کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
384452 | 660847 | 2012 | 11 صفحه PDF | دانلود رایگان |

Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures.
► Two new methods for segmentation of images based on DPSO and FODPSO were proposed.
► Those were used to overcome the disadvantages of other evolutionary methods.
► FODPSO is able to find better thresholds with more stability in less CPU time.
Journal: Expert Systems with Applications - Volume 39, Issue 16, 15 November 2012, Pages 12407–12417