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## 计算机代写|计算机视觉代写Computer Vision代考|The Rudin-Osher-Fatemi (ROF) Model

There exist implementations of regularization techniques being different from Tikhonov’s approach. More specifically, they differ in the calculation of the regularization term $E_{\mathrm{s}}$. For example, if we take the integral of the absolute gradients instead of the squared magnitudes, we talk about total variation regularization, which was first used in an image processing context by Rudin et al. [22] for noise suppression. When we use total variation as a regularizer, we make use of the observation that noise introduces additional gradient strength, too. In contrast to Tikhonov regularization, however, total variation takes the absolute values of the gradient strength as regularization term, which is $|\nabla \hat{R}(x, y)|=\sqrt{(\partial R \hat{R} / \partial x)^2+(\partial \hat{R} / \partial y)^2}$. Consequently, the energy functional of Rudin et al. can be written as

$$E_{T V}=\frac{1}{2 \lambda} \cdot \iint(\hat{R}(x, y)-I(x, y))^2 \mathrm{~d} x \mathrm{~d} y+\iint|\nabla \hat{R}(x, y)| \mathrm{d} x \mathrm{~d} y$$
According to first letters of the names of the authors of [22], Rudin, Osher, and Fatemi, this energy functional is also known as the ROF model in literature.

The smoothness term differs from Tikhonov regularization, where the L2 norm of the gradient strength is used. A disadvantage of the L2 norm is that it tends to oversmooth the reconstructed image because it penalizes strong gradients too much. However, sharp discontinuities producing strong gradients actually do occur in real images, typically at the boundary between two objects or object and background. In contrast to the L2 norm, the absolute gradient strength (or L1 norm of the gradient strength) has the desirable property that it has no bias in favor of smooth edges. The shift from L2 to L1 norm seems only a slight modification, but in practice it turns out that the quality of the results can be improved considerably, because the bias to oversmoothed reconstructions is removed efficiently.

## 计算机代写|计算机视觉代写Computer Vision代考|Numerical Solution of the ROF Model

A technique for solving (4.18) numerically, which leads to a quite simple update procedure, was suggested by Chambolle [3]. The derivation is rather complicated;

therefore, only an outline will be given here. The interested reader is referred to $[3,4,20]$ for details.

In order to obtain a solution, Chambolle transforms the original problem into a so-called primal-dual formulation. The primal-dual formulation of the problem involves the usage of a 2-dimensional vector field $\mathbf{p}(x, y)=\left[p_1(x, y), p_2(x, y)\right]$. The vector field $\mathbf{p}$ is introduced as an auxiliary variable (also termed dual variable) and helps to convert the regularization term into a differentiable expression. With the help of $\mathbf{p}$, the absolute value $|\mathbf{v}|$ of a 2-dimensional vector $\mathbf{v}$ can be rewritten as
$$|\mathbf{v}|=\sup _{|\mathbf{p}| \leq 1}\langle\mathbf{v}, \mathbf{p}\rangle$$
where $\langle\cdot\rangle$ denotes the dot product and can be written as $\langle\mathbf{v}, \mathbf{p}\rangle=|\mathbf{v}| \cdot|\mathbf{p}| \cdot \cos \theta$, i.e., the product of the lengths of the two vectors $\mathbf{v}$ and $\mathbf{p}$ with the angle $\theta$ in between these two vectors. If $|\mathbf{p}|=1$ and, furthermore, $\mathbf{v}$ and $\mathbf{p}$ point in the same direction (i.e., $\theta=0),\langle\mathbf{v}, \mathbf{p}\rangle$ exactly equals $|\mathbf{v}|$. Therefore, $|\mathbf{v}|$ is the supremum of the dot product for all vectors $\mathbf{p}$ which are constrained to lie within the unit circle, i.e., $|\mathbf{p}| \leq 1$.

## 计算机代写|计算机视觉代写ComputerVision代考|The Rudin-OsherFatemi (ROF) Model

$|\nabla \hat{R}(x, y)|=\sqrt{(\partial R \hat{R} / \partial x)^2+(\partial \hat{R} / \partial y)^2}$. 因此，Rudin 等人的能量函数。可以写成
$$E_{T V}=\frac{1}{2 \lambda} \cdot \iint(\hat{R}(x, y)-I(x, y))^2 \mathrm{~d} x \mathrm{~d} y+\iint|\nabla \hat{R}(x, y)| \mathrm{d} x \mathrm{~d} y$$

## 计算机代写|计算机视觉代写ComputerVision代考|Numerical Solution of the ROF Model

Chambolle [3] 提出了一种数值求解 (4.18) 的技术，这导致了一个非常简单的更新过程。推导比较复杂；

$$|\mathbf{v}|=\sup _{|\mathbf{p}| \leq 1}\langle\mathbf{v}, \mathbf{p}\rangle$$

## 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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## 计算机代写|计算机视觉代写Computer Vision代考|The Fundamental Problem.

