Abstract:In model-based fitting, the model parameters that best fit the image are determined by searching for the optimum of an objective function. Often, this function is designed manually, based on implicit and domaindependent knowledge. We acquire more robust objective function by learning them from annotated images, in which many critical decisions are automated, and the remaining manual steps do not require domain knowledge. Still, the trade-off between generality and accuracy remains. General functions can be app… Show more
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