基于降雪模型的图像轮廓提取方法研究
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摘要
作为一个非常重要的过程,轮廓提取直接影响到智能视觉系统中特别是模式识别的结果。作为经典图像分割方法,分水岭算法被广泛运用,却存在严重过分割问题。基于边缘的水平集方法可以自然地改变轮廓曲线的拓扑,在图像轮廓提取中广泛应用,但是却因大量的迭代计算导致了巨大的时间开销。
本文针对上述的焦点问题进行了深入研究,主要内容有以下几点:
1)自适应曲面进化降雪模型
本文借鉴基于模拟降雪的图像分割新思想,探索具有图像区域特征引导下的图像轮廓曲线进化和符合人们视觉特性的图像轮廓提取方法。数据自适应的高斯核可以根据图像区域特征信息实现自适应的图像曲面进化,鉴于此,本文将数据自适应的高斯核引入降雪模型中,尝试模拟简单降雪模型,即自适应曲面进化降雪模型。与模拟降雪过程相似,基于降雪模型的地表曲面的进化隐含了地面轮廓曲线进化,并具有拓扑适应能力。这些讨论与研究为图像轮廓提取提供了新的研究思路。
2)基于降雪模型自适应曲面进化的分水岭图像轮廓提取方法
结合降雪模型自适应曲面进化的优点,本文进一步研究了基于降雪模型自适应曲面进化的分水岭图像轮廓提取方法。首先,在降雪模型自适应曲面进化机制下实现图像曲面进化,以平滑图像中大尺度细节信息,同时达到抑制噪声的效果。其次,原始图像的轮廓信息通过融合梯度与检测出来的边缘信息得以增强。最后,谷底填充被引入以控制分水岭分割区域,最终实现更符合人们视觉特性要求的轮廓提取。
3)降雪模型自适应曲面进化机制下的水平集图像轮廓提取方法
结合降雪模型的自适应曲面进化,文中研究了降雪模型自适应曲面进化机制下的水平集图像轮廓提取方法。基于降雪模型的自适应曲面进化机制,新方法可以将图像中隐含的重要特征信息得以展现。在传统水平集方法中,高斯滤波器被用来计算边缘指示算子,为了加以改进,在新方法中高斯滤波器被数据自适应曲面进化核函数取而代之。原始图像经过自适应的曲面进化处理后,可以得到其进化后的特征图像,基于特征图像水平集方法得以实现,从而实现轮廓提取。
本文结合降雪模型,针对分水岭的过分割问题,研究了改进的分水岭轮廓提取新方法;在降雪模型的自适应进化机制下讨论了水平集轮廓提取新方法,实现了对经典方法的改进和创新。实验结果证明了新方法的优越性,新方法、新思路的提出进一步完善了基于分水岭和水平集的轮廓提取理论。
As a very important process, contour extraction directly affects the results in the intelligent vision system, especially pattern recognition. As a classical image segmentation method, watershed has been widely used, but existing serious over-segmentation problem. The level set method, which is based on the edge, can naturally change the topology of contour curve, so it is widely used in image contour extraction,however, large number of iterative calculation causes great time consuming.
This paper studied these focus problems above, and main research work as follows:
1) Adaptive surface evolution snow model
Based on the new image segmentation idea of simulating snowfall, this paper focuses on exploring image contour extraction method, which fits on the people visual characteristics and has the image contour curved evolution guided by regional characteristics. According to the image regional characteristics information, data adaptive Gaussian kernel can realize adaptive image surface evolution, in view of this point, in this article data adaptive Gaussian kernel will be introduced into the snow model, simple snow model, i.e. adaptive evolutionary snow model surface, is proposed. And similar to the process of simulating snowfall, the ground surface evolution based on that new model, implies a simple surface contour evolution, and has the topological adaptability. These discussions and researches will provide some new ideas for image contour extraction.
2) Watershed image contour extraction based on adaptive curved surface evolution of snow model
Combined with the adaptive surface of snowfall model, watershed image contour extraction method based on snow model adaptive surface evolution is proposed in this paper. Firstly, image curved surface evolution is realized under adaptive curved surface evolution mechanism of snow model, to smoothing the large scale details information in the images, meanwhile suppressing noise effect. Secondly, the contour information of original image is enhanced through the fusion with the gradient and detected edge information. Finally, the bottom filling is introduced to control the watershed segmenting region, and realizing contour extraction,which can fit better with the people visual property requirements.
3) Level set image contour extraction method under adaptive curved surface evolution mechanism of snow model
Combined with adaptive curved surface evolution of snow model, a level set image contour extraction method under adaptive curved surface evolution mechanism of snow model is proposed in this paper. Based on adaptive curved surface evolution mechanism of snow model, the important feature information underlying the image in the can be shown. In traditional level set method, Gaussian filter is used to calculate edge operator, in order to improve the method, in the new method Gaussian filter is instead with the data adaptive curved surface evolution kernel function. The original image is processed by adaptive curved surface evolution, and then the evaluated characteristics image is obtained, based on the characteristics image level set method can be realized, so as to realize the contour extraction.
