［翻译］计算机视觉

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions.A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image.Understanding in this context means the transformation of visual images (the input of retina) into descriptions of world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception.
计算机视觉是一个包括获取，处理，分析和理解图像的领域，在一般情况下，高纬数据是从现实世界中产生的数字或符号信息，例如决定的形式。在这个领域中的一个发展领域就是通过电子感知和图像理解来复制人类视觉的能力。在这个方面的理解意味着视觉图像转化成世界的描述，可以和其它的思维过程连接，引发恰当的行为。图像的理解可以被视作是使用在几何学，物理学，数据统计和学习理论的帮助下构建的模型，在图片数据中摆脱了符号信息。
计算机视觉可以被描述成是一个自动化，集成一个广泛领域的处理和视觉感知描述的的企业活动。


As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems.

作为一个科学的学科，计算机视觉是和提取图片信息的人工系统的原理是相关的。图片的数据可以是不同形式的，例如视频镜头，多个摄像机的风景照，或者是医学扫描器的多维度数据。作为一个技术学科，计算机视觉寻求应用它的理论和模型到这个计算机视觉系统的构建中。

Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, object pose estimation, learning, indexing, motion estimation, and image restoration.
子领域的计算机啊视觉包括场景的重建，事件的检测，视频的跟踪，物体的识别，物体姿态的评估，学习，索引，手势识别，和图像恢复。
Related fields
相关领域


Relation between computer vision and various other fields

Areas of artificial intelligence deal with autonomous planning or deliberation for robotical systems to navigate through an environment. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.
人工智能的领域通过一个环境导航解决自主的计划或者经过深思熟虑的自动化系统。一个详细的环境的导航是要求通过这些的。环境的信息可以由计算机啊视觉系统提供，作为一个视觉传感器提供关于环境和机器人的高级的信息。



Artificial intelligence and computer vision share other topics such as pattern recognition and learning techniques. Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.
人工智能和计算机视觉共享着其它的话题，例如模式识别和学习技巧。
因此，计算机视觉一般来说有时是被看作是人工智能领域或者计算机啊科学领域的一部分。


Solid-state physics is another field that is closely related to computer vision. Most computer vision systems rely on image sensors, which detect electromagnetic radiation which is typically in the form of either visible or infra-red light. The sensors are designed using quantum physics. The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of optics which are a core part of most imaging systems. Sophisticated image sensors even require quantum mechanics to provide a complete understanding of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example motion in fluids.
固态物理是另外一个和计算机视觉相关的领域。大多数电脑视觉系统取决于图像的传感器，它是典型的通过可见光或者红外线检测电磁辐射。这些传感器是使用量子物理设计的。光和表面交互的过程是通过物理来解释的。物理学解释光的行为是大多数图像系统的核心。聪明的图像传感器甚至要求量子的机制去提供一个完整理解图像形成的流程。当然，各种各样物理上测量的问题也可以使用计算机啊视觉去解决，例如流体的运动。

A third field which plays an important role is neurobiology, specifically the study of the biological vision system. Over the last century, there has been an extensive study of eyes, neurons, and the brain structures devoted to processing of visual stimuli in both humans and various animals. This has led to a coarse, yet complicated, description of how "real" vision systems operate in order to solve certain vision related tasks. These results have led to a subfield within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems, at different levels of complexity. Also, some of the learning-based methods developed within computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their background in biology.
第三个有着重要角色的领域是神经生物学，特别是在生物视觉系统方面的研究。在上个世纪，曾经有大范围的关于眼睛，神经和脑结构的学习，致力于在人类和不痛动物间的可视化刺激的处理。这导致了一个粗燥的，复杂的关于真正的视觉系统为了可以解决特定的视觉相关任务的描叙。这些结果在计算机视觉领域内引起了一个新的子领域，用于在不同级别的复杂度来设计模范生物系统的流程和行为。同时，一些在计算机视觉领域给予学习的方法（例如 神经网络，基于图像的深度学习，特征分析和分类）在生物学上也有它们的背景。

