摘要
为了提高自然场景中物料袋的分割、检测与识别效率,提出了一种基于模糊理论的物料袋场景图像自适应增强算法。该算法运用Otsu算子对图像背景与目标进行自适应分类,并确定目标与背景图像像素的最佳渡越点;然后采用新的线性隶属度函数将图像对比度值从灰度域变换为模糊域,通过渡越点计算广义增强算子(GFO)与双曲正切函数的关联参数,并利用广义增强算子与双曲正切函数对图像进行增强变换;最后采用线性变换与灰度值叠加的方法将变换值由模糊域映射为灰度域。实验结果显示,该算法在减少迭代次数的同时,使图像局部细节得到有效增强,在实现算法自适应的条件下,可以得到良好的视觉效果。
In order to improve segmentation, detection and recognition efficiency of the materials image bag, this paper proposed an adaptive enhancement algorithm of materials bag scene based on fuzzy theory. Firstly, it classified the image background and objectives by using the Otsu operator, then determined the crossover point of the objectives and the background image. Secondly, it applied a novel linear membership function to map the image gray value into fuzzy domain, then computed the correlation parameter of the generalized enhancement operator （GFO） and the hyperbolic tangent function by the crossover point. It transformed the enhancement result by using the generalized enhancement operator and the hyperbolic tangent function. Finally, it used the superposition method to map the fuzzy values into the gray domain. The experimental result shows that, the local image details are effectively enhanced while the algorithm uses fewer iterations, under the adaptive conditions of realization of algorithm, the algorithm also has better visual effect.
出处
《计算机应用研究》
CSCD
北大核心
2015年第8期2547-2550,共4页
Application Research of Computers
基金
贵州省科技厅
合肥市科技局
合肥师范学院联合基金项目(黔科合J字LKZS[2014]23号
黔科合J字LKZS[2014]08号)
合肥师范学院教学科研项目(13-08
13-06)
关键词
物料袋
图像增强
模糊集理论
双曲正切函数
materials bag
image enhancement
fuzzy set theory
hyperbolic tangent function
