Differentiating a detected object from a background using a gaussian brightness falloff pattern
A method for detecting a finger is provided. The method includes obtaining a plurality of digital images including a first digital image captured by a camera from a field of view containing a background and a hand including at least one finger, and obtaining an identification of pixels of the plurality of digital images that correspond to at least one finger that is visible in the plurality of digital images rather than to the background, the pixels being identified by: obtaining, from the digital images, a Gaussian brightness falloff pattern indicative of at least one finger, identifying an axis of the at least one finger based on the obtained Gaussian brightness falloff pattern indicative of the at least one finger without identifying edges of the at least one finger, and identifying the pixels that correspond to the at least one finger based on the identified axis.
1. A method comprising:
obtaining a plurality of digital images including a first digital image captured by a camera at a time when at least one light source is illuminating a field of view containing a background and a hand including at least one finger;
obtaining an identification of pixels of the plurality of digital images that correspond to at least one finger of the hand that is visible in the plurality of digital images rather than to the background, the pixels being identified by:
obtaining, from the plurality of digital images, a Gaussian brightness falloff pattern indicative of at least one finger of the hand;
identifying an axis of the at least one finger of the hand based on the obtained Gaussian brightness falloff pattern indicative of the at least one finger without identifying edges of the at least one finger; and
identifying the pixels that correspond to the at least one finger based on the identified axis; and
tracking motion of the identified pixels that correspond to the at least one finger through the plurality of digital images.
2. The method of claim 1 , further comprising:
comparing the tracked motion to a library of gestures;
identifying a particular gesture from the library of gestures that corresponds to the tracked motion; and
enabling the particular gesture that corresponds to the tracked motion.
3. The method of claim 1 , wherein:
the method further includes constructing a model of the at least one finger based on the identified pixels that correspond to the at least one finger and the tracked motion of the identified pixels that correspond to the at least one finger;
the constructing of the model includes constructing a 3D model of the at least one finger, including a position and a shape of the at least one finger, to geometrically determine whether the at least one finger corresponds to an object of interest; and
the at least one light source is positioned such that objects of interest are located within a proximal zone of the field of view, the proximal zone extending from the camera to a distance less than twice an expected maximum distance between the objects of interest and the camera.
4. The method of claim 1 , wherein:
the constructing of the model includes constructing a 3D model of the at least one finger, including a position and a shape of the at least one finger, to geometrically determine whether the at least one finger corresponds to an object of interest; and
the at least one light source is positioned such that objects of interest are located within a proximal zone of the field of view, the proximal zone extending from the camera to a distance that is half or less than a distance to the background.
5. The method of claim 1 , wherein edges of the at least one finger is not visible in at least one digital image of the plurality of digital images.
6. The method of claim 1 , wherein the Gaussian brightness falloff pattern is characterized by a negative second derivative.
7. The method of claim 1 , wherein the Gaussian brightness falloff pattern includes an asymmetric curve.
8. The method of claim 1 , wherein the identifying of the pixels that correspond to the at least one finger includes:
comparing pixels in successive digital images of the plurality of digital images to distinguish pixels corresponding to the at least one finger from pixels corresponding to the background.
9. The method of claim 1 , wherein the identifying of the pixels that correspond to the at least one finger includes:
generating a reduced representation of each digital image of the plurality of digital images in which identified pixels corresponding to the background are zeroed out.
10. An image capture and analysis system comprising:
an image analyzer configured to:
obtain a plurality of digital images including a first digital image captured by a camera at a time when at least one light source is illuminating a field of view containing a background and a hand including at least one finger;
obtain an identification of pixels of the plurality of digital images that correspond to at least one finger of the hand that is visible in the plurality of digital images rather than to the background, the image analyzer identifying the pixels by:
obtaining, from the plurality of digital images, a Gaussian brightness falloff pattern indicative of at least one finger of the hand that is visible;
identifying an axis of the at least one finger of the hand based on the obtained Gaussian brightness falloff pattern indicative of the at least one finger without identifying edges of the at least one finger; and
identifying the pixels that correspond to the at least one finger based on the identified axis of the at least one fingers; and
track motion of the identified pixels that correspond to the at least one finger through the plurality of digital images.
11. The image capture and analysis system of claim 10 , wherein image analyzer is further configured to:
compare the tracked motion to a library of gestures;
identify a particular gesture from the library of gestures that corresponds to the tracked motion; and
enable the particular gesture that corresponds to the tracked motion.
12. The image capture and analysis system of claim 10 , wherein:
the image analyzer is further configured to construct a model of the at least one finger based on the identified pixels that correspond to the at least one finger and the tracked motion of the identified pixels that correspond to the at least one finger;
the image analyzer constructs the model by constructing a 3D model of the at least one finger, including a position and a shape of the at least one finger, to geometrically determine whether the at least one finger correspond to an object of interest; and
the at least one light source is positioned such that objects of interest are located within a proximal zone of the field of view, the proximal zone extending from the camera to a distance less than twice an expected maximum distance between the objects of interest and the camera.
13. The image capture and analysis system of claim 10 , wherein:
the image analyzer constructs the model by constructing a 3D model of the at least one finger, including a position and a shape of the at least one finger, to geometrically determine whether the at least one finger corresponds to an object of interest; and
the at least one light source is positioned such that objects of interest are located within a proximal zone of the field of view, the proximal zone extending from the camera to a distance that is half or less than a distance to the background.
14. The image capture and analysis system of claim 10 , wherein edges of the at least one finger is not visible in at least one digital image of the plurality of digital images.
15. The image capture and analysis system of claim 10 , wherein the Gaussian brightness falloff pattern is characterized by a negative second derivative.
16. The image capture and analysis system of claim 10 , wherein the Gaussian brightness falloff pattern includes an asymmetric curve.
17. The image capture and analysis system of claim 10 , wherein the image analyzer identifies the pixels that correspond to the at least one fingers by:
comparing pixels in successive digital images of the plurality of digital images to distinguish pixels corresponding to the at least one finger from pixels corresponding to the background.
18. The image capture and analysis system of claim 10 , wherein the image analyzer identifies the pixels that correspond to the at least one finger by:
generating a reduced representation of each digital image of the plurality of digital images in which identified pixels corresponding to the background are zeroed out.
19. A wearable goggle, comprising:
a processor configured to:
obtain a plurality of digital images including a first digital image captured by a camera at a time when at least one light source is illuminating a field of view containing a background and a hand including at least one finger;
obtain an identification of pixels of the plurality of digital images that correspond to at least one finger of the hand that is visible in the plurality of digital images rather than to the background, the processor identifying the pixels by:
obtaining, from the plurality of digital images, a Gaussian brightness falloff pattern indicative of at least one finger of the hand that is visible;
identifying an axis of the at least one finger of the hand based on the obtained Gaussian brightness falloff pattern indicative of the at least one finger without identifying edges of the at least one finger; and
identifying the pixels that correspond to the at least one finger based on the identified axis of the at least one fingers; and
track motion of the identified pixels that correspond to the at least one finger through the plurality of digital images.