Some stands of computer vision research are closely related to the study of biological vision – indeed, just as many strands of AI research are closely tied with research into human consciousness, and the use of stored knowledge to interpret, integrate and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, studies and describes the processes implemented in software and hardware behind artificial vision systems. Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.
一些计算机视觉研究的标准是和生物视觉的学习是相关的－确实，正如很多标准的人工智能研究是和人的意识紧密联系在一起的，还有使用存储的知识去解释，集成和利用视觉信息。生物视觉领域研究和示范在人类和和其它动物视觉感知背后的物理过程。计算机视觉，从另外一个方面讲，研究和描述了人工视觉系统在软件和硬件实现的流程。在生物学和计算机视觉跨学科的交换已经被证实是在这两个领域是富有成效的。

Yet another field related to computer vision is signal processing. Many methods for processing of one-variable signals, typically temporal signals, can be extended in a natural way to processing of two-variable signals or multi-variable signals in computer vision. However, because of the specific nature of images there are many methods developed within computer vision which have no counterpart in processing of one-variable signals. Together with the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision.

另外一个和计算机视觉相关的就是信号处理。很多方法处理单元信号，典型是临时的信号，在计算机视觉中从一种自然的方式去处理双元信号或者多元信号。但是，由于特定的自然图片，这里有很多在计算机视觉里面开发的方法在处理单元信号的时候没有对应物。和多维信号一起，这里在信号处理中定义为一个子领域作为计算机视觉的一部分。

Beside the above-mentioned views on computer vision, many of the related research topics can also be studied from a purely mathematical point of view. For example, many methods in computer vision are based on statistics, optimization or geometry. Finally, a significant part of the field is devoted to the implementation aspect of computer vision; how existing methods can be realized in various combinations of software and hardware, or how these methods can be modified in order to gain processing speed without losing too much performance.
除了上面计算机视觉方面的观点，很多相关的研究主题也可以从一个单纯的数学视角去研究。例如，很多计算机视觉的方法都是基于统计，优化或者是几何学。最后，这个领域一个重要部分就致力于计算机视觉方面的实现；当前存在的方法如何在不同的软件和硬件组合中实现，或者这些方法如何被修改以获得不会影响太多性能的速度。


The fields most closely related to computer vision are image processing, image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are more or less identical, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented.

这些和计算机视觉相关的领域是图片处理，图片分析和机器视觉。这里有很多的重叠在这里覆盖的技术和应用中。这也暗示在这些领域里面使用和开发的技术或多或少是唯一的，可以这样解释，是同一个领域，但是却有不同的名字。另外一方面，对于那些想要表现或者推销自己是属于这些特定领域的研究组，科学杂志，会议和公司这是很有必要的，因此，存在着许多不同的角色刻画来区分这些不同领域。

Computer vision is, in some ways, the inverse of computer graphics. While computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality.
计算机视觉是，在某些方面，是计算机图像的相反事物。计算机图形从3D模型中制作计算机图像，计算机视觉经常从图像数据中生产3D模型。

The following characterizations appear relevant but should not be taken as universally accepted:
下面的角色刻画显得相关，但是不是普遍被接受。

• Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither require assumptions nor produce interpretations about the image content.

• Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, e.g., how to reconstruct structure or other information about the 3D scene from one or several images. 

图像处理和图像分析趋向于关注2D图像，怎样把一个图像转换成另外一个，例如，像素级的操作，例如对比增强，本地操作例如边缘提取或者去噪，几何学的操作例如旋转图片。这个角色划分意味着图片处理／分析既不需要假设或者关于其它图片内容的产品说明。计算机视觉包括了从2D图片里面的3D分析。这个将3D场景投射成一张或者几张图片，例如，如何从一张或者几张照片里面去重建关于3D场景的结构和其它信息。

• Computer vision often relies on more or less complex assumptions about the scene depicted in an image.

• Machine vision is the process of applying a range of technologies & methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications.Machine vision tends to focus on applications, mainly in manufacturing, e.g., vision based autonomous robots and systems for vision based inspection or measurement. 

