Quantitative Methods of Edge Detection
UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES IMAGE PROCESSING INST
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Most local operators used in edge detection can be modelled by one of two methods edge enhancementthresholding and edge fitting. This dissertation presents a quantitative design and performance evaluation of these methods. The design techniques are based on statistical detection theory and deterministic pattern recognition classification procedure. The performance evaluation methods developed include a deterministic measurement of the edge gradient amplitude b comparison of the probabilities of correct and false edge detection and c figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge enhancement thresholding operators. A performance comparison is given between these edge detectors. A new edge fitting algorithm is introduced. The new algorithm is derived in the discrete domain, this allows a direct optimization of the operators performance. The advantages of new algorithm are better performance with real world pictures and less sensitivity to signal-to-noise ratio.