1. A method for estimating whether an image is blurred or sharp, the method comprising the steps of:
determining an image gradient histogram distribution of at least a portion of the image;
comparing at least a portion of the image gradient histogram distribution to a Gaussian model; and
estimating a defocus blur size of at least a portion of the image.
2. The method of claim 1 wherein the step of determining includes the steps of dividing the image into a plurality of image regions and determining an image gradient histogram distribution of at least one of the image regions, and wherein the step of comparing includes the step of comparing the image gradient histogram distributions for at least one of the image regions to a Gaussian model.
3. The method of claim 2 wherein the step of comparing includes the step of comparing a tail section of the Gaussian model to a tail section of the image gradient histogram distribution.
4. The method of claim 1 wherein the step of determining includes the steps of dividing the image into a plurality of image regions and determining an image gradient histogram distribution for each of the image regions, and wherein the step of comparing includes the step of comparing the image gradient histogram distributions for each of the image regions to its respective Gaussian model to estimate if the image is either blurred or sharp.
5. The method of claim 4 wherein the step of comparing includes the step of comparing a tail section of the Gaussian model to a tail section of the image gradient histogram distribution for each image region.
6. The method of claim 1 further comprising the step of evaluating the image to estimate if there is a possible motion blur direction.
7. The method of claim 6 wherein the step of evaluating the image for possible motion blur includes the step of analyzing the gradient direction histogram to determine if there is a peak gradient direction.
8. The method of claim 6 wherein the step of evaluating the image for possible motion blur includes the steps of computing reliable gradients, computing first and second principal components from the gradients, and determining if a dominant gradient direction exists.
9. The method of claim 1 further comprising the steps of evaluating at least a portion of the image to determine if the contrast is above a predetermined contrast threshold, and adjusting the contrast for at least a portion of the image if the contrast is determined to be below the predetermined contrast threshold.
10. The method of claim 1 wherein the step of determining an image gradient histogram distribution of at least a portion of the image includes the step of excluding pixels having an intensity above a predetermined intensity threshold.
11. The method of claim 1 wherein the step of estimating a defocus blur size includes the steps of removing at least a portion of any content correlation from at least a portion of the input image to generate a decorrelated image of at least a portion of the input image; and computing an autocorrelation matrix from the decorrelated image.
12. The method of claim 11 wherein the step of removing includes the step of applying a decorrelation filter to the input image.
13. The method of claim 11 further comprising the step of rotating the autocorrelation matrix to a plurality of alternative angles to generate a plurality of rotated matrixes.
14. The method of claim 13 further comprising the steps of averaging the plurality of rotated matrixes to calculate an averaged matrix and estimating a defocus blur size from the averaged matrix.
15. The method of claim 1 further comprising the step of deblurring the input image to provide an adjusted image.
16. The method of claim 1 further comprising the step of removing noise from the input image.
17. The method of claim 1 wherein the step of estimating a defocus blur size includes the steps of (i) computing an autocorrelation matrix for at least a portion of the input image, (ii) rotating the autocorrelation matrix to a plurality of alternative angles to generate a plurality of rotated matrixes, (iii) averaging the plurality of rotated matrixes to calculate an averaged matrix, and (iv) estimating the defocus blur size from the averaged matrix.
18. A device for estimating whether an image is either blurred or sharp, the device comprising:
a control system that (i) determines an image gradient histogram distribution of at least a portion of the image; (ii) compares at least a portion of the image gradient histogram distribution to a Gaussian model and (iii) estimating a defocus blur size of at least a portion of the image.
19. The device of claim 18 wherein the control system divides the image into a plurality of image regions and determines an image gradient histogram distribution of at least one of the image regions, and wherein the control system compares the image gradient histogram distributions for at least one of the image regions to a Gaussian model to estimate if the image is either blurred or sharp.
20. The device of claim 19 wherein the control system compares a tail section of the Gaussian model to a tail section of the image gradient histogram distribution.
21. The device claim 18 wherein the control system divides the image into a plurality of image regions and determines an image gradient histogram distribution for each of the image regions, and wherein the control system compares the image gradient histogram distributions for each of the image regions to its respective Gaussian model to estimate if the image is either blurred or sharp.
22. The device of claim 21 wherein the control system compares a tail section of the Gaussian model to a tail section of the image gradient histogram distribution for each image region.
23. The device of claim 18 wherein the control system evaluates the image to estimate if there is a possible motion blur direction, and wherein the control system compares pixels along the possible motion blur direction to determine the image gradient histogram distribution.
24. The device of claim 18 wherein the control system evaluates at least a portion of the image to determine if the contrast is above a predetermined contrast threshold, and wherein the control system adjusts the contrast for at least a portion of the image if the contrast is determined to be below the contrast predetermined threshold.
25. The device of claim 18 wherein the control system (i) removes at least a portion of any content correlation from at least a portion of the input image to generate a decorrelated image; and (ii) computes an autocorrelation matrix from the decorrelated image.
26. The device of claim 25 wherein the control system rotates the autocorrelation matrix to a plurality of alternative angles to generate a plurality of rotated matrixes, and averages the plurality of rotated matrixes to calculate an average autocorrelation matrix, and wherein the control system estimates a defocus blur size from the average autocorrelation matrix.
