（转载自wiki）可视计算定义

原文：

Visual computing is a generic term for all computer science disciplines handling with images and 3D models, i.e. computer graphics, image processing, visualization, computer vision, virtual and augmented reality, video processing, but also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. The core challenges are the acquisition, processing, analysis and rendering of visual information (mainly images and video). Application areas include industrial quality control, medical image processing and visualization, surveying, robotics, multimedia systems, virtual heritage, special effects in movies and television, and computer games.

History and overview

Visual computing is a relatively newly coined term, which got its current meaning around 2005,^[1] when the established computer science disciplines computer graphics, image processing, computer vision and others noticed that their methods and applications overlapped more and more, so that a new generic term was needed. 

Many of the used mathematical and algorithmic methods are the same in all areas dealing with images: image formats, filtering methods, color models, image metrics and others. And also the programming methods on graphics hardware, the manipulation tricks to handle huge data, textbooks and conferences, the scientific communities of these disciplines and working groups at companies intermixed more and more.

Furthermore, applications increasingly needed techniques from more than one of these fields concurrently. To generate very detailed models of complex objects you need image recognition, 3D sensors and reconstruction algorithms, and to display these models believably you need realistic rendering techniques with complex lighting simulation. Real-time graphics is the basis for usable virtual and augmented reality software. A good segmentation of the organs is the basis for interactive manipulation of 3D visualizations of medical scans. Robot control needs the recognition of objects just as a model of its environment. And all devices (computers) need ergonomic graphical user interfaces.

Although many problems are considered solved within the scientific communities of the sub-disciplines making up visual computing (mostly under idealistic assumptions), one major challenge of visual computing as a whole is the integration of these partial solutions into applicable products. This includes dealing with many practical problems like addressing a multitude of hardware, the use of real data (that is often erroneous and/or gigantic in size), and the operation by untrained users. In this respect, Visual computing is more than just the sum of its sub-disciplines, it is the next step towards systems fit for real use in all areas using images or 3D objects on the computer.

 

Visual computing disciplines (可视计算学科)

At least the following disciplines are sub-fields of visual computing. More detailed descriptions of each of these fields can be found on the linked special pages.（下述学科都是可视计算的子领域）

• Computer graphics and computer animation

Computer graphics is a general term for all techniques that produce images as result with the help of a computer. To transform the description of objects to nice images is called rendering which is always a compromise between image quality and run-time.

• Image analysis and computer vision

Techniques that can extract content information from images are called image analysis techniques. Computer vision is the ability of computers (or of robots) to recognize their environment and to interpret it correctly.

• Visualization and visual analytics

Visualization is used to produce images that shall communicate messages. Data may be abstract or concrete, often with no a priori geometrical components. Visual analytics describes the discipline of interactive visual analysis of data, also described as “the science of analytical reasoning supported by the interactive visual interface”.^[2]

• Geometric modeling and 3D-printing

To represent objects for rendering it needs special methods and data structures, which subsumed with the term geometric modeling. In addition to describing and interactive geometric techniques, sensor data are more and more used to reconstruct geometrical models. Algorithms for the efficient control of 3D printers also belong to the field of visual computing.

• Image processing and image editing

In contrast to image analysis image processing manipulates images to produce better images. “Better” can have very different meanings subject to the respective application. Also, it has to be discriminated from image editing which describes interactive manipulation of images based on human validation.

• Virtual and augmented reality

Techniques that produce the feeling of immersion into a fictive world are called virtual reality (VR). Requirements for VR include head-mounted displays, real-time tracking, and high-quality real-time rendering. Augmented reality enables the user to see the real environment in addition to the virtual objects, which augment this reality. Accuracy requirements on rendering speed and tracking precision are significantly higher here.

• Human computer interaction

The planning, design and uses of interfaces between people and computers is not only part of every system involving images. Due to the high bandwidth of the human visual channel (eye), images are also a preferred part of ergonomic user interfaces in any system, so that human-computer interaction is also an integral part of visual computing.

