# 线性代数网课代修|机器学习代写 machine learning代考|ACDL2022

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

## 线性代数作业代写linear algebra代考|OtSU’s ThRESHOLDING

Thresholding is the method of removing pixels from the foreground to the background. There are several methods to hit the limit and the Otsu process, introduced by Nobuyuki Otsu, is one of them. The Otsu method was a technique of comparing the values of a threshold where the difference in weight between the background and foreground pixels is the smallest. The fundamental concept should be to get to know all feasible threshold values and calculate the distribution of the foreground and background pixels. The minimum spread is then sought. The formula for determining the variance in underclass at a certain threshold $t$ is defined by:
$\sigma^{2}(t)=\omega_{b_{g}}(t) \sigma_{b_{g}}^{2}(t)+\omega_{f g}(t) \sigma_{f g}^{2}(t) \quad$ Equation 3.3: $2 D$ Otsu thresholding
This algorithm [Li et al., 2018] seeks the threshold to minimize the internal variance specified by a weighted total (background and foreground) of the two categories of variances. The gray colors typically vary from $0-255$ ( $0-1$ for floats). Thus, if this threshold is 100 , all pixels below 100 will be the background, and all pixels above or equal to 100 will be the foreground of that same image.

Where $\omega_{b g}(t)$ and $\omega_{f g}(t)$ describes the probability of pixel number by threshold $\mathrm{t}$ and $\sigma^{2}$ is the variance of the pixel value. $P_{a l l}=$ total count of pixels in an image, $P_{b g}(t)=$ the count of background pixels at threshold t, $P_{f g}(t)=$ the count of foreground pixels at threshold t. Hence, the weights are then indicated as
$$\omega_{b g}(t)=\frac{P_{b g}(t)}{P_{a l l}}, \omega_{f g}(t)=\frac{P_{f g}(t)}{P_{a l l}}$$

## 线性代数作业代写linear algebra代考|FEATURE EXTRACTION

The extraction of features is an important step for image processing. This process specifies the appropriate processing information. Conformity or irregularity may also be observed in the lung. For tumor diagnosis and staging, these isolated properties have been used. Area, Major Axis Length, Minor Axis Length, Eccentricity, Convex Area, Filled Area, Perimeter, Solidity, Extent, Mean Intensity, Actual Area, Actual Perimeter, Actual Major Axis Length, and Compactness have become the separate notable features in this article. The purpose would be to use the fewest steps available to accurately classify an object to be categorized unequivocally. The accuracy of its main image, and how the images are pre-processed relies on the efficiency of some shape calculation. Erosion of objects, including small holes, and noises can lead to poor measuring results and inevitably misclassified outcomes. Shape information is just what exists after an object has been removed with its position, inclination, and size characteristics [Echegaray et al., 2016]. Shape features could be classified into the boundary and region features, which are described below [Johora et al., 2018].

## 线性代数作业代写linear algebra代考|OtSU’s ThRESHOLDING

$\sigma^{2}(t)=\omega_{b_{g}}(t) \sigma_{b_{g}}^{2}(t)+\omega_{f g}(t) \sigma_{f g}^{2}(t) \quad$ 公式 3.3：2DOtsu thresholding

$$\omega_{b g}(t)=\frac{P_{b g}(t)}{P_{a l l}}, \omega_{f g}(t)=\frac{P_{f g}(t)}{P_{a l l}}$$

# 计量经济学代写

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions