Managing Models Representing Different Expected Distortions Associated with a Plurality of Data Captures

Abstract

The present disclosure relates generally to managing models representing different expected distortions associated with a plurality of data captures. One claim recites a method comprising: obtaining a plurality of models each representing a different expected distortion associated with a plurality of data captures, the plurality of data captures each resulting in distortion of a machine-readable signal; indexing the plurality of models; upon receiving a request, selecting a model associated with the request; and providing the selected model. Of course, other methods and combinations are provided as well.

1 . A method comprising:
 obtaining a plurality of models each representing a different expected distortion associated with a plurality of data captures, the plurality of data captures each resulting in distortion of a machine-readable signal;   indexing the plurality of models;   upon receiving a request, selecting a model associated with the request; and   providing the selected model.   
     
     
         2 . The method of  claim 1  wherein the plurality of models each comprises a template, mask or instruction set indicating how to distort a machine-readable signal or change a machine-readable signal generator to compensate for a particular expected distortion. 
     
     
         3 . The method of  claim 1  wherein the machine-readable signal comprises digital watermarking. 
     
     
         4 . The method of  claim 1  wherein the request includes an identifier associated with a selected model. 
     
     
         5 . The method of  claim 1  in which the plurality of data captures are each associated with optical data capture. 
     
     
         6 . The method of  claim 5  in which the distortion is due to blurring. 
     
     
         7 . The method of  claim 6  in which the blurring is associated with lens focal length. 
     
     
         8 . The method of  claim 5  wherein the blurring—left uncompensated for—would result in a loss of high frequency information of the machine-readable signal upon signal detection. 
     
     
         9 . The method of  claim 2  wherein the template, mask or instruction set indicate how to distort the machine-readable signal by altering a phase of the machine-readable signal relative to the expected distortion. 
     
     
         10 . The method of  claim 1  wherein distortion is modeled as a convolution of the input data with a predetermined function. 
     
     
         11 . The method of  claim 10  wherein the predetermined function is approximated by a Bessel function. 
     
     
         12 . The method of  claim 1  wherein the expected distortion is associated with lens focal length. 
     
     
         13 . A non-transitory computer readable medium comprising instructions to cause a processor to perform the method recited in  claim 1 . 
     
     
         14 . An apparatus comprising:
 memory for storing a plurality of models each representing a different expected distortion associated with a plurality of data captures, the plurality of data captures each resulting in distortion of a machine-readable signal, and in which the plurality of models are indexed for retrieval;   a processor programmed for:
 upon receiving a request, selecting a model associated with the request; and 
 providing the selected model. 
   
     
     
         15 . The apparatus of  claim 14  wherein the plurality of models each comprises a template, mask or instruction set indicating how to distort a machine-readable signal or change a machine-readable signal generator to compensate for a particular expected distortion. 
     
     
         16 . The apparatus of  claim 14  wherein the machine-readable signal comprises digital watermarking. 
     
     
         17 . The apparatus of  claim 14  wherein the request includes an identifier associated with a selected model. 
     
     
         18 . The apparatus of  claim 14  in which the plurality of data captures are each associated with optical data capture. 
     
     
         19 . The apparatus of  claim 18  in which the distortion is due to blurring. 
     
     
         20 . The apparatus of  claim 19  in which the blurring is associated with lens focal length. 
     
     
         21 . The apparatus of  claim 19  wherein the blurring—left uncompensated for—would result in a loss of high frequency information of the machine-readable signal upon signal detection. 
     
     
         22 . The apparatus of  claim 15  wherein the template, mask or instruction set indicate how to distort the machine-readable signal by altering a phase of the machine-readable signal relative to the expected distortion. 
     
     
         23 . The apparatus of  claim 14  wherein distortion is modeled as a convolution of the input data with a predetermined function. 
     
     
         24 . The apparatus of  claim 23  wherein the predetermined function is approximated by a Bessel function.