• 计算机视觉经常或多或少取决于在图像中描述的场景的假定。计算机视觉是在工业应用中应用了一系列的技术和方法去提供基于图像的自动化检测，流程控制和机器人导航的处理过程。计算机视觉趋向于聚焦应用，主要是在制造，例如基于视觉的自动化机器人和系统，基于视觉的检测或测量。

• This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasised by means of efficient implementations in hardware and software. It also implies that the external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms.

• 这就暗示着图像传感器技术和控制理论经常和图像数据的处理流程是结合在一起用来控制机器人。实时的流程是强调通过软硬件的有效实现的。同时也暗示着外部的条件例如光也可以，也是在比起一般的计算机视觉中，在机器视觉中更多被控制，同时也能够使用不同的算法。

• There is also a field called imaging which primarily focus on the process of producing images, but sometimes also deals with processing and analysis of images. For example, medical imaging includes substantial work on the analysis of image data in medical applications.

• 这里也有一个成像领域，主要聚焦在处理图片的流程中，但是有时也处理一些图像的流程和分析。例如，医疗成像包括了在医疗应用中很重要的的图像数据的分析。

• Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to applying these methods to image data.

最后，模式识别一般是使用不同方法从信号中提取数据，主要基于数据分析方法和人工智能神经网络。一个这个领域的重要部分就是应用这些方法到图像数据中。

Photogrammetry also overlaps with computer vision, e.g., stereophotogrammetry vs. stereo computer vision.
摄影测量同样和计算机视觉重叠，例如立体摄影测量与计算机立体视觉。



Applications for computer vision     计算机视觉应用                                                                   Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:
应用的业务范围包括工业机器视觉系统，也就是说在一个生产线上检查瓶子的速度，人工智能的研究或者机器人可以理解它们周围的世界。计算机视觉和机器视觉领域有重要的重叠。计算机视觉覆盖了在许多自动图像分析领域的核心技术。计算机视觉通常指的是用其它的方法或者技术，结合了自动化图像分析的流程，提供在工业领域自动化检测和机器人导航。在很多计算机领域，电脑是预编程好去解决一个特定任务，但是基于学习的方法现在已经变得越来越常见。计算机视觉的应用例子包括以下系统：

• Controlling processes, e.g., an industrial robot; 控制流程。例如工业机器人

• Navigation, e.g., by an autonomous vehicle or mobile robot; 导航，例如无人驾驶汽车或者移动机器人

• Detecting events, e.g., for visual surveillance or people counting; 事件检测，例如视觉检测或者人员计数

• Organizing information, e.g., for indexing databases of images and image sequences; 组织信息，例如，数据库图片索引和图像序列

• Modeling objects or environments, e.g., medical image analysis or topographical modeling; 模型物体和环境，例如，医学图像分析或者地形模型。

• Interaction, e.g., as the input to a device for computer-human interaction, 交互，例人机交互，计算机设备输入

• Automatic inspection, e.g., in manufacturing applications. 自动化检测，例如，制造应用

One of the most prominent application fields is medical computer vision or medical image processing. This area is characterized by the extraction of information from image data for the purpose of making a medical diagnosis of a patient. Generally, image data is in the form of microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images. An example of information which can be extracted from such image data is detection of tumours, arteriosclerosis or other malign changes. It can also be measurements of organ dimensions, blood flow, etc. This application area also supports medical research by providing new information, e.g., about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images that are interpreted by humans, for example ultrasonic images or X-ray images, to reduce the influence of noise.

其中一个最重要的应用领域就是医疗计算机视觉或者医疗图像处理。这个领域是以从图像数据中提取信息为特征，用于标示病人的医疗诊断。一般来说，图像数据是以显微镜图片，X射线图片，血管摄影图片，超声波图片，层析图像的形式出现的。一个可以从那些图像数据中提取的样本信息有关于肿瘤的检测，动脉硬化或者有害的改变。它可以是器官维度的测试，血液流动等等。这个应用领域同时也通过提供新信息支持医疗研究，例如关于大脑的结构或者医疗治疗的质量。在医疗领域的计算机视觉应用也包括由人解释的图像增强，例如超声波图像或者X射线图像，减少噪声的影响等等。