Detecting objects in an image being acquired by a digital camera or other electronic image acquisition device
View Patent ↗The likelihood of a particular type of object, such as a human face, being present within a digital image, and its location in that image, are determined by comparing the image data within defined windows across the image in sequence with two or more sets of data representing features of the particular type of object. The evaluation of each set of features after the first is preferably performed only on data of those windows that pass the evaluation with respect to the first set of features, thereby quickly narrowing potential target windows that contain at least some portion of the object. Correlation scores are preferably calculated by the use of non-linear interpolation techniques in order to obtain a more refined score. Evaluation of the individual windows also preferably includes maintaining separate feature set data for various positions of the object around one axis and rotating the feature set data with respect to the image data for the individual windows about another axis.
1. A method comprising:
defining a plurality of windows within an image;
assigning scores to individual windows of the plurality of windows by
evaluating data of the image within the individual windows with respect to stored data of a set of features of a particular type of object;
for each unique pair of one window of the plurality of windows and one feature of the set of features, determining a relationship between a result of said evaluating and a plurality of different thresholds, to determine a plurality of relationships for each of the windows; and
assigning a score to each window of the plurality of windows based on the determined relationships for said window; and
determining a likelihood that the particular type of object is present the image based on the assigned scores.
2. The method of claim wherein said assigning scores comprises:
for each individual window, comparing the result of said evaluating against a first threshold and a second threshold;
if the result is above a higher of the first and second thresholds, assigning a first value to the score;
if the result is below a lower of the first and seco thresholds, assigning a second value to the score; and
if the result is between the first and second thresholds, assigning to the score a value interpolated between the first and second values.
3. The method of claim 2 , wherein assigning to the score a value interpolated between the first and second values comprises performing interpolation between the first and second values non-linearly by a transfer function formed of at least two parabolic functions.
4. The method of claim 1 , wherein assigning a score to a current window, of the plurality of windows, comprises comparing a dot product of a vector of the image projected onto the current window with a vector of the current window.
5. The method of claim 4 , wherein:
the dot product is represented by F(ν i , I), where ν i is a vector projected onto the current window I,
the first and second thresholds are represented by θ 0 and θ 1 , respectively, where θ 1 >θ 0 ,
the first and second values are predetermined values represented by α and β, respectively, where β>α, and
the score is interpolated between the first and second predetermined when θ 0 <F(ν i , I)<θ 1 and is represented by (α+(F(ν i , I)−θ 0 (β−α)/(θ 1 −θ 0 )).
6. The method of claim 4 , wherein
the dot product is represented by F(ν i , I), where ν i is a vector projected onto the current window I,
the first and second thresholds are represented by θ 0 and θ 1 respectively, where θ 1 >θ 0 ,
the first and second values are predetermined values represented by α and β, respectively, where β>α, and
the score is interpolated between the first and second predetermined values when θ 0 <F(ν i , I)<θ 1 by a transfer function made of at least two parabolic functions.
7. The method of claim 6 , wherein the transfer function includes at least a 0 [F(ν i , I)] 2 −b 0 F(ν i , I)+c 0 when θ 0 <F(ν i , I)<θ′ and (a 1 [F(ν i , I)] 2 −b 1 F(ν i , I)+c 1 when θ″<F(ν i , I)<θ 1 , wherein a 0 , a 1 , b 0 , b 1 , c 0 and c 1 are constants and θ′ and θ″ fixed threshold values intermediate of θ 1 and θ 2 .
8. The method of claim 7 , wherein θ′ and θ″ are equal.
9. The method of claim 4 , wherein:
the dot product is represented by F(ν i , I) where ν i is a vector projected onto the current window I,
the first and second thresholds are represented by θ 0 and θ 1 , respectively, where θ 1 >θ 0 ,
the first and second value are determined by parabolic transfer functions, and
the score is interpolated between the first and second values when θ 0 <F(ν i , I)<θ 1 by a transfer function made of one or more parabolic functions.
10. The method of claim 1 , wherein said evaluating comprises, for a window, rotating the stored data with respect to data of the image about an axis perpendicular to a plane of the window by changing a parameter of the stored data.
11. An electronic image acquisition device comprising:
an input interface circuit to receive data of an output of an image sensor, the data representing an image; and
processor circuitry coupled to the input interface circuit to receive the data and to provide processed data of the image, wherein the processor circuitry is configured to detect a likelihood that an object of a particular type is present within the image by processing that includes:
defining a plurality of windows within an image;
assigning scores to individual windows of the plurality of windows by
evaluating data of the image within the individual windows with respect to stored data of a set of features of a particular type of object,
for each individual window, determining relationships between a result of said evaluating and a plurality of different thresholds, and
assigning a score to each individual window based on the determined relationships for said window.
12. The electronic acquisition device of device of claim 11 , wherein said assigning scores comprises:
for each individual window, comparing the result said against a first threshold and a second threshold;
if the result is above a higher of the first and second thresholds, assigning first value to the score;
if the result is below a lower of the first and seco thresholds, assigning a second value to the score; and
if the result is between the first and second thresholds, assigning to the score value a value interpolated between the first and second values.
13. The device of claim 11 , further comprising said image sensor.
14. The device of claim 13 , wherein said image sensor is a two-dimensional image sensor.
15. The device of claim 13 , further comprising:
an optical system that projects an image of an object scene outside of the device onto the sensor.
16. The device of claim 11 , wherein assigning to the score a value interpolated between the first and second values comprises performing interpolation between the first and second values non-linearly by a transfer function formed of at least two parabolic functions.
17. The device of claim 11 , wherein the processing of the image processing circuitry to evaluate the likeihood of the presence of the set of features in the individual windows includes comparing a dot product of a vector of the image projected onto a current one of the windows with a vector of the current window.
18. The device of claim 11 , wherein said evaluating comprises, for a window, rotating the stored data with respect to data of the image about an axis perpendicular to a plane of the window by changing a parameter of the stored data.
19. The method of claim 1 , wherein said assigning a score to each individual window comprises:
calculating a weight value for each individual window and each feature of the set of features based on the determined relationships; and
assigning a score to each individual window by cumulating the weigh values for said individual window and each feature of the set of features.