# 计算机代写|计算机视觉代写Computer Vision代考|CS763

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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
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## 计算机代写|计算机视觉代写Computer Vision代考|Template Matching

The region-based method needs to consider the nature of the neighborhood of the point, and the neighborhood is often determined with the help of templates (also called mask, sub-images, or windows). When a point in the left image of a given binocular image pair needs to be searched for a matching point in the corresponding right image, the neighborhood centered on the point in the left image can be extracted as a mask, and the mask can be translated on the right image, and calculate the correlation with each position, to determine whether it matches according to the correlation value. If it matches, it is considered that the center point of the matching position in the right image and that point in the left image form a corresponding point pair. Here, the place of maximum correlation value can be selected as the matching position, or a threshold value can be given first, and the points satisfying the correlation value greater than the threshold value can be extracted first and then selected according to some other factors.

The generally used matching method is called template matching, and its essence is to use a mask (smaller image) to match a part (sub-image) of a larger image. The result of the matching is to determine whether there is a small image in the large image, and if so, the position of the small image in the large image is further determined. In template matching, the template is often square, but it can also be rectangular or other shapes. Now consider finding the matching position of a template image $w(x, y)$ of size $J \times K$ and a large image $f(x, y)$ of $M \times N$; set $J \leq M$ and $K \leq N$. In the simplest case, the correlation function between $f(x, y)$ and $w(x, y)$ can be written as
$$c(s, t)=\sum_{\mathrm{r}} \sum_y f(x, y) w(x-s, y-t)$$
where $s=0,1,2, \ldots, M-1 ; t=0,1,2, \ldots, N-1$.
The summation in Eq. $(6.1)$ is performed on the image region where $f(x, y)$ and $w(x, y)$ overlap. Figure $6.2$ shows a schematic diagram of related calculations,

## 计算机代写|计算机视觉代写Computer Vision代考|Stereo Matching

Using the principle of template matching, the similarity of regional gray levels can be used to search for the corresponding points of two images. Specifically, in the stereo image pair, first select a window centered on a certain pixel in the left image, construct a template based on the grayscale distribution in the window, and then use the template to search in the right image to find the most matching window position,

and then the pixel in the center of the matching window corresponds to the pixel to be matched in the left image.

In the above search process, if there is no prior knowledge or any restriction on the position of the template in the right image, the search range may cover the entire right image. It is time-consuming to search in this way for each pixel in the left image. In order to reduce the search range, it is better to consider using some constraints, such as the following three constraints.

1. Compatibility constraints. Compatibility constraint means that black dots can only match black dots. More generally speaking, only the features of the same type of physical properties in the two images can be matched. It is also called photometric compatibility constraint.
2. Uniqueness constraint. The uniqueness constraint means that a single black point in one image can only be matched with a single black point in another image.
3. Continuity constraints. The continuous constraint means that the parallax change near the matching point is smooth (gradual) in most points except the occluded region or the discontinuous region in the entire image, which is also called the disparity smoothness constraint.

When discussing stereo matching, in addition to the above three constraints, you can also consider the epipolar constraints introduced below and the sequential constraints introduced in Sect. 6.3.

# 计算机视觉代考

## 计算机代写|计算机视觉代写Computer Vision代考|Template Matching

$$c(s, t)=\sum_{\mathrm{r}} \sum_y f(x, y) w(x-s, y-t)$$

$s=0,1,2, \ldots, M-1 ; t=0,1,2, \ldots, N-1$.

## 计算机代写|计算机视觉代写Computer Vision代考|Stereo Matching

1. 兼容性约束。兼容性约束是指黑点只能匹配黑点。更一般地说，只有两幅图像中物理性质相同类型的特征才能匹配。它也被称为光度兼容性约束。
2. 唯一性约束。唯一性约束意味着一幅图像中的单个黑点只能与另一幅图像中的单个黑点匹配。
3. 连续性约束。连续约束是指匹配点附近的视差变化在整幅图像中除遮挡区域或不连续区域外的大部分点是平滑的（渐变的），也称为视差平滑约束。

## 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。

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