The central issue in pattern recognition is the relation between within-class variability and between-class variability. These are determined by the degrees of freedom spanned by the pattern classes. Ideally the within-class variability should be small and the between-class variability large, so that the classes are well separated. In the case of encoding faces for identity, one would like different faces to generate face codes that are as different from each other as possible, while different images of the same face should ideally generate similar codes across conditions. Several recent investigations of how well this goal is achieved have studied the invariances in face coding schemes under changes in illumination, perspective angle or pose, and expression. Their results have tended to show that there is greater variability in the code for a given face across these three types of changes, than there is among the codes for different faces when these three factors are kept constant. Since reports documenting performance of particular face recognition algorithms have often been based upon trials in which these factors (pose, illumination, and expression) were held artificially constant, the performance statistics in real-world settings have been very disappointing by contrast, with error rates approaching $50 \%$.

The array of images above shows how dramatic are the effects of even only a change in illumination direction. Facial expression remains exactly the same. Going across the columns from left to right, the illumination changes from frontal to side; and going down the rows, it changes in elevation. If you compare the 3 images in the last column on the right, it seems almost inconceivable that any means could be found to represent these as images of the same person.

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

Paradoxically, face detection is a harder problem than face recognition, and the performance rates of algorithms are poorer. (This seems paradoxical since detection must precede recognition; but recognition performance is measured only with images already containing faces.) Approaches to face detection still use generic templates, spanning multiple scales (for faces of different distances, hence sizes) and poses. One of the most powerful features aiding face detection is the rather special hue composition of human skin, which is matched by few other types of surfaces. (Racial differences correspond only to varations in saturation, due to differential melanin density, but not to differences in hue.) If skin tone is ignored, most current algorithms for face detection will find lots of faces when “aimed” merely at a nice Persian rug…

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

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## 计算机代写|计算机视觉代写Computer Vision代考|Bayesian inference in vision. Classifiers; probabilistic methods

It is virtually impossible to perform most computer vision tasks in a purely “bottom-up” fashion. Consider the following images, and how impoverished are the data which must support the task of object recognition!

An important “AI” perspective on vision is that vision is knowledge-driven. In this view, all of the front-end image processing is merely a distraction, if not an irrelevancy. What is really needed for vision is not a lot of theorems involving the 2D Fourier transform of the Laplacian of a Gaussian filter, but rather a good interface to an expert system that stores and indexes knowledge about such things as Dalmatian hounds and the general way that dogs behave when following a scent…

This section reviews the basic ideas behind Bayesian inference, which is a method fundamental to probability theory, statistics, and machine learning. Its purpose is to provide a means for integrating prior information (such as general knowledge about the sorts of things that populate the world, their properties and relationships, the metaphysics of objects, etc…) with empirical information gathered from incoming image data. This principle is expressed in the form of a basic rule for relating conditional probabilities in which the “antecedent” and “consequent” are interchanged. The value of this method for computer vision is that it provides a framework for continually updating one’s theory of what one is looking at, by integrating continuously incoming evidence with the best available inference or interpretation so far.

## 计算机代写|计算机视觉代写Computer Vision代考|Decisions under uncertainty

Most real-world tasks (whose solution requires intelligence) involve degrees of uncertainty. Decision-making under uncertainty is especially characteristic in computer vision. The sources of uncertainty may include:

the nature of the data or signals available

the inherent problem of classifying or recognizing them

the unpredictability of the future

the fact that objects and events have probabilities

the uncertainty of causation

the fact that associative knowledge is only probabilistic

the inherent incompleteness or imperfection of processing

possible undecidability of a problem, given all available data

the “ill-posed” nature of many tasks

inherent trade-offs such as speed versus accuracy
But despite these realities, decisions are required. The framework to adopt is that, in a sense, the world consists of probabilities, and that visual processing really amounts to computing probabilities and assigning them.

