CASIA OpenIR  > 毕业生  > 硕士学位论文
多尺度MRF模型图象分割
陈韵强
Subtype工学硕士
Thesis Advisor马颂德
1998-06-01
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract本论文以图象中的上下文关系为主线,讨论了图象处理中的两个问题。首先 是通过扩展传统MRF模型得到了一个新的多尺度MRF模型,提供了一种在多 尺度下处理“不适定”问题的框架,并用以完成多尺度下的图象恢复;其次,我 们根据Terman及Wang提出的非线性振荡神经元模型完成在尺度空间中的多尺 度图象分割算法。 我们首先对传统MRF模型进行了分析,指出了传统算法中优化困难、无法 充分利用图象中上下文信息的缺点。这主要是由于传统模型中邻域结构造成的。 我们在论文中结合多尺度处理中的一些思想,通过建立尺度空间中的邻域结构, 建立了在离散的尺度空间中的MRF模型,将MRF模型中的约束(如平滑性约 束)扩展到不同尺度的图象中。 为了提高算法的鲁棒性,我们结合了鲁棒性估计的一些方法,将图象中的细 节及偏离较大的噪声点做为异常点来考虑。考虑到图象中合理边界存在的可能 性,我们提出了一种新的异常点(outlier)检测方法,并防止在图象恢复过程中过 度平滑物体边界。 另外,在本论文中,我们改进了Terman及Wang的动态神经网络并用于多 尺度的图象分割。在这种神经网络中,分割结果通过神经元的时间同步性表示出 来,属于同一物体的象素点同步振荡,而分属于不同物体的象素点则异步兴奋。 这种新的表达方式解决了传统神经网络中的组合爆炸问题。 我们利用神经元间的局部兴奋性连接实现了神经元与其相邻(尺度空间中) 的灰度相似的点之间的同步振荡,并利用全局性抑制子实现了代表不同物体的神 经元的异步振荡。计算机模拟表明同步及异步可在几个振荡周期内实现。我们还 给出了一些对真实图象的分割结果。
Other AbstractIn this paper, we address two problems concerning the contextual information. First, we extend the traditional MRF model into multi-scale structure and provide a frame for dealing the ill-posed problems in multi-scale space. We also apply it to multi-scale image restoration. Second, we provide a new representation and segmentation scheme that is based on the dynamic link architecture neural network by Terman and Wang. We first analysis the traditional MRF model and point out that the limit of the neighbor-hood system caused the defects of the MRF model. It is hard to optimize the objective function and utilize the contextual information with such limited neighbor- hood. We make a new definition of neighbor-hood in scale-space and extend the traditional MRF Model into pyramid structure and hence extend the constrain (such as smooth constrain) into pyramid structure. For a more robust and efficient method, we combine the outlier rejection method in our model. Considers the presence of edge, our method can give a much accurate ourlier detection and hence can provide the edges from being overly smoothed. We also extend the dynamic link architecture neural network to segment images in scale space. In this kind of neural network, the segmentation result is represented by the correlation of the activities of the neurons. The neurons within the same object will tent to synchronize in their oscillation, while the neurons in different object will desynchronize. This new representation scheme resolves the problem of "combination explosion".
shelfnumXWLW457
Other Identifier457
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7208
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
陈韵强. 多尺度MRF模型图象分割[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1998.
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