Object detection apparatus, learning apparatus, object detection system, object detection method and object detection program
First Claim
Patent Images
1. An object detection apparatus comprising:
- a storing unit configured to store learned information learned previously with respect to a sample image extracted from an input image and including first information and second information, the first information indicating at least one combination of a given number of feature-region/feature-value groups selected from a plurality of feature-region/feature-value groups each including one of feature areas and one of quantized learned-feature quantities, the feature areas each having a plurality of pixel areas, and the quantized learned-feature quantities obtained by quantizing learned-feature quantities corresponding to feature quantities of the feature areas in the sample image, and the second information indicating whether the sample image is an object or a non-object;
a feature-value computation unit configured to compute an input feature value of each of the feature areas belonging to the combination in the input image;
a quantization unit configured to quantize the computed input feature value to obtain quantized input feature value; and
a determination unit configured to determine whether the input image includes the object, using the quantized input feature value and the learned information.
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Abstract
Object detection apparatus includes storing unit storing learned information learned previously with respect to sample image extracted from an input image and including first information and second information, first information indicating at least one combination of given number of feature-region/feature-value groups selected from plurality of feature-region/feature-value groups each including one of feature areas and one of quantized learned-feature quantities, feature areas each having plurality of pixel areas, and quantized learned-feature quantities obtained by quantizing learned-feature quantities corresponding to feature quantities of feature areas in sample image, and second information indicating whether sample image is an object or non-object, feature-value computation unit computing an input feature value of each of feature areas belonging to combination in input image, quantization unit quantizing computed input feature value to obtain quantized input feature value, and determination unit determining whether input image includes object, using quantized input feature value and learned information.
30 Citations
23 Claims
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1. An object detection apparatus comprising:
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a storing unit configured to store learned information learned previously with respect to a sample image extracted from an input image and including first information and second information, the first information indicating at least one combination of a given number of feature-region/feature-value groups selected from a plurality of feature-region/feature-value groups each including one of feature areas and one of quantized learned-feature quantities, the feature areas each having a plurality of pixel areas, and the quantized learned-feature quantities obtained by quantizing learned-feature quantities corresponding to feature quantities of the feature areas in the sample image, and the second information indicating whether the sample image is an object or a non-object;
a feature-value computation unit configured to compute an input feature value of each of the feature areas belonging to the combination in the input image;
a quantization unit configured to quantize the computed input feature value to obtain quantized input feature value; and
a determination unit configured to determine whether the input image includes the object, using the quantized input feature value and the learned information. - View Dependent Claims (2, 3, 4, 5)
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6. A learning apparatus comprising:
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a first storing unit configured to store at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
a feature generation unit configured to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
a feature computation unit configured to compute, for each of the sample images, a feature value of each of the feature areas;
a probability computation unit configured to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
a combination generation unit configured to generate a plurality of combinations of the feature areas;
a joint probability computation unit configured to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the computed joint probabilities, and information indicating whether each of the sample images is the object or the non-object;
a determination unit configured to determine, concerning each of the combinations with reference to the tables, whether a ratio of a joint probability indicating the object sample image to a joint probability indicating the non-object sample image is higher than a threshold value, to determine whether each of the sample images is the object;
a selector configured to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images; and
a second storing unit which stores the selected combination and one of the tables corresponding to the selected combination. - View Dependent Claims (7, 8, 9)
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10. A learning apparatus comprising:
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a first storing unit which stores at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
an imparting unit configured to impart an initial weight to the stored sample images;
a feature generation unit configured to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
a feature computation unit configured to compute, for each of the sample images, a weighted sum of differently weighted pixel areas included in each of the feature areas, or an absolute value of the weighted sum, the weighted sum or the absolute value being used as a feature value corresponding to each of the feature areas;
a probability computation unit configured to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
a combination generation unit configured to generate a plurality of combinations of the feature areas;
a joint probability computation unit configured to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the quantized feature quantities, a plurality of values acquired by multiplying the computed joint probabilities by the initial weight, and information indicating whether each of the sample images is the object or the non-object;
a determination unit configured to determine, concerning each of the combinations with reference to the tables, whether a ratio of a value acquired by multiplying a joint probability indicating the object sample image by the initial weight to a value acquired by multiplying a joint probability indicating the non-object sample image by the initial weight is higher than a threshold value, to determine whether each of the sample images is the object;