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

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## 计算机代写|计算机视觉代写Computer Vision代考|SPATIO-TEMPORAL SPECTRAL MODELS

It is possible to detect and measure image motion purely by Fourier means. This approach exploits the fact that motion creates a covariance in the spatial and temporal spectra of the time-varying image $I(x, y, t)$, whose three-dimensional (spatio-temporal) Fourier transform is defined:
$$F\left(\omega_x, \omega_y, \omega_t\right)=\int_X \int_Y \int_T I(x, y, t) e^{-i\left(\omega_x x+\omega_y y+\omega_t t\right)} d x d y d t$$

In other words, rigid image motion has a 3D spectral consequence: the local 3D spatio-temporal spectrum, rather than filling up 3-space $\left(\omega_x, \omega_y, \omega_t\right)$, collapses onto a 2D inclined plane which includes the origin. Motion detection then occurs just by filtering the image sequence in space and in time, and observing that tuned spatio-temporal filters whose center frequencies are co-planar in this 3-space are activated together. This is a consequence of the SPECTRAL CO-PLANARITY THEOREM:
Theorem: Translational image motion of velocity $\overrightarrow{\mathbf{v}}$ has a 3D spatiotemporal Fourier spectrum that is non-zero only on an inclined plane through the origin of frequency-space. Spherical coordinates of the unit normal to this spectral plane correspond to the speed and direction of motion.
Let $I(x, y, t)$ be a continuous image in space and time.
Let $F\left(\omega_x, \omega_y, \omega_t\right)$ be its 3D spatio-temporal Fourier transform:
$$F\left(\omega_x, \omega_y, \omega_t\right)=\int_X \int_Y \int_T I(x, y, t) e^{-i\left(\omega_x x+\omega_y y+\omega_t t\right)} d x d y d t .$$
Let $\overrightarrow{\mathbf{v}}=\left(v_x, v_y\right)$ be the local image velocity.
Uniform motion $\overrightarrow{\mathbf{v}}$ implies that for all time shifts $t_o$,
$$I(x, y, t)=I\left(x-v_x t_o, y-v_y t_o, t-t_o\right) .$$
Taking the 3D spatio-temporal Fourier transform of both sides, and applying the shift theorem, gives
$$F\left(\omega_x, \omega_y, \omega_t\right)=e^{-i\left(\omega_x v_x t_o+\omega_y v_y t_o+\omega_t t_o\right)} F\left(\omega_x, \omega_y, \omega_t\right)$$

## 计算机代写|计算机视觉代写Computer Vision代考|Lambertian and specular surfaces. Reflectance maps

How can we infer information about the surface reflectance properties of objects from raw measurements of image brightness? This is a more recondite matter than it might first appear, because of the many complex factors which determine how (and where) objects scatter light.
Some definitions of surface type and properties:

• Surface albedo refers to the fraction of the illuminant that is re-emitted from the surface in all directions, in total. Thus, albedo corresponds moreor-less to “greyness.”

The amount of light reflected is the product of two factors: the albedo of the surface, times a geometric factor that depends on angle.

• A Lambertian surface is “pure matte.” It reflects light equally well in all directions.

Examples of Lambertian surfaces include snow, non-glossy paper, pingpong balls, magnesium oxide, projection screens, $\ldots$

A Lambertian surface looks equally bright from all directions; the amount of light reflected depends only on the angle of incidence.

## 计算机代写|计算机视觉代写Computer Vision代考|SPATIO-TEMPORAL SPECTRAL MODELS

$$F\left(\omega_x, \omega_y, \omega_t\right)=\int_X \int_Y \int_T I(x, y, t) e^{-i\left(\omega_x x+\omega_y y+\omega_t t\right)} d x d y d t$$

$$F\left(\omega_x, \omega_y, \omega_t\right)=\int_X \int_Y \int_T I(x, y, t) e^{-i\left(\omega_x x+\omega_y y+\omega_t t\right)} d x d y d t .$$

$$I(x, y, t)=I\left(x-v_x t_o, y-v_y t_o, t-t_o\right) .$$

$$F\left(\omega_x, \omega_y, \omega_t\right)=e^{-i\left(\omega_x v_x t_o+\omega_y v_y f_o+\omega_t t_o\right)} F\left(\omega_x, \omega_y, \omega_t\right)$$

## 计算机代写|计算机视兴代奇Computer Vision代考|Lambertian and specular surfaces. Reflectance maps

• 表面反照率是指从表面向所有方向重新发射的光源总量的分数。因此，反照率或多或少对应于“灰度”。
反射的光量是两个因嗉的乘积：表面的反照率乘以取决于角度的几何因嗉。
• 朗伯表面是”纯哑光”。它在所有方向上都能㑡好地反射光线。
朗伯表面的例子包括雪、无光泽纸、乒乓球、華化镁、投影屏幕、 $\ldots$
朗伯表面从各个方向看起来都同样明亮; 反射的光量仅取决于入射角。

