Convergence of Calculated Features in Image Analysis

This paper informs about number-theoretical and geometrical estimates of worst-case bounds for quantization errors in calculating features such as moments, moment based features, or perimeters in image analysis, and about probability-theoretical estimates of error bounds (eg. Standard derivations) for such digital approximations. New estimates (with proofs) and a review of previously known results are provided. 1 CITR, Tamaki Campus, University of Auckland, Private Bag 92019, Auckland, New Zealand 2 University of Novi Sad, Faculty of Engineering, Trg D. Obradovica 6 21000 Novi Sad, Yugoslavia Multigrid Convergence of Calculated Features in Image Analysis Reinhard Klette (1) and Jovi sa Zuni c (2) (1) CITR, University of Auckland, Tamaki Campus, Building 731 Auckland, New Zealand (2) University of Novi Sad , Faculty of Engineering, Trg D. Obradovi ca 6 21000 Novi Sad, Yugoslavia Abstract. This paper informs about estimates of worst-case bounds for quantization errors in calculating features such as moments, moment based features, or perimeters in image analysis, and about probabilitytheoretical estimates of error bounds (eg. standard deviations) for such digital approximations. New estimates (with proofs) and a review of previousely known results are provided. This paper informs about estimates of worst-case bounds for quantization errors in calculating features such as moments, moment based features, or perimeters in image analysis, and about probabilitytheoretical estimates of error bounds (eg. standard deviations) for such digital approximations. New estimates (with proofs) and a review of previousely known results are provided.