Decomposition of Arbitrarily Shaped Morphological Structuring Elements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Decoding by Heuristic Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characteristics of vision algorithms
Machine Vision and Applications - Special issue on performance evaluation
Random perturbation models for boundary extraction sequence
Machine Vision and Applications - Special issue on performance evaluation
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphologically Constrained GRFs: Applications to Texture Synthesis and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Morphology and Its Applications to Image and Signal Processing
Mathematical Morphology and Its Applications to Image and Signal Processing
Document Image Decoding Using Markov Source Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
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Detection followed by morphological processing is commonly used in machine vision. However, choosing the morphological operators and parameters is often done in a heuristic manner since a statistical characterization of their performance is not easily derivable. If we consider a morphology operator sequence as a classifier distinguishing between two patterns, the automatic choice of the operator sequence and parameters is possible if one derives the misclassification distribution as a function of the input signal distributions, the operator sequence, and parameter choices. The main essence of this paper is the illustration that misclassification statistics, the distribution of bit errors measured by the Hamming distance, can be computed by using an embeddable Markov chain approach. License plate extraction is used as a case study to illustrate the utility of the theory on real data.