 

译文（google机翻）：

视觉计算是所有计算机科学学科使用图像和3D模型处理的通用术语，即计算机图形，图像处理，可视化，计算机视觉，虚拟和增强现实，视频处理，但还包括模式识别，人机交互，机器学习和数字图书馆。核心挑战是视觉信息（主要是图像和视频）的获取，处理，分析和渲染。应用领域包括工业质量控制，医学图像处理和可视化，勘测，机器人技术，多媒体系统，虚拟遗产，电影和电视特效以及计算机游戏。

历史和概述
视觉计算是一个相对较新的术语，它的最新含义在2005年左右出现[1]，这是因为已建立的计算机科学学科对计算机图形学，图像处理，计算机视觉等进行了研究，发现它们的方法和应用越来越重叠，因此，需要一个新的通用术语。

（通过计算机图形学，图像处理，计算机视觉等个学科间的交叉越来越广泛，以便更一般化的描述这种交叉领域或学科，“可视计算”名字应运而生。

可视计算是一个相对新的词汇，在2005年才明确当前可视计算的含义。 ）

在处理图像的所有领域中，许多使用的数学和算法方法都是相同的：图像格式，过滤方法，颜色模型，图像度量标准和其他。图形硬件的编程方法，处理海量数据的操纵技巧，教科书和会议，这些学科的科学团体以及公司的工作组越来越多地混杂在一起。

此外，应用程序越来越多地同时需要这些领域中不止一个领域的技术。要生成非常详细的复杂对象模型，您需要图像识别，3D传感器和重建算法，并且如实地显示这些模型，您需要具有复杂照明模拟的逼真的渲染技术。实时图形是可用的虚拟和增强现实软件的基础。器官的良好分割是医学扫描的3D可视化交互操作的基础。机器人控制需要识别对象，就像其环境的模型一样。并且所有设备（计算机）都需要符合人体工程学的图形用户界面。

尽管人们认为构成视觉计算的子学科的科学界解决了许多问题（大多数情况下是在理想主义的假设下），但整体而言，视觉计算的一项主要挑战是将这些部分解决方案集成到适用的产品中。这包括处理许多实际问题，例如处理大量硬件，使用实际数据（通常是错误的和/或巨大的大小）以及未经培训的用户进行操作。在这方面，视觉计算不仅仅是其子学科的总和，它是朝着使用计算机上的图像或3D对象在所有领域都适合实际使用的系统迈出的下一步。

视觉计算学科
至少以下学科是视觉计算的子领域。在链接的特殊页面上可以找到每个字段的详细说明。

计算机图形学和计算机动画
计算机图形学是所有在计算机的帮助下产生图像的技术的总称。将对象的描述转换为漂亮的图像称为渲染，它始终是图像质量和运行时间之间的折衷。

图像分析和计算机视觉
可以从图像中提取内容信息的技术称为图像分析技术。计算机视觉是计算机（或机器人）识别其环境并正确解释其环境的能力。

可视化和视觉分析
可视化用于生成将传达消息的图像。数据可以是抽象的或具体的，通常没有先验的几何成分。可视化分析描述了数据交互式可视化分析的学科，也被称为“交互式可视化界面支持的分析推理的科学”。[2]

几何建模和3D打印
为了表示要渲染的对象，需要特殊的方法和数据结构，其中包括术语几何建模。除了描述和交互式几何技术外，传感器数据也越来越多地用于重建几何模型。有效控制3D打印机的算法也属于视觉计算领域。

图像处理和图像编辑
与图像分析相反，图像处理可操纵图像以产生更好的图像。根据各自的应用，“更好”的含义可能完全不同。同样，它必须与图像编辑区分开来，后者描述了基于人工验证的图像交互操作。

虚拟和增强现实
产生沉浸在虚拟世界中的感觉的技术称为虚拟现实（VR）。 VR的要求包括头戴式显示器