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

## 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！

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## 计算机代写|计算机视觉代写Computer Vision代考|Generalization of Wavelet Logons to 2D for Image Analysis

An effective method for extracting, representing, and analyzing image structure is the computation of the 2D Gabor wavelet coefficients for the image. This family of 2D filters were originally proposed as a framework for understanding the orientation-selective and spatial-frequency-selective receptive field properties of neurons in the brain’s visual cortex, as well as being useful operators for practical image analysis problems. These 2D filters are conjointly optimal in extracting the maximum possible information both about the orientation and modulation of image structure (“what”), simultaneously with information about 2D position (“where”). The 2D Gabor filter family uniquely achieves the theoretical lower bound on joint uncertainty over these four variables in the Uncertainty Principle when it is suitably generalized.
These properties are particularly useful for texture analysis because of the 2D spectral specificity of texture as well as its variation with 2D spatial position. These wavelets are also used for motion detection, stereoscopic vision, and many sorts of visual pattern recognition such as face recognition. A large and growing literature now exists on the efficient use of this non-orthogonal expansion basis and its applications.

## 计算机代写|计算机视觉代写Computer Vision代考|Unification of Domains

Until now we have viewed “the image domain” and “the Fourier domain” as very different domains of visual representation. But now we can see that the “Gabor domain” of representation actually embraces and unifies both of these other two domains. How?

In the wavelet equations above, the scale constant $\alpha$ (and $\beta$ in the 2D case) actually builds a continuous bridge between the two domains. If the scale constant is set very large, then the Gaussian term becomes just 1 and so the expansion basis reduces to the familiar Fourier basis. If instead the scale constant is made very small, then the Gaussian term shrinks to a discrete delta function (1 only at the location $x=x_0$, and 0 elsewhere), so the expansion basis implements pure space-domain sampling: a pixel-by-pixel image domain representation. This allows us to build a continuous deformation between the two domains when representing, analyzing, and recognizing image structure, merely by changing a single scaling parameter in this remarkable, unifying, expansion basis.

## 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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## 计算机代写|计算机视觉代写Computer Vision代考|Neurobiological Visual Principles =⇒ Machine Vision

The structure of biological nervous tissue and the nature of events that occur in it are utterly different from those found in computing hardware. Yet since the only general-purpose visual systems that exist today are the biological ones, let us learn what we can from “wetware.” Neurons are sluggish but richly interconnected devices having both analogue and discrete aspects. Fundamentally they consist of an enclosing membrane that can separate electrical charge (hence there is generally a voltage difference between inside and out). The membrane is a lipid bilayer that has a capacitance of about $10,000 \mu \mathrm{Farads} / \mathrm{cm}^2$, and it also has pores that are differentially selective to different ions (mainly $\mathrm{Na}^{+}, \mathrm{K}^{+}$, and $\mathrm{Cl}^{-}$). These ion species enter or leave a neuron through protein pores studding its lipid membrane, acting as conductances (hence as resistors). The resistors for $\mathrm{Na}^{+}$and $\mathrm{K}^{+}$have the further crucial property that their resistance is not constant, but voltage-dependent. Hence as more positive ions $\left(\mathrm{Na}^{+}\right)$flow into the neuron, the voltage becomes more positive on the inside, and this further reduces the membrane’s resistance to $\mathrm{Na}^{+}$, allowing still more to enter. This catastrophic breakdown in resistance to $\mathrm{Na}^{+}$constitutes a nerve impulse. Within about a msec a slower but opposite effect involving $\mathrm{K}^{+}$takes over, eventually restoring the original voltage. Following a short refractory period of about $2 \mathrm{msec}$ during which ions are actively pumped back in opposite directions to reach their original electro-osmotic equilibrium concentrations, the neuron is ready for action again. Meanwhile, the impulse thus generated propagates down the axon, at a speed of about $100 \mathrm{~m} / \mathrm{sec}$. This signalling pulse can be described as discrete, but the antecedent summations of current flows into the neuron (from various influences by other neurons) which caused the catastrophic impulse are fundamentally analogue events.

## 计算机代写|计算机视觉代写Computer Vision代考|Receptive field structure in the retina

The spatial structuring of excitatory and inhibitory influences amongst neurons in the retina gives them their properties as image operators. Similarly for the temporal structure of their interactions. In both space and time, retinal neurons can thus be described as filters; and to the extent that they act as linear devices (having the properties of proportionality and superposition of responses to components of stimuli), their behaviour can be fully understood (and even predicted for arbitrary images) through Fourier analysis and the other tools of linear systems analysis. An important aspect of retinal receptive fields – as distinct from those found in most neurons of the visual cortex – is that their spatial structure is isotropic, or circularly symmetric, rather than oriented.