a selector configured to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images;
a second storing unit which stores the selected combination and one of the tables corresponding to the selected combination; and
an update unit configured to update a weight of any one of the sample images to increase the weight when the sample images are subjected to a determination based on the selected combination, and a determination result concerning the any one of the sample images indicating an error, wherein;
the joint probability computation unit generates tables storing the generated combinations, a plurality of values acquired by multiplying the computed joint probabilities by the updated weight, and information indicating whether each of the sample images is the object or the non-object;
the determination unit performs a determination based on the values acquired by multiplying the computed joint probabilities by the updated weight;
the selector selects, from a plurality of combinations determined based on the updated weight, a combination which minimizes number of errors in determination results corresponding to the sample images; and
the second storing unit newly stores the combination selected by the selector, and one of the tables corresponding to the combination selected by the selector. - View Dependent Claims (11, 12, 13)
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14. An object detection system comprising a learning apparatus and an object detection apparatus,
the learning apparatus including: -
a first storing unit configured to store at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
a feature generation unit configured to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
a feature computation unit configured to compute, for each of the sample images, a feature value of each of the feature areas;
a probability computation unit configured to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
a combination generation unit configured to generate a plurality of combinations of the feature areas;
a joint probability computation unit configured to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the computed joint probabilities, and information indicating whether each of the sample images is the object or the non-object;
a first determination unit configured to determine, concerning each of the combinations with reference to the tables, whether a ratio of a joint probability indicating the object sample image to a joint probability indicating the non-object sample image is higher than a threshold value, to determine whether each of the sample images is the object;
a selector configured to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images; and
a second storing unit which stores the selected combination and one of the tables corresponding to the selected combination, and the object detection apparatus including;
a feature-value computation unit configured to compute an input feature value of each of the feature areas belonging to the combination in an input image;
a quantization unit configured to quantize the computed input feature value to obtain quantized input feature value; and
a second determination unit configured to determine whether the input image includes the object, using the quantized input feature value and the one of the tables stored in the second storing unit.
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15. An object detection system comprising a learning apparatus and an object detection apparatus,
the learning apparatus including: -
a first storing unit which stores at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
an imparting unit configured to impart an initial weight to the stored sample images;
a feature generation unit configured to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
a first computation unit configured to compute, for each of the sample images, a weighted sum of differently weighted pixel areas included in each of the feature areas, or an absolute value of the weighted sum, the weighted sum or the absolute value being used as a feature value corresponding to each of the feature areas;
a probability computation unit configured to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
a combination generation unit configured to generate a plurality of combinations of the feature areas;
a joint probability computation unit configured to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the quantized feature quantities, a plurality of values acquired by multiplying the computed joint probabilities by the initial weight, and information indicating whether each of the sample images is the object or the non-object;
a first determination unit configured to determine, concerning each of the combinations with reference to the tables, whether a ratio of a value acquired by multiplying a joint probability indicating the object sample image by the initial weight to a value acquired by multiplying a joint probability indicating the non-object sample image by the initial weight is higher than a threshold value, to determine whether each of the sample images is the object;
a selector configured to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images;
a second storing unit which stores the selected combination and one of the tables corresponding to the selected combination; and
an update unit configured to update a weight of any one of the sample images to increase the weight when the sample images are subjected to a determination based on the selected combination, and a determination result concerning the any one of the sample images indicates an error, wherein;
the joint probability computation unit generates tables storing the generated combinations, a plurality of values acquired by multiplying the computed joint probabilities by the updated weight, and information indicating whether each of the sample images is the object or the non-object;
the first determination unit performs a determination based on the values acquired by multiplying the computed joint probabilities by the updated weight;
the selector selects, from a plurality of combinations determined based on the updated weight, a combination which minimizes number of errors in determination results corresponding to the sample images; and
the second storing unit newly stores the combination selected by the selector, and one of the tables corresponding to the combination selected by the selector, the object detection apparatus including;
a second computation unit configured to compute an input feature value of each of the feature areas belonging to the combination in an input image;
a quantization unit configured to quantize the computed input feature value into one of the discrete values in accordance with the input feature value to obtain quantized input feature value;
a second determination unit configured to determine whether the input image includes the object, referring to the selected combination and the one of the tables; and
a total determination unit configured to determine whether the input image includes the object, using a weighted sum acquired by imparting weights to a plurality of determination results acquired by the second determination unit concerning the plurality of combinations.
