An algorithm for determination of threshold value in extruded products by the method of maximum increments: modi cation of Otsu method. There are presented the results of comparative analysis of threshold value determination in the extruded products with the use of developed new algorithm. The original and modi ed Otsu algorithms were compared. In calculations of porosity indices of the extrudate cross section, the threshold values determined automatically with the use of investigated algorithms were compared with indications of an experts’ panel. The results of threshold value calculated with the use of the proposed algorithm were closer to values indicated by the expert panel, than that obtained with the use of comparative algorithms. However, the proposed method tends to overstate the threshold value; in some cases it can cause losing the pores of small lacunarity on the bit map calculated by this method.
In recent years, oil spill accidents have become increasingly frequent due to the development of marine transportation and massive oil exploitation. At present, satellite remote sensing is the principal method used to monitor oil spills. Extracting the locations and extent of oil spill spots accurately in remote sensing images reaps significant benefits in terms of risk assessment and clean-up work. Nowadays the method of edge detection combined with threshold segmenta- tion (EDCTS) to extract oil information is becoming increasingly popular. However, the current method has some limitations in terms of accurately extracting oil spills in synthetic aperture radar (SAR) images, where heterogeneous background noise exists. In this study, we propose an adaptive mechanism based on Otsu method, which applies region growing combined with both edge detection and threshold segmentation (RGEDOM) to extract oil spills. Remote sensing images from the Bohai Sea on June 11, 2011 and the Gulf of Dalian on July 17, 2010 are utilized to validate the accuracy of our algorithm and the reliability of extraction results. In addition, results according to EDCTS are used as a comparator to further explore validity. The comparison with results according to EDCTS using the same dataset demonstrates that the proposed self-adapting algorithm is more robust and boasts high-accuracy. The accuracy computing by the adaptive algorithm is significantly improved compared with EDCTS and threshold method.
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