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revision 1.86 by jvk, Mon Mar 31 11:46:05 2003 UTC revision 1.87 by tjl, Mon Mar 31 12:29:22 2003 UTC
# Line 45  WHY HW ACCEL NEEDED - PIXELLATION - NO S Line 45  WHY HW ACCEL NEEDED - PIXELLATION - NO S
45    
46  NO SEMANTIC CORRELATIONS!!!!!!!  NO SEMANTIC CORRELATIONS!!!!!!!
47    
 MOTIVATING EXAMPLE NOT YET USER-TESTED  
   
48  IMAGE: RENDERING MODES, TEXTURE COORDINATES!  IMAGE: RENDERING MODES, TEXTURE COORDINATES!
49    
50  SPARSE CODING: A TEXTURE CONTAINING BOTH YELLOW TRIANGLE AND  SPARSE CODING: A TEXTURE CONTAINING BOTH YELLOW TRIANGLE AND
# Line 544  TJL Line 542  TJL
542    
543  %XXX: shorten by one half column  %XXX: shorten by one half column
544    
545  In this section, we discuss the methods to generate unique  In this section, we discuss the principles underlying
546  background textures on an abstract level.  our algorithm to generate unique
547    background textures.
548  To be useful, the unique backgrounds should be easily  To be useful, the unique backgrounds should be easily
549  distinguishable and recognizable, and should not  distinguishable and recognizable, and should not
550  significantly impair the reading of black text on top of it.  significantly impair the reading of black text on top of it.
# Line 560  textures would look alike, being just no Line 558  textures would look alike, being just no
558  In order to design a distinguishable distribution of textures,  In order to design a distinguishable distribution of textures,
559  we have to take into account the properties of the human  we have to take into account the properties of the human
560  visual system.  visual system.
 % The seed for randomly choosing  
 % an easily distinguishable unique background from a  
 % distribution based on  
561    
562  %providing an infinite source of unique backgrounds.  The simple model of texture perception we use assumes
 %generating textures based on seed numbers [identity]  
   
 The simple model we use here assumes  
563  that at some point,  that at some point,
564  the results from the different pre-attentive feature detectors,  the results from the different pre-attentive feature detectors,
565  such as different shapes and colors,  such as different shapes and colors,
# Line 579  input corresponds to by comparing it to Line 571  input corresponds to by comparing it to
571  in a simple perceptron-like  in a simple perceptron-like
572  fashion\cite{rosenblatt62neurodynamics,widrow60adaptive}.  fashion\cite{rosenblatt62neurodynamics,widrow60adaptive}.
573  This configuration is commonly used in neural computation.  This configuration is commonly used in neural computation.
574    As seen for example in \cite{olson02vstm}, only a limited
575    number of different features detected can be grouped into
576    objects, ... XXX SO!!! JVK
577    
578  \begin{figure}  \begin{figure}
579  %\fbox{\vbox{\vskip 3in}}  %\fbox{\vbox{\vskip 3in}}
# Line 665  There should also be as many possible fe Line 660  There should also be as many possible fe
660      or if there were no curved lines, we would be wasting      or if there were no curved lines, we would be wasting
661      recognition potential by leaving some elements      recognition potential by leaving some elements
662      of the feature vector always zero.      of the feature vector always zero.
663        However, the results cited above\cite{olson02vstm}  also
664        indicate that in {\em one} texture, only a limited range
665        of features should be used.
666    
667    
668  Indeed, the entropy is maximized when the features are distributed  Generally, the entropy is maximized when the features are distributed
669  independently from each other:  independently from each other:
670  features orthogonal to human perception  features orthogonal to human perception
671  (e.g.,~color, direction of fastest luminance change)  (e.g.,~color, direction of fastest luminance change)

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