vector as scores respectively representing probabilities of the corresponding partial image being the predetermined objects of the N types corresponding to the basis vectors; and
carrying out the judgment based on magnitude of the scores.
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Abstract
In a method of detection of different objects in an input image by application, to partial images cut at different positions in the input image, of a plurality of weak classifiers that evaluate whether a detection target image is an image of a predetermined object based on a histogram of values of characteristic quantities calculated from a plurality of sample images representing the predetermined object, the histogram is extended to multi-dimensions and a criterion for the evaluation by the weak classifiers is a multi-dimensional histogram representing histograms for the different objects in the form of vectors.
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Citations
12 Claims
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1. A method of objects detection for detecting different objects in an input image, the method comprising the steps of:
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preparing a plurality of weak classifiers each of which selects a vector in an N−
1 (N≧
3) dimensional histogram, the vector corresponding to values of characteristic quantities related to distribution of luminance calculated from each of partial images of a predetermined size cut from the input image, the N−
1 dimensional histogram generated by;
obtaining N histograms of values of the characteristic quantities for predetermined objects of N types through calculation of the characteristic quantities for each of the N types from different sample images representing the predetermined objects;
converting values of frequency in the respective N histograms into vectors by linearly combining the values of frequency with predetermined N basis vectors corresponding to the N histograms one to one, the basis vectors having the same magnitude and isotropy in an N−
1 dimensional space; and
obtaining the N−
1 dimensional histogram by combining the vectors of frequency for the values of the characteristic quantities corresponding to each other between the N histograms;
cutting the partial images of the predetermined size at different positions in the input image; and
carrying out judgment as to which one of the N types of the predetermined objects each of the partial images represents by;
combining the vector or vectors selected by at least one of the weak classifiers by applying the classifier or classifiers on each of the partial images;
extracting components of the basis vectors of the combined vector as scores respectively representing probabilities of the corresponding partial image being the predetermined objects of the N types corresponding to the basis vectors; and
carrying out the judgment based on magnitude of the scores. - View Dependent Claims (2, 3, 4)
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5. An apparatus of objects detection for detecting different objects in an input image, and the apparatus comprising:
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a plurality of weak classifiers each of which selects a vector in an N−
1 (N≧
3) dimensional histogram, the vector corresponding to values of characteristic quantities related to distribution of luminance calculated from each of partial images of a predetermined size cut from the input image, the N−
1 dimensional histogram generated by;
obtaining N histograms of values of the characteristic quantities for predetermined objects of N types through calculation of the characteristic quantities for each of the N types from different sample images representing the predetermined objects;
converting values of frequency in the respective N histograms into vectors by linearly combining the values of frequency with predetermined N basis vectors corresponding to the N histograms one to one, the basis vectors having the same magnitude and isotropy in an N−
1 dimensional space; and
obtaining the (N−
1) dimensional histogram by combining the vectors of frequency for the values of the characteristic quantities corresponding to each other between the N histograms;
partial image cutting means for cutting the partial images of the predetermined size at different positions in the input image; and
judgment means for carrying out judgment as to which one of the N types of the predetermined objects each of the partial images represents by;
combining the vector or vectors selected by at least one of the weak classifiers by applying the classifier or classifiers on each of the partial images;
extracting components of the basis vectors of the combined vector as scores respectively representing probabilities of the corresponding partial image being the predetermined objects of the N types corresponding to the basis vectors; and
carrying out the judgment based on magnitude of the scores. - View Dependent Claims (6, 7, 8)
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9. A program for causing a computer to function as means of objects detection for detecting different objects in an input image, the program causing the computer to function as:
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a plurality of weak classifiers each of which selects a vector in an N−
1 (N≧
3) dimensional histogram, the vector corresponding to values of characteristic quantities related to distribution of luminance calculated from each of partial images of a predetermined size cut from the input image, the N−
1 dimensional histogram generated by;
obtaining N histograms of values of the characteristic quantities for predetermined objects of N types through calculation of the characteristic quantities for each of the N types from different sample images representing the predetermined objects;
converting values of frequency in the respective N histograms into vectors by linearly combining the values of frequency with predetermined N basis vectors corresponding to the N histograms one to one, the basis vectors having the same magnitude and isotropy in an N−
1 dimensional space; and
obtaining the (N−
1) dimensional histogram by combining the vectors of frequency for the values of the characteristic quantities corresponding to each other between the N histograms;
partial image cutting means for cutting the partial images of the predetermined size at different positions in the input image; and
judgment means for carrying out judgment as to which one of the N types of the predetermined objects each of the partial images represents by;
combining the vector or vectors selected by at least one of the weak classifiers by applying the classifier or classifiers on each of the partial images;
extracting components of the basis vectors of the combined vector as scores respectively representing probabilities of the corresponding partial image being the predetermined objects of the N types corresponding to the basis vectors; and
carrying out the judgment based on magnitude of the scores. - View Dependent Claims (10, 11, 12)
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Specification