Representations of Knowledge in Complex Systems
复杂系统中的知识表示AI 解读 ↓
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复杂系统中的知识表示
- 研究问题
- 如何将领域知识表示为概率模型，使算法能够从复杂生物医学图像中自动形成关于目标数量、位置、方向和形状的假设？
- 方法与思路
- 作者用模式理论在配置空间上构造先验，通过广义 Langevin 跳跃扩散与修正贝叶斯方法生成假设，并实现并行图像分析算法。
- 材料陈述的贡献
- 摘要称证明相关跳跃扩散过程的遍历性，给出加速漂移项计算的方法，并展示线粒体、膜等图像分析中的算法实现。
- 阅读关注点
- 应核对配置空间和先验如何编码知识、修正贝叶斯方法的定义、遍历性证明条件，以及图像示例使用了哪些评价标准。
- 阅读边界
- 仅据 Crossref 摘要。遍历性不直接给出有限计算时间的效果保证；摘要未报告识别精度或运行时间比较，也不能推断版本变化。
原文摘要
SUMMARY Modern sensor technologies, especially in biomedicine, produce increasingly detailed and informative image ensembles, many extremely complex. It will be argued that pattern theory can supply mathematical representations of subject-matter knowledge that can be used as a basis for algorithmic ‘understanding’ of such pictures. After a brief survey of the basic principles of pattern theory we shall illustrate them by an application to a concrete situation: high magnification (greater than 15000×) electron micrographs of cardiac muscle cells. The aim is to build algorithms for automatic hypothesis formation concerning the number, location, orientation and shape of mitochondria and membranes. For this we construct a pattern theoretic model in the form of a prior probability measure on the space of configurations describing these hypotheses. This measure is synthesized by solving sequentially a jump–diffusion equation of generalized Langevin form. The jumps occur for the creation–annihilation of hypotheses, corresponding to a jump from one continuum to another in configuration (hypothesis) space. These continua (subhypotheses) are expressed in terms of products of low dimensional Lie groups acting on the generators of a template. We use a modified Bayes approach to obtain the hypothesis formation, also organized by solving a generalized Langevin equation. to justify this it is shown that the resulting jump-diffusion process is ergodic so that the solution converges to the desired probability measure. to speed up the convergence we reduce the computation of the drift term in the stochastic differential equation analytically to a curvilinear integral, with the random term computed almost instantaneously. The algorithms thus obtained are implemented, both for mitochondria and membranes, on a 4000 processor parallel machine. Photographs of the graphics illustrate how automatic hypothesis formation is achieved. This approach is applied to deformable neuroanatomical atlases and tracking recognition from narrow band and high resolution sensor arrays.