Photoreceptors respond to light by hyperpolarising (the voltage across the cell membrane becomes more negative inside, for vertebrates; the opposite is true for invertebrates). Their “receptive field” is just their own cross-section for absorbing light, a small disk about $3 \mu$ in diameter on the human retina, about a minute of visual arc.

Horizontal cells pool together the responses from large numbers of photoreceptors within a local area. With these “surround” signals, they inhibit bipolar cells (hence the name).

Bipolar cells are the first to have a “centre-surround” receptive field structure: their response to light in a central disk is opposite from their response to light in the local surrounding area. Field boundaries are circular and roughly concentric (i.e. annular).

Amacrine cells are “on-off” in temporal, as opposed to spatial, terms.

Ganglion cells combine these spatial and temporal response properties and thus serve as integro-differential image operators with specific scales and time constants. Moreover they convert their responses to impulses in a spike frequency code, traveling down their axons which are the fibres of the optic nerve to the thalamus and thence on to the primary visual cortex in the brain.

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

## 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！

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

A special case of objective functions which can be found quite often in practice is characterized by the fact that the objective function can be written as a sum of $N$ elements:

$$f(\mathbf{x})=\sum_{n=1}^N f_n(\mathbf{x})$$
This splitting of $f(\mathbf{x})$ into $N$ summands can be observed, e.g., for MRF-based energy functions. This structure is similar to $(2.21)$, but in contrast to $(2.21)$, the summands are not restricted to be square terms.

If we want to apply gradient-based optimization, even simple methods like steepest descent would involve a calculation of $\partial f_n(\mathbf{x}) / \partial \mathbf{x}$ for all $N$ components in every iteration, which could be infeasible as far as time demand is concerned.

An alternative approach is to perform an iterative optimization which considers only one of the summands of (2.44) at each iteration. Clearly, now more iterations are necessary, but at the same time, each iteration can be performed much faster, which should overcompensate for the increase in the number of iterations. The proceeding suggested here comprises the following steps:

1. Pick one $f_n(\mathbf{x})$ at random.
2. Try to reduce $f(\mathbf{x})$ by optimizing $f_n(\mathbf{x})$ with steepest descent, i.e., calculate $\partial f_n(\mathbf{x}) / \partial \mathbf{x}$ and perform a one-dimensional optimization in this direction.

## 计算机代写|计算机视觉代写Computer Vision代考|Example: Classiﬁﬁed Training for Object Class Recognition

Vijnhoven et al. [15] showed how stochastic gradient descent optimization can be successfully applied to the task of training a decision function for object detection. They considered the application of detecting instances of a certain object category, e.g., “cars” or “pedestrians,” in an image, which can be solved by the approach suggested in [4].

Dalal et al. derive a feature vector d (a so-called descriptor) which they call “Histograms of Oriented Gradients” (HOG) from a subregion of the image and, based on d, run a classifier which decides whether an instance of the object category to be searched is present at this particular position or not. The classifier has a binary output: $-1$ for “object not present” and 1 for “object present.” In order to scan the image, they propose a so-called sliding window approach, where the region for calculating the descriptor is shifted pixel by pixel over the entire image, with a subsequent classification at every position. Finally, they obtain a position vector where each element reveals the position of a detected instance of the searched object category.

The classifier has to be trained prior to recognition in an off-line teaching phase with the help of example images. A Support Vector Machine (SVM) for classification is used in [4], whereas the authors of [15] suggest to employ SGD in the classifier training step. Through the usage of SGD, they showed to reduce training times by a factor of $100-1,000$ with similar recognition performance.

Before we describe in detail how SGD is utilized in training, let’s first take a closer look at some different aspects of the proceeding of [4] (HOG descriptor, sliding window, and classifier design) in order to get a better understanding of the method.