Combined with the snow model, aiming at the over-segmentation, the improved watershed contour extraction method is proposed. Under the adaptive evolution mechanism of the snow model, a new level set contour extraction method is discussed, and the improvement and innovation with classical method is realized. The experimental and theoretical analysis proves the superiority of the new method, all of these new method and new ideas will further perfect the contour extraction theory based on the watershed and the level set.
本文针对上述的焦点问题进行了深入研究,主要内容有以下几点:
1)自适应曲面进化降雪模型
本文借鉴基于模拟降雪的图像分割新思想,探索具有图像区域特征引导下的图像轮廓曲线进化和符合人们视觉特性的图像轮廓提取方法。数据自适应的高斯核可以根据图像区域特征信息实现自适应的图像曲面进化,鉴于此,本文将数据自适应的高斯核引入降雪模型中,尝试模拟简单降雪模型,即自适应曲面进化降雪模型。与模拟降雪过程相似,基于降雪模型的地表曲面的进化隐含了地面轮廓曲线进化,并具有拓扑适应能力。这些讨论与研究为图像轮廓提取提供了新的研究思路。
2)基于降雪模型自适应曲面进化的分水岭图像轮廓提取方法
结合降雪模型自适应曲面进化的优点,本文进一步研究了基于降雪模型自适应曲面进化的分水岭图像轮廓提取方法。首先,在降雪模型自适应曲面进化机制下实现图像曲面进化,以平滑图像中大尺度细节信息,同时达到抑制噪声的效果。其次,原始图像的轮廓信息通过融合梯度与检测出来的边缘信息得以增强。最后,谷底填充被引入以控制分水岭分割区域,最终实现更符合人们视觉特性要求的轮廓提取。
3)降雪模型自适应曲面进化机制下的水平集图像轮廓提取方法
结合降雪模型的自适应曲面进化,文中研究了降雪模型自适应曲面进化机制下的水平集图像轮廓提取方法。基于降雪模型的自适应曲面进化机制,新方法可以将图像中隐含的重要特征信息得以展现。在传统水平集方法中,高斯滤波器被用来计算边缘指示算子,为了加以改进,在新方法中高斯滤波器被数据自适应曲面进化核函数取而代之。原始图像经过自适应的曲面进化处理后,可以得到其进化后的特征图像,基于特征图像水平集方法得以实现,从而实现轮廓提取。
本文结合降雪模型,针对分水岭的过分割问题,研究了改进的分水岭轮廓提取新方法;在降雪模型的自适应进化机制下讨论了水平集轮廓提取新方法,实现了对经典方法的改进和创新。实验结果证明了新方法的优越性,新方法、新思路的提出进一步完善了基于分水岭和水平集的轮廓提取理论。
As a very important process, contour extraction directly affects the results in the intelligent vision system, especially pattern recognition. As a classical image segmentation method, watershed has been widely used, but existing serious over-segmentation problem. The level set method, which is based on the edge, can naturally change the topology of contour curve, so it is widely used in image contour extraction,however, large number of iterative calculation causes great time consuming.
This paper studied these focus problems above, and main research work as follows:
1) Adaptive surface evolution snow model
Based on the new image segmentation idea of simulating snowfall, this paper focuses on exploring image contour extraction method, which fits on the people visual characteristics and has the image contour curved evolution guided by regional characteristics. According to the image regional characteristics information, data adaptive Gaussian kernel can realize adaptive image surface evolution, in view of this point, in this article data adaptive Gaussian kernel will be introduced into the snow model, simple snow model, i.e. adaptive evolutionary snow model surface, is proposed. And similar to the process of simulating snowfall, the ground surface evolution based on that new model, implies a simple surface contour evolution, and has the topological adaptability. These discussions and researches will provide some new ideas for image contour extraction.
2) Watershed image contour extraction based on adaptive curved surface evolution of snow model
Combined with the adaptive surface of snowfall model, watershed image contour extraction method based on snow model adaptive surface evolution is proposed in this paper. Firstly, image curved surface evolution is realized under adaptive curved surface evolution mechanism of snow model, to smoothing the large scale details information in the images, meanwhile suppressing noise effect. Secondly, the contour information of original image is enhanced through the fusion with the gradient and detected edge information. Finally, the bottom filling is introduced to control the watershed segmenting region, and realizing contour extraction,which can fit better with the people visual property requirements.
3) Level set image contour extraction method under adaptive curved surface evolution mechanism of snow model
Combined with adaptive curved surface evolution of snow model, a level set image contour extraction method under adaptive curved surface evolution mechanism of snow model is proposed in this paper. Based on adaptive curved surface evolution mechanism of snow model, the important feature information underlying the image in the can be shown. In traditional level set method, Gaussian filter is used to calculate edge operator, in order to improve the method, in the new method Gaussian filter is instead with the data adaptive curved surface evolution kernel function. The original image is processed by adaptive curved surface evolution, and then the evaluated characteristics image is obtained, based on the characteristics image level set method can be realized, so as to realize the contour extraction.
Combined with the snow model, aiming at the over-segmentation, the improved watershed contour extraction method is proposed. Under the adaptive evolution mechanism of the snow model, a new level set contour extraction method is discussed, and the improvement and innovation with classical method is realized. The experimental and theoretical analysis proves the superiority of the new method, all of these new method and new ideas will further perfect the contour extraction theory based on the watershed and the level set.
引文
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