In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold val...In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.展开更多
Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The pro...Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.展开更多
In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding....In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.展开更多
To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has prop...To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system.The steps in this research are as follows:1.The crack digital images of concrete specimens with typical fea-tures were collected by using the Actis system of KURABO Co,Ltd,of Japan in the concrete beam bending test.2.The images are segmented into blocks to dis-tinguish backgrounds of different grayscale.3.The max imum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented.4.Segmentation is made to the image with 2D max imum entropy threshold segmentation method to obtain the binary image,and the target image can be obtained by screening the connected domain features of the binary image.Results have shown that compared with other algo-rithms,the proposed method can effectively decrease the image over-segmentation and under segmentation rates,highlight the characteristics of the target cracks,solve the problems of excessive difference between the identified length and actual length of cracks caused by background gray level change and uneven ilumnination,and effectively improve the recognition accuracy of bridge concrete cracks.展开更多
文摘In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.
文摘Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.
文摘In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.
文摘To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system.The steps in this research are as follows:1.The crack digital images of concrete specimens with typical fea-tures were collected by using the Actis system of KURABO Co,Ltd,of Japan in the concrete beam bending test.2.The images are segmented into blocks to dis-tinguish backgrounds of different grayscale.3.The max imum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented.4.Segmentation is made to the image with 2D max imum entropy threshold segmentation method to obtain the binary image,and the target image can be obtained by screening the connected domain features of the binary image.Results have shown that compared with other algo-rithms,the proposed method can effectively decrease the image over-segmentation and under segmentation rates,highlight the characteristics of the target cracks,solve the problems of excessive difference between the identified length and actual length of cracks caused by background gray level change and uneven ilumnination,and effectively improve the recognition accuracy of bridge concrete cracks.
