图像分割若干问题的研究与应用
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摘要
图像分割是图像处理中一个基本而关键的环节,是图像分析和机器视觉研究的基础。虽然,近些年的研究已经取得了一定卓有成效的成果。但是,面对众多的图像分割问题,还是存在着一些有待进一步探讨的地方。本文根据不同的分割特征对图像分割方法进行了较为深入的研究,具体工作如下：
(1)针对图像的阈值分割问题,提出了一种基于超模糊集的多属性阈值分割算法。将图像分割问题引申到超模糊集中进行解决,利用推广到超模糊集合下的超模糊熵和超模糊相似性的概念,构造综合评价函数,从不同角度刻画分割性能,获得最佳阈值。与传统方法相比,该方法体现了更好分割准确性。
(2)针对图像的边缘分割问题,提出了一种基于边缘分类与Hopfield网络优化的边缘检测方法。首先利用改进的边缘定义,有效地解决了传统方法对边缘拐点,终点,以及孤立噪声点的误检问题；然后,利用Hopfield网络对所得的初边缘进行优化,通过弥补缺失边缘和消除假边缘达到精确检测的目的。实验证明,这种多阶段的边缘检测方法能够有效地检测出粗细适度的图像边缘。
(3)提出了基于视觉感知理论和基于交互式分割两种轮廓提取方法。基于视觉感知理论的轮廓提取方法,通过模拟初级视皮层细胞抑制与增强作用,有效地抑制了背景纹理的干扰,增强了具有相同结构神经元的响应,得到了清晰的轮廓；而基于结构张量的随机游走分割算法,则通过提出一种自适应各向异性的结构张量,代替灰度值作为随机游走分割所需的权值,解决了传统随机游走算法,无法体现待分割图像局部结构的问题。两种分割方法都改善了在区域分割过程中,局部结构中背景纹理信息对图像分割的影响。实验表明,两种分割方法都取得了较好的分割效果。
(4)利用视觉感知理论,将图像的全局特征和局部特征结合,提出一种无监督的显著目标自动识别方法。通过模拟不同尺度、不同朝向的细胞响应,有效地提取到了图像的全局特征。然后,再结合显著目标的自身特性,定义图像的局部特征,进一步确定感兴趣区域的位置。从而准确地检测出图像中的显著目标。
(5)针对车牌检测过程中的定位问题,提出一种新的解决策略。通过分析我国车牌的特点,算法提出四种车牌特征描述算子,结合数学形态学和自组织映射神经网络,实现车牌的精确定位。实验结果表明,所提定位策略有效地克服了传统单一特征检测方法的不足,针对80幅彩色图像进行测试,总有效率可达96.25%。
Image segmentation is a basic and important problem in image processing, and it is also the foundation of the researches on image analysis and computer vision. Although, some effective algorithms have been proposed in recent years, there are some deficiencies and problems that needed to be improved in most researches. This paper deeply investigates image segmentation according to different segmentation features, and the primary works and remarks are as follows:
(1) For the threshold segmentation, this paper presents a new threshold segmentation measure based on the multi-properties in ultra-fuzzy set. The algorithm solves the segmentation problem in ultra-fuzzy set, and obtains the optimal threshold by comprehensive assessment function constructed by ultra-fuzzy entropy and ultra-fuzzy similarity, which evaluate performances of the segmentation operation from different aspects. Comparing the ordinary thresholding algorithms, the proposed method embodies better results.
(2) For the edge detection, we propose a new edge detection algorithm based on edge classification and Hopfield neural network optimization. Firstly, a new edge detector is employed to label the edges, which is improved for overcoming the miss-detection of abrupt and end of the edges, isolated noise. And then, the Hopfield neural net is applied for enhancement of the labeled edges by recovering missing edges and eliminating false edges. In experiments, this algorithm is proved that it can obtain the moderately thick edges for the image.
(3) This paper also presents two contour extraction algorithms, which are based on visual perception theory and interactive segmentation respectively. The visual perception based model effectively suppresses the interference of the background texture and enhances the response of the neurons with the same configuration, and obtains a clear contour by simulating the inhabitation and enhancement of cells in primary visual cortex. Moreover, the improved random walk algorithm embodies the better structure features of an image by proposing a new adaptive anisotropy structure tensor to replace the intensity to represent the weights in common random walk algorithm. Both of them overcome the interference of local texture information in the segmenting process. The experiments show that two extracting methods can all obtain the desired results.
(4) Propose an unsupervised algorithm for automatically extracting the salient object by combining the global feature and local feature of the image based on visual perception theory. The method firstly obtains the global feature by simulating the cell responses of different scales and different orientation. And then, define the local feature according to the unique features of the salient object as supplement. So, the salient object in an image can be located exactly.
(5) To locate the vehicle license palate accurately, a new detecting measure is proposed in this paper. The method presents four feature operators by analyzing the unique features of vehicle license palate to characterize vehicle license palate. And, combine them with morphology and self organizing map neural network to achieve the accurate location. The experiments prove that the proposed measure can overcome the deficiencies of the common locating algorithms, and the total correct recognition rate is 96.25% according to 80 test images.
