Systems and methods for automatic scale selection in real-time imaging
View Patent ↗A system and method for automatic scale selection in real-time image and video processing and computer vision applications. In one aspect, a non-parametric variable bandwidth mean shift technique, which is based on adaptive estimation of a normalized density gradient, is used for detecting one or more modes in the underlying data and clustering the underlying data. In another aspect, a data-driven bandwidth (or scale) selection technique is provided for the variable bandwidth mean shift method, which estimates for each data point the covariance matrix that is the most stable across a plurality of scales. The methods can be used for detecting modes and clustering data for various types of data such as image data, video data speech data, handwriting data, etc.
1. A method for performing variable bandwidth selection, comprising the steps of:
receiving one of image data, video data, speech data and handwriting data, and extracting feature data points from the data;
selecting a plurality of analysis scales;
for each analysis scale, partitioning the data into one or more modes and determining a mean/covariance pair for each mode, wherein each data point is associated with a detected mode and a corresponding mean/covariance pair of the mode for each of the analysis scales;
for each data point, determining the most stable mean/covariance pair associated with the data point; and
for each data point, selecting for output the covariance of the most stable meanlcovariance pair as a variable bandwidth for the data point, wherein the variable bandwidths of the data points are used for analyzing features of the data.
2. The method of claim 1 , wherein the step of partitioning the data comprises:
performing a fixed bandwidth mean shift process for each data point using each analysis scale; and
partitioning the data by grouping all mean shift vector trajectories that converge to a similar node.
3. The method of claim 2 , wherein the step of determining a mean/covariance pair of a given mode comprises fitting a normal surface to density of the given mode using mean shift trajectory points associated with the mode.
4. The method of claim 3 , wherein the step of fitting comprises using a least squares solution of a mean shift vector for determining the covariance.
5. The method of claim 4 , wherein the mean is determined as the location of the mode.
6. The method of claim 1 , wherein the step of determining the most stable mean/covariance pair for a given data point comprises the steps of:
defining a distribution for each mean/covariance pair; and
determining a dissimilarity between a given distribution and neighboring distributions across the scales.
7. The method of claim 6 , wherein the dissimilarity is measured using an extension of the Jensen-Shannon divergence.
8. The method of claim 1 , further comprising the step of using the selected variable bandwidths of the data points for detecting modes in the data set.
9. The method of claim 8 , wherein the modes are detected by applying the selected variable bandwidths in a variable bandwidth mean shift process.
10. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for performing variable bandwidth selection, the method steps comprising:
receiving one of image data, video data, speech data and handwriting data, and extracting feature data points from the data;
selecting a plurality of analysis scales;
for each analysis scale, partitioning the data into one or more modes and determining a mean/covariance pair for each mode, wherein each data point is associated with a detected mode and a corresponding meanlcovariance pair of the mode for each of the analysis scales;
for each data point, determining the most stable mean/covariance pair associated with the data point; and
for each data point, selecting for output the covariance of the most stable meanlcovariance pair as a variable bandwidth for the data point, wherein the variable bandwidths of the data points are used for analyzing features of the data.
11. The program storage device of claim 10 , wherein the instructions for partitioning the data comprise instructions for performing the method steps of:
performing a fixed bandwidth mean shift process for each data point using each analysis scale; and
partitioning the data by grouping all mean shift vector trajectories that converge to a similar node.
12. The program storage device of claim 11 , wherein the instructions for determining a mean/covariance pair of a given mode comprise instructions for fitting a normal surface to density of the given mode using mean shift trajectory points associated with the mode.
13. The program storage device of claim 12 , wherein the instructions for fitting comprise instructions for using a least squares solution of a mean shift vector for determining the covariance.
14. The program storage device of claim 13 , wherein the mean is determined as the location of the mode.
15. The program storage device of claim 10 , wherein the instructions for determining the most stable mean/covariance pair for a given data point comprise instructions for performing the method steps of:
defining a distribution for each mean/covariance pair; and
determining a dissimilarity between a given distribution and neighboring distributions across the scales.
16. The program storage device of claim 15 , wherein the dissimilarity is measured using an extension of the Jensen-Shannon divergence.
17. The program storage device of claim 10 , further comprising instructions for using the selected variable bandwidths of the data points for detecting modes in the data set.
18. The program storage device of claim 17 , wherein the modes are detected by applying the selected variable bandwidths in a variable bandwidth mean shift process.