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

$$f(\mathbf{x})=\sum_{n=1}^N f_n(\mathbf{x})$$

## 计算机代写|计算机视觉代写Computer Vision代考|Example: Classifified Training for Object Class Recognition

Vijnhoven 等人。 [15] 展示了如何将随机梯度下降优化成功应用于训绩目标检则夫策函数的任务。他们考虑了在图像中检则特定 对象类别的实例的应用，例吅“汽车“或”行人”，这可以通过 [4] 中建仪的方法来解快。

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

## avatest™帮您通过考试

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## 计算机代写|计算机视觉代写Computer Vision代考|Newton’s Method

Newton’s method is a classical example of second-order continuous optimization (see, e.g., [2] for a more detailed description). Here, the function $f(\mathbf{x})$ is approximated by a second-order Taylor expansion $T$ at the current solution $\mathbf{x}^k$ :
$$f(\mathbf{x}) \cong T(\delta \mathbf{x})=f\left(\mathbf{x}^k\right)+\nabla f\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}+\frac{1}{2} \delta \mathbf{x}^T \cdot \mathbf{H}\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}$$
with $\delta \mathbf{x}$ being the difference $\mathbf{x}-\mathbf{x}^k$. As long as $\delta \mathbf{x}$ remains sufficiently small, we can be quite sure that the second-order Taylor expansion $T(\delta \mathbf{x})$ is a sufficiently good approximation of $f(\mathbf{x})$.

As $f(\mathbf{x})$ is approximated by a quadratic form, a candidate of its minimum can be found analytically in a single step by setting the derivative of the quadratic form to zero. This yields a linear system of equations which can be solved with standard techniques (see also Sect. 2.1). Because the Taylor expansion is just an approximation of $f(\mathbf{x})$, its minimization at a single position is usually not sufficient for finding the desired solution. Hence, finding a local minimum of $f(\mathbf{x})$ involves an iterative application of the following two steps:

1. Approximate $f(\mathbf{x})$ by a second-order Taylor expansion $T(\delta \mathbf{x})$ (see (2.16)).
2. Calculate the minimizing argument $\delta \mathbf{x}^*$ of this approximation $T(\delta \mathbf{x})$ by setting its first derivative to zero: $\nabla T(\delta \mathbf{x})=\mathbf{0}$

In order for $\delta \mathbf{x}^*$ to be a local minimum of $T(\delta \mathbf{x})$, the following two conditions must hold:

1. $\nabla T\left(\delta \mathbf{x}^*\right)=\mathbf{0}$.
2. $\mathbf{H}\left(\mathbf{x}^k\right)$ is positive, i.e., $\mathbf{d}^T \cdot \mathbf{H}\left(\mathbf{x}^k\right) \cdot \mathbf{d}>0$ for every vector $\mathbf{d}$. This is equivalent to the statement that all eigenvalues of $\mathbf{H}\left(\mathbf{x}^k\right)$ are positive real numbers. This condition corresponds to the fact that for one-dimensional functions, their second derivative has to be positive at a local minimum.

Now let’s see how the two steps of Newton’s method can be implemented in practice. First, a differentiation of $T(\delta \mathbf{x})$ with respect to $\delta \mathbf{x}$ yields:
$$\nabla f(\mathbf{x}) \cong \nabla T(\delta \mathbf{x})=\nabla f\left(\mathbf{x}^k\right)+\mathbf{H}\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}$$

## 计算机代写|计算机视觉代写Computer Vision代考|Gauss-Newton and Levenberg-Marquardt Algorithm

A special case occurs if the objective function $f(\mathbf{x})$ is composed of a sum of squared values:
$$f(\mathbf{x})=\sum_{i=1}^N r_i(\mathbf{x})^2$$

Such a specific structure of the objective can be encountered, e.g., in least squares problems, where the $r_i(\mathbf{x})$ are deviations from the values of a regression function to observed data values (so-called residuals). There are numerous vision applications where we want to calculate some coefficients $\mathbf{x}$ such that the regression function fits “best” to the sensed data in a least squares sense.

If the residuals are linear in $\mathbf{x}$, we can apply the linear regression method already presented in Sect. 2.1. Nonlinear $r_i(\mathbf{x})$, however, are a generalization of this regression problem and need a different proceeding to be solved.

Please bear in mind that in order to obtain a powerful method, we always should utilize knowledge about the specialties of the problem at hand if existent. The Gauss-Newton algorithm (see, e.g., [1]) takes advantage of the special structure of $f(\mathbf{x})$ (i.e., $f(\mathbf{x})$ is composed of a sum of residuals) by approximating the secondorder derivative by first-order information.

