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# 数学代写|图像处理代写Digital image processing代考|COMP4840 Classification

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## 数学代写|图像处理代写Digital image processing代考|Classification

Another important task is the classification of objects observed in images. The classical example of classification is the recognition of characters (optical character recognition or short $O C R$ ). Figure $1.10$ a shows a typical industrial OCR application, the recognition of a label on an integrated circuit. Object classification includes also the recognition of different possible positioning of objects for correct handling by a robot. In Fig. 1.10b, connectors are placed in random orientation on a conveyor belt. For proper pick up and handling, whether the front or rear side of the connector is seen must also be detected.

The classification of defects is another important application. Figure $1.11$ shows a number of typical errors in the inspection of integrated circuits: an incorrectly centered surface mounted resistor (Fig. 1.11a), and broken or missing bond connections (Fig. 1.11b-f).

The application of classification is not restricted to industrial tasks. Figure $1.12$ shows some of the most distant galaxies ever imaged by the Hubble telescope. The galaxies have to be separated into different classes due to their shape and color and have to be distinguished from other objects, e.g., stars.

## 数学代写|图像处理代写Digital image processing代考|Hierarchy of Image Processing Operations

Image processing is not a one-step process. We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene. In this way a hierarchical processing scheme is built up as sketched in Fig. 1.13. The figure gives an overview of the different phases of image processing, together with a summary outline of this book.

Image processing begins with the capture of an image with a suitable, not necessarily optical, acquisition system. In a technical or scientific application, we may choose to select an appropriate imaging system. Furthermore, we can set up the illumination system, choose the best wavelength range, and select other options to capture the object feature of interest in the best way in an image (Chapter 6). 2-D and 3-D image formation are discussed in Chapters 7 and 8 , respectively. Once the image is sensed, it must be brought into a form that can be treated with Chapter $9 .$

The first steps of digital processing may include a number of different operations and are known as image preprocessing. If the sensor has nonlinear characteristics, these need to be corrected. Likewise, brightness and contrast of the image may require improvement. Commonly, too, $\mathrm{co}^{-}$ ordinate transformations are needed to restore geometrical distortions introduced during image formation. Radiometric and geometric corrections are elementary pixel processing operations that are discussed in Chapter $10 .$

A whole chain of processing steps is necessary to analyze and identify objects. First, adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background. Essentially, from an image (or several images), one or more feature images are extracted. The basic tools for this task are averaging (Chapter 11), edge detection (Chapter 12), the analysis of simple neighborhoods (Chapter 13) and complex patterns known in image processing as texture (Chapter 15). An important feature of an object is also its motion. Techniques to detect and determine motion are discussed in Chapter 14 .

Then the object has to be separated from the background. This means that regions of constant features and discontinuities must be identified by segmentation (Chapter 16). This can be an easy task if an object is well distinguished from the background by some local features. This is, however, not often the case. Then more sophisticated segmentation techniques are required (Chapter 17). These techniques use various optimization strategies to minimize the deviation between the image data and a given model function incorporating the knowledge about the objects in the image.

## 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。