Optimization of the Fast Image Binarization Method Based on the Monte Carlo Approach
AbstractThe paper concerns the problem of fast image processing in the low computational power systems with limited memory, which are typical for robot vision and embedded systems. Assuming the necessity of decision based on incomplete information when the amount of visual data is too big for an efficient processing, the reduction of their amount becomes a crucial element of the processing system. A good classical example may be histogram based image binarization which requires the knowledge of the distribution of intensities for the whole grey-scale image. Applying the Monte Carlo method for the reduction of the amount of data, much smaller images with similar statistical properties may be obtained, which can be further used for thresholding and binarization e.g. using Otsu algorithm. A relevant problem in this approach is the proper choice of the number of samples for the Monte Carlo method which influences the result of binarization. In this paper the method based on the analysis of image entropy or energy changes is proposed for this purpose. Obtained results, verified for various images, are promising even for relatively small number of samples used for the estimation of the histogram.
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