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An Active Vision Architecture based on Iconic Representations
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@TechReport{Rao:TR95,
  author = 	 {Rajesh P.N. Rao and Dana H. Ballard},
  title = 	 {An Active Vision Architecture based on Iconic Representations},
  institution =  {Comp Sci Dept University of Rochester},
  year = 	 {1995},
  number = 	 {548},
  address = 	 {Rochester NY},
  month = 	 {March},
  abstract =   {
Active vision systems have the capability of continuously interacting with the 
environment. The rapidly changing environment of such systems means that it is 
attractive to replace static representations with visual routines that compute 
information on demand. Such routines place a premium on image data structures that 
are easily computed and used.

The purpose of this paper is to propose a general active vision architecture based 
on efficiently computable iconic representations. This architecture employs two 
primary visual routines, one for identifying the visual image near the fovea (object
 identification), and another for locating a stored prototype on the retina (object
 localization). This design allows complex visual behaviors to be obtained by 
composing these two routines with different parameters.

The iconic representations are comprised of high-dimensional feature vectors 
obtained from the responses of an ensemble of Gaussian derivative spatial filters 
at a number of orientations and scales. These representations are stored in two 
separate memories. One memory is indexed by image coordinates while the other is 
indexed by object coordinates. Object localization matches a localized set of model
 features with image features at all possible retinal locations. Object 
identification matches a foveal set of image features with all possible model 
features. We present experimental results for a near real-time implementation of 
these routines on a pipeline image processor and suggest relatively simple 
strategies for tackling the problems of occlusions and scale variations. We also 
discuss two additional visual routines one for top-down foveal targeting using 
log-polar sensors and another for looming detection, which are facilitated by the 
proposed architecture.    }
}