Computer Vision
Computer vision, like image processing, deals with digital images, but it differs greatly in its goals in the techniques that it uses．Image processing is primarily concerned with image-to-image operations，both the input and the output are images. In computer vision, on the other hand, the input still consists of images, but the goal is now to construct adscription of the scene from which the images were obtained. Such a description usually involves recognizing objects that are present in the scene and de-termini their properties and relationships The tech-inquest used to achieve this goal come primarily from from pattern recognition and artificial intelligence, rather than from signal processing．
Computer vision is still, in some respects, more of an art than a science, Standard signal processing approaches are not sufficient to handle the range of problems and tasks encountered in vision. Advances in pattern recognition and artificial intelligence have led to most of the progress in the field, but major advances are still needed if significant further progress is to be achieved. The field of computer vision had its beginning over 25 years ago, and has let to many practical applications, but it still faces many challenges．For example, in the area of document processing, systems that read printed characters have been commercially available for many years，but reading unconstrained handwriting is still a research problem. In industrial applications，systems for various types of simple inspection task(e.g. Checking the alignment of into-grated circuit chip) are already in use, but systems that can allow a robot to pick parts out of a bin aren’t yet practical. Work is needed on the closer integration, and on the development of "expert systems that can make use of problem domain specific knowledge in controlling the application of these techniques to given visual tasks．
翻译：
计算机视觉
计算机视觉与图象处理一样 也是处理数字图象，但两者在所要达到的目的与所采用的技术方面差别很大。图象处理主要关心图象到图象的运算，即输入与输出都是图象。计算机视觉却不一样，输入仍是由图象组成，但其目的是形成一个对景物描述，输入的图象就是从此景物中获得 的。这样的一个描述通常涉及到对景物中物体的识别和确定它们的特性和相互关系。用来达到这种目的的技术主要来自模式识别和人工智能，而不是信号处理。在某些方面，计算机视觉仍是一种技巧，还不完全是一门科学标准的信息处理方法对视觉中所涉及的多种多样的问题与任务来说是不能胜任的。而模式识别与人工智能的进展已导致了计算机视觉领域中的多项进步，但是，如果要获得进一步有意义的进步，还需要模式识别和人工智能有一重大进展。
计算机视觉这个领域起始于25年前，至今己有多种实际应用，但是它仍面临许多问题等待解决。例如，在文字资料处理方面，很多年前就可一从市场上沟得能阅读印刷字符的系统，但是，阅读小规则的手写字符仍是一个研究课题。在工业应用中，完成各种类型的简单检查任务（例如，检查集成电路芯片对准）的系统早已投入使用，但是使机器人从箱子里把零件取出来的系统尚未实用。今后还需要做工作，把信号处理技术与模式识别技术更紧密地结合起来以及开发“专家系统”，这种专家系统在控制这些技术应用于给定的视觉任务中能够利用问题范畴的特定知识。
文章来源于领测软件测试网 https://www.ltesting.net/
