A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the chara...A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.展开更多
Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obst...Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.展开更多
The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.展开更多
红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红...红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红外与可见光图像融合算法。首先,将对输入的红外图像和可见光图像进行FRGF多尺度分解,得到基底层和细节层图像。然后,基于改进的遗传算法和Renyi熵计算出最优阈值,将红外图像中的目标区域进行提取。最后,基底层使用比较匹配最大熵融合机制进行融合的方法;采用修正的拉普拉斯能量融合细节层。该算法融合了多尺度分解和自适应阈值分割的优点。实验结果表明,本文算法在主客观评价指标方面均优于多种经典融合算法,能够生成良好的融合结果。展开更多
基金This project was supported by Science and Technology Research Emphasis Fund of Ministry of Education(204010) .
文摘A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(60525303)Doctoral Foundation of Yanshan University(B243).
文摘Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.
基金Sponsored by The National Natural Science Foundation of China(60872065)Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation(20105152026)State Key Laboratory Open Fund of Novel Software Technology,Nanjing University(KFKT2010B17)
文摘The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
文摘红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红外与可见光图像融合算法。首先,将对输入的红外图像和可见光图像进行FRGF多尺度分解,得到基底层和细节层图像。然后,基于改进的遗传算法和Renyi熵计算出最优阈值,将红外图像中的目标区域进行提取。最后,基底层使用比较匹配最大熵融合机制进行融合的方法;采用修正的拉普拉斯能量融合细节层。该算法融合了多尺度分解和自适应阈值分割的优点。实验结果表明,本文算法在主客观评价指标方面均优于多种经典融合算法,能够生成良好的融合结果。