(1)针对图像的阈值分割问题,提出了一种基于超模糊集的多属性阈值分割算法。将图像分割问题引申到超模糊集中进行解决,利用推广到超模糊集合下的超模糊熵和超模糊相似性的概念,构造综合评价函数,从不同角度刻画分割性能,获得最佳阈值。与传统方法相比,该方法体现了更好分割准确性。
(2)针对图像的边缘分割问题,提出了一种基于边缘分类与Hopfield网络优化的边缘检测方法。首先利用改进的边缘定义,有效地解决了传统方法对边缘拐点,终点,以及孤立噪声点的误检问题；然后,利用Hopfield网络对所得的初边缘进行优化,通过弥补缺失边缘和消除假边缘达到精确检测的目的。实验证明,这种多阶段的边缘检测方法能够有效地检测出粗细适度的图像边缘。
(3)提出了基于视觉感知理论和基于交互式分割两种轮廓提取方法。基于视觉感知理论的轮廓提取方法,通过模拟初级视皮层细胞抑制与增强作用,有效地抑制了背景纹理的干扰,增强了具有相同结构神经元的响应,得到了清晰的轮廓；而基于结构张量的随机游走分割算法,则通过提出一种自适应各向异性的结构张量,代替灰度值作为随机游走分割所需的权值,解决了传统随机游走算法,无法体现待分割图像局部结构的问题。两种分割方法都改善了在区域分割过程中,局部结构中背景纹理信息对图像分割的影响。实验表明,两种分割方法都取得了较好的分割效果。
(4)利用视觉感知理论,将图像的全局特征和局部特征结合,提出一种无监督的显著目标自动识别方法。通过模拟不同尺度、不同朝向的细胞响应,有效地提取到了图像的全局特征。然后,再结合显著目标的自身特性,定义图像的局部特征,进一步确定感兴趣区域的位置。从而准确地检测出图像中的显著目标。
(5)针对车牌检测过程中的定位问题,提出一种新的解决策略。通过分析我国车牌的特点,算法提出四种车牌特征描述算子,结合数学形态学和自组织映射神经网络,实现车牌的精确定位。实验结果表明,所提定位策略有效地克服了传统单一特征检测方法的不足,针对80幅彩色图像进行测试,总有效率可达96.25%。
Image segmentation is a basic and important problem in image processing, and it is also the foundation of the researches on image analysis and computer vision. Although, some effective algorithms have been proposed in recent years, there are some deficiencies and problems that needed to be improved in most researches. This paper deeply investigates image segmentation according to different segmentation features, and the primary works and remarks are as follows:
(1) For the threshold segmentation, this paper presents a new threshold segmentation measure based on the multi-properties in ultra-fuzzy set. The algorithm solves the segmentation problem in ultra-fuzzy set, and obtains the optimal threshold by comprehensive assessment function constructed by ultra-fuzzy entropy and ultra-fuzzy similarity, which evaluate performances of the segmentation operation from different aspects. Comparing the ordinary thresholding algorithms, the proposed method embodies better results.
(2) For the edge detection, we propose a new edge detection algorithm based on edge classification and Hopfield neural network optimization. Firstly, a new edge detector is employed to label the edges, which is improved for overcoming the miss-detection of abrupt and end of the edges, isolated noise. And then, the Hopfield neural net is applied for enhancement of the labeled edges by recovering missing edges and eliminating false edges. In experiments, this algorithm is proved that it can obtain the moderately thick edges for the image.
(3) This paper also presents two contour extraction algorithms, which are based on visual perception theory and interactive segmentation respectively. The visual perception based model effectively suppresses the interference of the background texture and enhances the response of the neurons with the same configuration, and obtains a clear contour by simulating the inhabitation and enhancement of cells in primary visual cortex. Moreover, the improved random walk algorithm embodies the better structure features of an image by proposing a new adaptive anisotropy structure tensor to replace the intensity to represent the weights in common random walk algorithm. Both of them overcome the interference of local texture information in the segmenting process. The experiments show that two extracting methods can all obtain the desired results.
(4) Propose an unsupervised algorithm for automatically extracting the salient object by combining the global feature and local feature of the image based on visual perception theory. The method firstly obtains the global feature by simulating the cell responses of different scales and different orientation. And then, define the local feature according to the unique features of the salient object as supplement. So, the salient object in an image can be located exactly.
(5) To locate the vehicle license palate accurately, a new detecting measure is proposed in this paper. The method presents four feature operators by analyzing the unique features of vehicle license palate to characterize vehicle license palate. And, combine them with morphology and self organizing map neural network to achieve the accurate location. The experiments prove that the proposed measure can overcome the deficiencies of the common locating algorithms, and the total correct recognition rate is 96.25% according to 80 test images.
引文
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