Posted on Categories:Digital image processing, 图像处理, 数学代写

# 数学代写|图像处理代写Digital image processing代考|GPY470 Image Processing and Computer Graphics

avatest™

## avatest™帮您通过考试

avatest™的各个学科专家已帮了学生顺利通过达上千场考试。我们保证您快速准时完成各时长和类型的考试，包括in class、take home、online、proctor。写手整理各样的资源来或按照您学校的资料教您，创造模拟试题，提供所有的问题例子，以保证您在真实考试中取得的通过率是85%以上。如果您有即将到来的每周、季考、期中或期末考试，我们都能帮助您！

•最快12小时交付

•200+ 英语母语导师

•70分以下全额退款

## 数学代写|图像处理代写Digital image processing代考|Image Processing and Computer Graphics

For some time now, image processing and computer graphics have been treated as two different areas. Knowledge in both areas has increased considerably and more complex problems can now be treated. Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes, while image processing is trying to reconstruct one from an image actually taken with a camera. In this sense, image processing performs the inverse procedure to that of computer graphics. In computer graphics we start with knowledge of the shape and features of an object $-$ at the bottom of Fig. $1.13$ – and work upwards until we get a two-dimensional image. To handle image processing or computer graphics, we basically have to work from the same knowlhow a three-dimensional scene is projected onto an image plane, etc.
There are still quite a few differences between an image processing and a graphics workstation. But we can envisage that, when the similarities and interrelations between computer graphics and image processing are better understood and the proper hardware is developed, we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks. The advent of multimedia, i. e., the integration of text, images, sound, and movies, will further accelerate the unification of computer graphics and image processing. The term “visual computing” has been coined in this context [58]

## 数学代写|图像处理代写Digital image processing代考|Cross-disciplinary Nature of Image Processing

By its very nature, the science of image processing is cross-disciplinary in several aspects. First, image processing incorporates concepts from various sciences. Before we can process an image, we need to know how the digital signal is related to the features of the imaged objects. This includes various physical processes from the interaction of radiation with matter to the geometry and radiometry of imaging. An imaging sensor converts the incident irradiance in one or the other way into an electric signal. Next, this signal is converted into digital numbers and processed by a digital computer to extract the relevant data. In this chain of processes (see also Fig. 1.13) many areas from physics, computer science and mathematics are involved including among others, optics, solid state physics, chip design, computer architecture, algebra, analysis, statistics, algorithm theory, graph theory, system theory, and numerical mathematics. From an engineering point of view, contributions from optical engineering, electrical engineering, photonics, and software engineering are required.

Image processing has a partial overlap with other disciplines. Image processing tasks can partly be regarded as a measuring problem, which is part of the science of metrology. Likewise, pattern recognition tasks are incorporated in image processing in a similar way as in speech processing. Other disciplines with similar connections to image processing are the areas of neural networks, artificial intelligence, and visual perception. Common to these areas is their strong link to biological sciences.

When we speak of computer vision, we mean a computer system that performs the same task as a biological vision system to “discover from images what is present in the world, and where it is” [120]. In contrast, the term machine vision is used for a system that performs a vision task such as checking the sizes and completeness of parts in a manufacturing environment. For many years, a vision system has been regarded just as a passive observer. As with biological vision systems, a computer vision system can also actively explore its surroundings by, e.g., moving around and adjusting its angle of view. This, we call active vision.

There are numerous special disciplines that for historical reasons developed partly independently of the main stream in the past. One of the most prominent disciplines is photogrammetry (measurements from photographs; main applications: mapmaking and surveying). Other areas are remote sensing using aerial and satellite images, astronomy, and medical imaging.

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。