机器视觉代写|图像处理作业代写Image Processing代考|CHAIN CODE

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

机器视觉代写|图像处理作业代写Image Processing代考|CHAIN CODE

Chain codes are a notation for recording the list of boundary pixels of an object. The chain code uses a logically connected sequence of straight-line segments with specified length and direction to represent the boundary [45]. A chain code can be created by tracking a boundary in some direction, say clockwise, and assigning a direction to the segments connecting every pair of pixels. The direction of each segment is coded by using a 4- or 8 -connected numbering scheme, as shown in Figure 2.18. An example of the representations of an object boundary by using 4 – and 8 directional chain codes are shown in Figure 2.19.

Figure $2.18$ Numbering scheme of the chain code.
Taking an 8 -connected numbering scheme, for example, each code indicates the change of angular direction (in multiples of $45^{\circ}$ ) from one boundary pixel to the next. The even codes $0,2,4$, and 6 correspond to horizontal and vertical directions, while the odd codes $1,3,5$, and 7 correspond to the diagonal directions. The boundary has changed direction when a change occurs between two consecutive chain codes, and the change in the code direction usually indicates a corner on the boundary. By using the chain code, a complete description of an object boundary can be represented by the coordinates of the starting point together with the list of chain codes leading to subsequent boundary pixels, as shown in Figure $2.20$. This representation of a list of boundary pixels becomes more succinct than using all boundary pixels’ coordinates.
However, the chain code depends on the starting point, and different starting points result in different chain codes for the same boundary. To address this, the chain code for a boundary can be normalized with respect to the starting point by treating it as a circular or periodic sequence of direction numbers, and redefining the starting point such that the resulting sequence of numbers is of minimum magnitude.

机器视觉代写|图像处理作业代写Image Processing代考|IMAGE INTERPOLATION

An image gives the intensity values at the integral lattice locations, that is, the coordinates of each pixel are both integers. Image interpolation is the process of using known pixel intensity values to estimate the values at arbitrary locations other than those defined exactly by the integral lattice locations.

Image interpolation is a fundamental operation in image processing and has been widely used in image zooming, rotating, geometric calibration, etc. For example, as seen in Figure 2.22, suppose the input image coordinates $(x, y)$ are assigned to another pair of image coordinates $(\eta, \xi)$ by some coordinate transformation T:
$$(\eta, \xi)=\mathrm{T}{(x, y)}$$
Then the intensity values of the input image also have to be assigned to the corresponding locations of the transformed image. However, with the coordinate transform $\mathrm{T}$, some output pixels with coordinates calculated by Equation $2.32$ may be located between the integer-valued grid points in the $x y$-plane. Thus, the image interpolation techniques are applied to determine the intensity values at those in-between locations. Note also that two or more pixels in the input image can be mapped into the same pixel in the output image by the coordinate transform, in which case the image interpolation techniques can be used to combine multiple input pixel values into a common output pixel value.

机器视觉代写|图像处理作业代写Image Processing代考|NEAREST NEIGHBOR INTERPOLATION

The nearest neighbor interpolation, also called zero-order interpolation, assigns to each output pixel the intensity value of its nearest neighbor in the input image. To perform nearest neighbor interpolation method, the coordinates of every pixel in the output image, denoted as $(m, n)$, are first mapped into the input image by:
$$(u, v)=\mathrm{T}^{-1}{(m, n)}$$
where $(u, v)$ becomes the corresponding coordinates in the input image. Then the intensity value of the pixel located at $(m, n)$ in the output image is set as the value of the pixel that has the shortest distance to $(u, v)$ in the input image. This process is illustrated by Figure $2.23$.The nearest neighbor interpolation method is computationally very simple and fast. However, this method only uses the value of the pixel that is closest to the interpolated location, without taking account of the influence of other neighboring pixels. As a result, this method may produce severe mosaic and saw-tooth effect.

(这,X)=吨(X,是)

(在,在)=吨−1(米,n)

有限元方法代写

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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。