47. A system for detecting instances of an object in a 2D (two-dimensional) image, the system comprising: means for computing a transform of a digitized version of said 2D image containing a 2D representation of said object, wherein said transform generates a plurality of transform coefficients; means for applying a plurality of view-based classifiers to the plurality of transform coefficients, wherein each view-based classifier is configured to detect a specific orientation of said object in said 2D image based on visual information received from corresponding transform coefficients, wherein at least one of said plurality of view-based classifiers includes a plurality of cascaded sub-classifiers, and wherein at least one of said plurality of sub-classifiers is based on a ratio of a plurality of graphical probability models; wherein a classifier is determined by: applying a view-based classifier to said 2D image, wherein said classifier includes a plurality of sub-classifiers; computing a sum of log-likelihood ratios for each of said plurality of sub-classifiers, wherein said log-likelihood ratio is a ratio of two graphical probability models; wherein a graphical probability model comprises a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables; and means for detecting orientation and location of each instance of said object in said 2D image from a combination of results of the application of said plurality of view-based classifiers.
48. The system of claim 47 , wherein each of said plurality of graphical probability models is a Bayesian network.