一种神经马尔可夫多分辨率图像标记算法
A Neural Markovian Multiresolution Image Labeling Algorithm
摘要：
本文介绍了正式评估的MCV（马尔可夫并发视觉）图像标记算法，这是一个（半）分层算法开始与分区由单像素区域和合并区域或区域子集使用马尔可夫随机场（MRF）图像模型的结果。它是计算机视觉的通用方法的一个例子，称为并发视觉，其中图像分割和图像分类的操作同时进行。虽然许多图像标记算法输出单个分区或分割，但MCV算法输出一系列分区，并且这种更精细的结构可以提供对更高级视觉系统有价值的信息。对于某些类型的MRF，用于图像评估的系统的组件可以被实现为硬连线的前馈神经网络。虽然适用于图像（即2D信号），但该算法同样适用于1D信号（例如语音）或3D信号（例如视频序列）（尽管其在这些域中的性能仍有待测试）。该算法进行了评估，使用主观和客观的标准，具有很好的效果。
英文摘要：
This paper describes the results of formally evaluating the MCV (Markov concurrent vision) image labeling algorithm which is a (semi-) hierarchical algorithm commencing with a partition made up of single pixel regions and merging regions or subsets of regions using a Markov random field (MRF) image model. It is an example of a general approach to computer vision called concurrent vision in which the operations of image segmentation and image classification are carried out concurrently. While many image labeling algorithms output a single partition, or segmentation, the MCV algorithm outputs a sequence of partitions and this more elaborate structure may provide information that is valuable for higher level vision systems. With certain types of MRF the component of the system for image evaluation can be implemented as a hardwired feed forward neural network. While being applicable to images (i.e. 2D signals), the algorithm is equally applicable to 1D signals (e.g. speech) or 3D signals (e.g. video sequences) (though its performance in such domains remains to be tested). The algorithm is assessed using subjective and objective criteria with very good results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
