Image Segmentation using Domain Constraint Knowledge

当前图像分割的趋势表明，为了获得一致的好结果，必须使用某种形式的领域知识。基于知识的图像分割系统传统上使用对象模型及其相互关系来指导或后处理低层分割操作。这种方法的一个主要缺点是，除了微不足道的领域外，所有领域都需要的知识库的规模和复杂性。这些知识往往很难嵌入到系统中，而且通常不容易转移到其他领域。此外，处理时间可能会很长。这项工作的目标是探索使用一小组领域约束，即光照、相机和一般对象表面特征，以产生可接受的分割结果，而不需要事先对对象进行分类。由于图像可以仅使用表面方向、颜色和反射率以及照明的位置和类型来图形化地构建，因此假设一般的域信息通常将足以执行适当的分割。提出的系统将使用这些知识来驱动一个低级别的专家分词系统，确定参数，选择策略，并解决歧义解释。

Current trends in image segmentation indicate that in order to achieve connsistently good results, some form of domain knowledge must be used. Knowledge-based image-segmentation systems traditionally use models of objects and their interrelationships to guide or postprocess the low-level segmentation operation. A major shortcoming of that appproach is the size and complexity of the knowledge base needed for all but trivial domains. The knowledge is often quite difficult to embed in the system and usually is not transferred easily to other domains. Also, processing time can be large. The goal of the proposed work is to explore the use of a small set of domain constraints, namely lighting, camera, and general object surface characteristics, to produce acceptable segmentation results without the need to classify objects beforehand. As images can be constructed graphically using only surface orientation, color, and reflectivity, along with the position and type of lighting, it is hypothesized that general domain information will often be sufficient to perform adequate segmentation. The proposed system will use this knowledge to drive a low-level expert segmentation system, determining parameters, selecting strategies, and resolving ambiguous interpretations.