Passive Touch System And Method Of Detecting User Input

Abstract

A method of tracking an object of interest preferably includes (i) acquiring a first image and a second image representing different viewpoints of the object of interest; (ii) processing the first image into a first image data set and the second image into a second image data set; (iii) processing the first image data set and the second image data set to generate a background data set associated with a background; (iv) generating a first difference map by determining differences between the first image data set and the background data set and a second difference map by determining differences between the second image data set and the background data set; (v) detecting a first relative position of the object of interest in the first difference map and a second relative position of the object of interest in the second difference map; and (vi) producing an absolute position of the object of interest from the first and second relative positions of the object of interest.

1 . A method of tracking an object of interest, the method comprising:
 acquiring a first image and a second image representing different viewpoints of the object of interest;   processing the first image into a first image data set and the second image into a second image data set;   processing the first image data set and the second image data set to generate a background data set associated with a background;   generating a first difference map by determining differences between the first image data set and the background data set and a second difference map by determining differences between the second image data set and the background data set;   detecting a first relative position of the object of interest in the first difference map and a second relative position of the object of interest in the second difference map; and   producing an absolute position of the object of interest from the first and second relative positions of the object of interest.   
   
   
       2 . The method of  claim 1  wherein processing the first image into the first image data set and the second image into the second image data set includes determining an active image region for each of the first and second images, and extracting an active image data set from the first and second images contained within the active image region. 
   
   
       3 . The method of  claim 2  wherein extracting the active image data set includes cropping the first and second images. 
   
   
       4 . The method of  claim 2  wherein extracting the active image data set includes rotating the first and second images. 
   
   
       5 . The method of  claim 2  wherein extracting the active image data set includes shearing the first and second images. 
   
   
       6 . The method of  claim 2  wherein extracting the active image data set includes arranging the active image data set into an image pixel array having rows and columns. 
   
   
       7 . The method of  claim 6  wherein extracting the active image data set further includes: identifying the maximum pixel value within each column of the image pixel array; and generating data sets having one row whereby the identified maximum pixel value for each column represents that column. 
   
   
       8 . The method of  claim 1  wherein processing the first image into a first image data set and the second image into a second image data set includes filtering the first and second images. 
   
   
       9 . The method of  claim 8  wherein filtering includes extracting the edges in the first and second images. 
   
   
       10 . The method of  claim 8  wherein filtering further includes processing the first image data set and the second image data set to emphasize differences between the first image data set and the background data set and to emphasize differences between the second image data set and the background data set. 
   
   
       11 . The method of  claim 1  wherein processing the first image data set and the second image data set to generate the background data set includes generating a first set of one or more background data sets associated with the first image data set, and generating a second set of one or more background data sets associated with the second image data set. 
   
   
       12 . The method of  claim 11  wherein generating the first set of background data sets includes sampling the first image data set, and generating the second set of background data sets includes sampling the second image data set. 
   
   
       13 . The method of  claim 12  wherein sampling occurs automatically at predefined time intervals, where each sample may include data that is not associated with the background. 
   
   
       14 . The method of  claim 1  wherein generating includes comparing the first image data set to a subset of the background data set, and comparing the second image data set to a subset of the background data set. 
   
   
       15 . The method of  claim 1  wherein generating a first difference map further includes representing each element in the first image data set as one of two states, and generating a second difference map further includes representing each element in the second image data set as one of two states, where the two states represent whether the value is consistent with the background. 
   
   
       16 . The method of  claim 1  wherein detecting includes identifying a cluster in each of the first and second difference maps, each cluster having elements whose state within its associated difference map indicates that the elements are inconsistent with the background. 
   
   
       17 . The method of  claim 16  wherein identifying the cluster further includes reducing the difference map to one row by counting the elements within a column that are inconsistent with the background. 
   
   
       18 . The method of  claim 17  wherein identifying the cluster further includes identifying the column as being within the cluster and classifying nearby columns as being within the cluster. 
   
   
       19 . The method of  claim 18  wherein identifying the column as being within the cluster includes identifying the median column. 
   
   
       20 . The method of  claim 16  wherein identifying the cluster further includes identifying a position associated with the cluster.