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16. An object detection method comprising:
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storing learned information learned previously with respect to a sample image extracted from an input image and including first information and second information, the first information indicating at least one combination of a given number of feature-region/feature-value groups selected from a plurality of feature-region/feature-value groups each including one of feature areas and one of quantized learned-feature quantities, the feature areas each having a plurality of pixel areas, and the quantized learned-feature quantities obtained by quantizing learned-feature quantities corresponding to feature quantities of the feature areas in the sample image, and the second information indicating whether the sample images is an object or a non-object;
computing an input feature value of each of the feature areas belonging to the combination in the input image;
quantizing the computed input feature value to obtain quantized input feature value; and
determining whether the input image includes the object, using the quantized input feature value and the learned information. - View Dependent Claims (17)
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18. A learning method comprising:
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storing at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
generating a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in-each of the sample images;
computing, for each of the sample images, a feature value of each of the feature areas;
computing a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then quantizing the feature value into one of a plurality of discrete values based on the computed probability;
generating a plurality of combinations of the feature areas;
computing, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generating tables storing the generated combinations, the computed joint probabilities, and information indicating whether each of the sample images is the object or the non-object;
determining, concerning each of the combinations with reference to the tables, whether a ratio of a joint probability indicating the object sample image to a joint probability indicating the non-object sample image is higher than a threshold value, to determine whether each of the sample images is the object;
selecting, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images; and
storing the selected combination and one of the tables corresponding to the selected combination.
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19. A learning method comprising:
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storing at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
imparting an initial weight to the stored sample images;
generating a plurality of feature areas, each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
computing, for each of the sample images, a weighted sum of differently weighted pixel areas included in each of the feature areas, or an absolute value of the weighted sum, the weighted sum or the absolute value being used as a feature value corresponding to each of the feature areas;
computing a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then quantizing the feature value into one of a plurality of discrete values based on the computed probability;
generating a plurality of combinations of the feature areas;
computing, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generating tables storing the generated combinations, the quantized feature quantities, a plurality of values acquired by multiplying the computed joint probabilities by the initial weight, and information indicating whether each of the sample images is the object or the non-object;
determining, concerning each of the combinations with reference to the tables, whether a ratio of a value acquired by multiplying a joint probability indicating the object sample image by the initial weight to a value acquired by multiplying a joint probability indicating the non-object sample image by the initial weight is higher than a threshold value, to determine whether each of the sample images is the object;
selecting, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images;
storing the selected combination and one of the tables corresponding to the selected combination;
updating a weight of any one of the sample images to increase the weight when the sample images are subjected to a determination based on the selected combination, and a determination result concerning the any one of the sample images indicating an error;
generating tables storing the generated combinations, a plurality of values acquired by multiplying the computed joint probabilities by the updated weight, and information indicating whether each of the sample images is the object or the non-object;
performing a determination based on the values acquired by multiplying the computed joint probabilities by the updated weight;
selecting, from a plurality of combinations determined based on the updated weight, a combination which minimizes number of errors in determination results corresponding to the sample images; and
newly storing the selected combination and one of the tables corresponding to the selected combination.