To understand this, let’s examine how the derivatives used in Newton’s method can be written for squared residuals. Applying the chain rule, the elements of the gradient $\nabla f(\mathbf{x})$ can be written as
$$\nabla f_j(\mathbf{x})=2 \sum_{i=1}^N r_i(\mathbf{x}) \cdot J_{i j}(\mathbf{x}) \quad \text { with } \quad J_{i j}(\mathbf{x})=\frac{\partial r_i(\mathbf{x})}{\partial x_j}$$
where the $J_{i j}(\mathbf{x})$ are the elements of the so-called Jacobi matrix $\mathbf{J}{\mathbf{r}}(\mathbf{x})$, which pools first-order derivative information of the residuals. With the help of the product rule, the Hessian $\mathbf{H}$ can be derived from $\nabla f(\mathbf{x})$ as follows: \begin{aligned} H{j l}(\mathbf{x}) &=\frac{\nabla f_j(\mathbf{x})}{\partial x_l}=2 \sum_{i=1}^N\left(\frac{\partial r_i(\mathbf{x})}{\partial x_l} \cdot J_{i j}(\mathbf{x})+r_i(\mathbf{x}) \cdot \frac{\partial J_{i j}(\mathbf{x})}{\partial x_l}\right) \ &=2 \sum_{i=1}^N\left(J_{i l}(\mathbf{x}) \cdot J_{i j}(\mathbf{x})+r_i(\mathbf{x}) \cdot \frac{\partial^2 r_i(\mathbf{x})}{\partial x_j \cdot \partial x_l}\right) \end{aligned}

## 计算机代写计算机视觉代写Computer Vision代考|Newton’s Method

$$f(\mathbf{x}) \cong T(\delta \mathbf{x})=f\left(\mathbf{x}^k\right)+\nabla f\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}+\frac{1}{2} \delta \mathbf{x}^T \cdot \mathbf{H}\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}$$

$\nabla T\left(\delta \mathbf{x}^*\right)=\mathbf{0}$.

$\mathbf{H}\left(\mathbf{x}^k\right)$ 为正，即 $\mathbf{d}^T \cdot \mathbf{H}\left(\mathbf{x}^k\right) \cdot \mathbf{d}>0$ 对于每个向量 $\mathrm{d}$. 这等价于声明所有的特征值 $\mathbf{H}\left(\mathbf{x}^k\right)$ 是正实数。这个条件对应于这 样一个事实: 对于一维函数，它们的二阶导数必须在局部最小值处为正。

$$\nabla f(\mathbf{x}) \cong \nabla T(\delta \mathbf{x})=\nabla f\left(\mathbf{x}^k\right)+\mathbf{H}\left(\mathbf{x}^k\right) \cdot \delta \mathbf{x}$$

## 计算机代写|计算机视觉代写Computer Vision代考|Gauss-Newton and Levenberg-Marquardt Algorithm

$$f(\mathbf{x})=\sum_{i=1}^N r_i(\mathbf{x})^2$$

$$\nabla f_j(\mathbf{x})=2 \sum_{i=1}^N r_i(\mathbf{x}) \cdot J_{i j}(\mathbf{x}) \quad \text { with } \quad J_{i j}(\mathbf{x})=\frac{\partial r_i(\mathbf{x})}{\partial x_j}$$

$$H j l(\mathbf{x})=\frac{\nabla f_j(\mathbf{x})}{\partial x_l}=2 \sum_{i=1}^N\left(\frac{\partial r_i(\mathbf{x})}{\partial x_l} \cdot J_{i j}(\mathbf{x})+r_i(\mathbf{x}) \cdot \frac{\partial J_{i j}(\mathbf{x})}{\partial x_l}\right) \quad=2 \sum_{i=1}^N\left(J_{i l}(\mathbf{x}) \cdot J_{i j}(\mathbf{x})+r_i(\mathbf{x}) \cdot \frac{\partial^2 r_i(\mathbf{x})}{\partial x_j \cdot \partial x_l}\right)$$

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

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## 计算机代写|计算机视觉代写Computer Vision代考|Common Optimization Concepts in Computer Vision

Before taking a closer look at the diverse optimization methods, let’s first introduce some concepts which are of relevance to optimization and, additionally, in widespread use in computer vision.

With the help of energy functions, for example, it is possible to evaluate and compare different solutions and thus use this measure to find the optimal solution. The well-known MAP estimator, which finds the best solution by estimating the “most likely” one, given some observed data, can be considered as one form of energy minimization.

Markov Random Fields (MRFs) are a very useful model if the “state” of each pixel (e.g., a label or intensity value) is related to the states of its neighbors, which makes MRFs suitable for restoration (e.g., denoising), segmentation, or stereomatching tasks, just to name a few.

Last but not least, many computer vision tasks rely on establishing correspondences between two entities. Consider, for example, an object which is represented by a set of characteristic points and their relative position. If such an object has to be detected in a query image, a common proceeding is to extract characteristic points for this image as well and, subsequently, try to match them to the model points, i.e., to establish correspondences between model and query image points.

In addition to this brief explanation, the concepts of energy functions, graphs, and Markov Random Fields are described in more detail in the following sections.