27. The device of claim 18 wherein the control system deblurs the input image to provide an adjusted image.
28. The device of claim 18 wherein the control system computes an autocorrelation matrix for at least a portion of the input image, the control system rotates the autocorrelation matrix to a plurality of alternative angles to generate a plurality of rotated matrixes, the control system averages the plurality of rotated matrixes to calculate an averaged matrix, and the control system estimates the defocus blur size from the averaged matrix.
29. A method for estimating whether an image is blurred or sharp, the method comprising the steps of:
downsampling the image to create a downsampled image
determining an image gradient histogram distribution of at least a portion of the downsampled image;
comparing at least a portion of the image gradient histogram distribution to a Gaussian model; and
estimating a defocus blur size of at least a portion of the downsampled image.
30. The method of claim 29 wherein the step of determining includes the steps of dividing the downsampled image into a plurality of image regions and determining an image gradient histogram distribution of at least one of the image regions, and wherein the step of comparing includes the step of comparing the image gradient histogram distributions for at least one of the image regions to a Gaussian model.
31. The method of claim 29 further comprising the step of evaluating the downsampled image to estimate if there is a possible motion blur direction.
32. The method of claim 29 further comprising the steps of evaluating at least a portion of the downsampled image to determine if the contrast is above a predetermined contrast threshold, and adjusting the contrast for at least a portion of the image if the contrast is determined to be below the predetermined contrast threshold.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A tracking assembly for locating and tracking a golf ball, said assembly comprising:
a remote processor coupled to said golf ball;
a remote transceiver coupled to said golf ball, said remote transceiver being operationally coupled to said remote processor;
a base housing being carried by a user;
a base processor coupled to said base housing;
a base transceiver being coupled to said base housing, said base transceiver being operationally coupled to said base processor, said base transceiver being in communication with said remote transceiver such that said base transceiver tracks said golf ball;
a GPS coupled to said base housing, said GPS being operationally coupled to said base processor such that said GPS determines a location of said golf ball with respect to said base housing; and
a display coupled to said base housing, said display being operationally coupled to said base processor, said display displaying the location of said golf ball with respect to said base housing such that the user tracks and locates said golf ball.
2. The assembly according to claim 1, wherein said remote processor being centrally positioned within an interior of said golf ball.
3. The assembly according to claim 1, wherein said remote transceiver being centrally positioned within an interior of said golf ball.
4. The assembly according to claim 1, said remote transceiver being electrically coupled to said remote processor.
5. The assembly according to claim 1, wherein said base housing having a front end and a back end, said base housing being elongated between said front and rear ends, said front end of said base housing being convexly curvilinear between each of a first lateral side and a second lateral side said base housing such that said front end of said base housing has a rounded shape.
6. The assembly according to claim 1, wherein said base transceiver being electrically coupled to said base processor.
7. The assembly according to claim 1, wherein said base transceiver being in electromagnetic communication with said remote transceiver.
8. The assembly according to claim 1, wherein said GPS being electrically coupled to said base processor.
9. The assembly according to claim 1, wherein an actuator movably coupled to a top side of said base housing proximate a middle of said base housing.
10. The assembly according to claim 9, wherein said actuator being electrically coupled to said base processer such that said actuator actuates and de-actuates said base processor.
11. The assembly according to claim 1, wherein said display being coupled to a top side of said base housing proximate a front end of said base housing such that said display is visible to the user.
12. The assembly according to claim 1, wherein said display being electrically coupled to said base processor.
13. The assembly according to claim 1, wherein a power supply coupled to said base housing.
14. The assembly according to claim 13, wherein said power supply being electrically coupled to said actuator.
15. The assembly according to claim 14, wherein said power supply comprising at least one battery.
16. A tracking assembly for locating and tracking a golf ball, said assembly comprising:
a remote processor coupled to said golf ball such that said remote processor is centrally positioned within an interior of said golf ball;
a remote transceiver coupled to said golf ball such that said remote transceiver is centrally positioned within said interior of said golf ball, said remote transceiver being electrically coupled to said remote processor;
a base housing having a front end and a back end, said base housing being elongated between said front and rear ends, said front end of said base housing being convexly curvilinear between each of a first lateral side and a second lateral side said base housing such that said front end of said base housing has a rounded shape, said base housing being carried by a user;
a base processor coupled to said base housing;
a base transceiver being coupled to said base housing, said base transceiver being electrically coupled to said base processor, said base transceiver being in electromagnetic communication with said remote transceiver such that said base transceiver tracks said golf ball;
a GPS coupled to said base housing, said GPS being electrically coupled to said base processor such that said GPS determines a location of said golf ball with respect to said base housing;
an actuator movably coupled to a top side of said base housing proximate a middle of said base housing, said actuator being electrically coupled to said base processer such that said actuator actuates and de-actuates said base processor;
a display coupled to said top side of said base housing proximate said front end of said base housing such that said display is visible to the user, said display being electrically coupled to said base processor, said display displaying the location of said golf ball with respect to said base housing such that the user tracks and locates said golf ball; and
a power supply coupled to said base housing, said power supply being electrically coupled to said actuator, said power supply comprising at least one battery.