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20. An object detection program stored in a computer-readable medium using a computer, the program comprising:
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means for instructing the computer to store learned information learned previously with respect to a sample image extracted from an input image and including first information and second information, the first information indicating at least one combination of a given number of feature-region/feature-value groups selected from a plurality of feature-region/feature-value groups each including one of feature areas and one of quantized learned-feature quantities, the feature areas each having a plurality of pixel areas, and the quantized learned-feature quantities obtained by quantizing learned-feature quantities corresponding to feature quantities of the feature areas in the sample image, and the second information indicating whether the sample images is an object or a non-object;
computation means for instructing the computer to compute an input feature value of each of the feature areas belonging to the combination in the input image;
means for instructing the computer to quantize the computed input feature value to obtain quantized input feature value; and
determination means for instructing the computer to determine whether the input image includes the object, using the quantized input feature value and the learned information stored. - View Dependent Claims (21)
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22. A learning program stored in a computer-readable medium, the program comprising:
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means for instructing a computer to store at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
means for instructing the computer to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
means for instructing the computer to compute, for each of the sample images, a feature value of each of the feature areas;
means for instructing the computer to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
means for instructing the computer to generate a plurality of combinations of the feature areas;
means for instructing the computer to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the computed joint probabilities, and information indicating whether each of the sample images is the object or the non-object;
means for instructing the computer to determine, concerning each of the combinations with reference to the tables, whether a ratio of a joint probability indicating the object sample image to a joint probability indicating the non-object sample image is higher than a threshold value, to determine whether each of the sample images is the object;
means for instructing the computer to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images; and
means for instructing the computer to store the selected combination and one of the tables corresponding to the selected combination.
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23. A learning program stored in a computer-readable medium, the program comprising:
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means for instructing a computer to store at least two sample images, one of the sample images being an object as a detection target and the other sample image being a non-object as a non-detection target;
means for instructing the computer to impart an initial weight to the stored sample images;
means for instructing the computer to generate a plurality of feature areas each of which includes a plurality of pixel areas, the feature areas being not more than a maximum number of feature areas which are arranged in each of the sample images;
means for instructing the computer to compute, for each of the sample images, a weighted sum of differently weighted pixel areas included in each of the feature areas, or an absolute value of the weighted sum, the weighted sum or the absolute value being used as a feature value corresponding to each of the feature areas;
means for instructing the computer to compute a probability of occurrence of the feature value corresponding to each of the feature areas, depending upon whether each of the sample images is the object, and then to quantize the feature value into one of a plurality of discrete values based on the computed probability;
means for instructing the computer to generate a plurality of combinations of the feature areas;
acquisition means for instructing the computer to compute, in accordance with each of the combinations, a joint probability with which the quantized feature quantities are simultaneously observed in each of the sample images, and generate tables storing the generated combinations, the quantized feature quantities, a plurality of values acquired by multiplying the computed joint probabilities by the initial weight, and information indicating whether each of the sample images is the object or the non-object;
determination means for instructing the computer to determine, concerning each of the combinations with reference to the tables, whether a ratio of a value obtained by multiplying a joint probability indicating the object sample image by the initial weight to a value obtained by multiplying a joint probability indicating the non-object sample image by the initial weight is higher than a threshold value, to determine whether each of the sample images is the object;
selection means for instructing the computer to select, from the combinations, a combination which minimizes number of errors in determination results corresponding to the sample images;
storing means for instructing the computer to store the selected combination and one of the tables corresponding to the selected combination; and
means for instructing the computer to update a weight of any one of the sample images to increase the weight when the sample images are subjected to a determination based on the selected combination, and a determination result concerning the any one of the sample images indicating an error, wherein;
the acquisition means instructs the computer to generate tables storing the generated combinations, a plurality of values obtained by multiplying the computed joint probabilities by the updated weight, and information indicating whether each of the sample images is the object or the non-object;
the determination means instructs the computer to perform a determination based on the values obtained by multiplying the computed joint probabilities by the updated weight;
the selection means instructs the computer to select, from a plurality of combinations determined based on the updated weight, a combination which minimizes number of errors in determination results corresponding to the sample images; and
the storing means instructs the computer to newly store the selected combination, and one of the tables corresponding to the selected combination.
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Specification
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Current AssigneeKabushiki Kaisha Toshiba (Toshiba Corporation)
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Original AssigneeKabushiki Kaisha Toshiba (Toshiba Corporation)
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InventorsKaneko, Toshimitsu, Mita, Takeshi, Hori, Osamu
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Application NumberUS11/236,619Publication NumberTime in Patent OfficeDaysField of SearchUS Class Current382/190CPC Class CodesG06V 40/165 using facial parts and geom...
