Image Segmentation using Domain Constraint Knowledge

图像分割的当前趋势表明，为了 实现连接良好的结果，某种形式的领域知识 必须使用。 基于知识的图像分割系统 传统上使用对象及其相互关系的模型 以指导或后处理低级分段操作。 一 该方法主要缺点是 除了琐碎的领域之外，所有领域都需要知识库。 的 知识通常很难嵌入系统， 通常不容易转移到其他领域。 还有， 处理时间可能很长。 建议工作的目标是探索使用小 一组域约束，即照明、摄像机和常规 物体表面特性，生产合格 分割结果，无需对对象进行分类 不是象预料 由于图像可以用图形方式构造， 表面方向、颜色和反射率，沿着 照明的位置和类型，假设一般 域信息通常足以执行适当的 细分 拟议的系统将利用这些知识， 驱动低级专家细分系统，确定 参数、选择策略和解决歧义 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.