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In this work a framework to measure the influence of training image resolution on classification performance for appearance-based object detection algorithms is presented. It is shown that based on sampling theory a reasonable image resolution for feature extraction can be chosen in advance, that is prior to the time consuming feature extraction and testing of the classifier. This is possible due to measuring the signal energy that is preserved in a low resolution image with respect to the optimal case of a high resolution image. The approach is justified using an AdaBoost algorithm with Haar-like features for vehicle detection. Tests of classifiers, trained with different resolutions, are performed and the results are presented. These results reveal that there is a good tradeoff between classification performance and computational load. The presented framework helps choosing a resolution for a good description of the training data.