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

The concept of so-called energy functions is a widespread approach in computer vision. In order to find the “best” solution, one reasonable way is to quantify how “good” a particular solution is, because such a measure enables us to compare different solutions and select the “best”. Energy functions $E$ are widely used in this context (see, e.g., [6]).

Generally speaking, the “energy” is a measure how plausible a solution is. High energies indicate bad solutions, whereas a low energy signalizes that a particular solution is suitable for explaining some observed data. Some energies are so-called functionals. The term “functional” is used for operators which map a functional relationship to a scalar value (which is the energy here), i.e., take a function as argument (which can, e.g., be discretely represented by a vector of values) and derive a scalar value from this. Functionals are needed in variational optimization, for example.

With the help of such a function, a specific energy can be assigned to each element of the solution space. In this context, optimization amounts to finding the argument which minimizes the function:
$$x^*=\arg \min _{x \in S} E(x)$$
As already mentioned in the previous section, $E$ typically consists of two components:

1. A data-driven or external energy $E_{\text {ext }}$, which measures how “good” a solution explains the observed data. In restoration tasks, for example, $E_{\text {ext }}$ depends on the fidelity of the reconstructed signal $\hat{R}$ to the observed data $I$.
2. An internal energy $E_{\text {int }}$, which exclusively depends on the proposed solution (i.e., is independent on the observed data) and quantifies its plausibility. This is the point where a priori knowledge is considered: based on general considerations, we can consider some solutions to be more likely than others and therefore assign a low internal energy to them. In this context it is often assumed that the solution should be “smooth” in a certain sense. In restoration, for example, the proposed solution should contain large areas with uniform or very smoothly varying intensity, and therefore $E_{\text {int }}$ depends on some norm of the sum of the gradients between adjacent pixels.

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

$$x^*=\arg \min _{x \in S} E(x)$$

1. 数据区动或外部能量 $E_{\mathrm{ext}}$ ，它衡量解决方案解释观察到的数据的”好”程度。例如，在恢夏任务中， $E_{\text {ext }}$ 取决于重建信号的 保真度 $\hat{R}$ 对观财数据 $I$.
2.一种内能 $E_{\text {int }}$ ，它完全取决于建议的解决方穼（即，独立于观䕓到的数据）并量化其合理性。这是考虑先验知识的点: 其 于一般考虑，我们可以认为某些解决方宨比其他解决方客更有可能，因此为它们分配较低的内部能量。在这种情况下，通常 假设解决方案在㭉种意义上应该是“平滑的”。例如，在修孭中，建议的解决方依应该包含具有均匀或非常平滑变化强度的大 区域，因此 $E_{\text {int }}$ 取决于相邻像靑之间的梯度总和的某个范数。

## 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。

Posted on Categories:Computer Vision, 计算机代写, 计算机视觉

## 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！

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## 计算机代写|计算机视觉代写Computer Vision代考|STRUCTURED ANALYSIS OF THE RETINA-STARE

This dataset comprises of 400 retinal pictures, caught utilizing TOPCON TRV-50 fundus camera with extra settings of $35^{\circ} \mathrm{FOV}$ and 8 bits/shading channel at $605 \times 700$ pixels. The normal width of the FOV is $650 \times 700$. Gaze has 20 vessel ground truth pictures utilized for vein division in which 9 are more beneficial while the rest of them have indicated various kinds of retinal maladies. ${ }^{41}$ Two specialists have physically sectioned these pictures where the main master portioned $10.4 \%$ vessel pixel, while the subsequent master sectioned $14.9 \%$ of the more slender vessel. By and large, the division of the main spectator used to figure the exhibition as the ground truth. ${ }^{38}$

## 计算机代写|计算机视觉代写Computer Vision代考|ANNOTATED DATASET FOR VESSEL SEGMENTATION AND CALCULATION OF ARTERIOVENOUS RATIO-AVRDB

AVRDB is a recently created $\mathrm{HR}$ database that will be freely accessible at www.biomisa.org in future for the consideration network. It is having 100 fundus retinal pictures that are caught through TOPCON TRC-NW8 and explained with the assistance of master ophthalmologists from the Armed Forces Institute of Ophthalmology. The vascular system is sorted into an arteriolar and venular design. The 100 pictures are having a measurements of $1504 \times 1000$ comprise retinal courses, veins, AVR, and entire vascular structure for ground certainties. It likewise has an explanation at the picture level for $\mathrm{HR}^{32}$

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

INSPIRE AVR with 40 shading pictures of the vessels and optic circle and an arterial-venous proportion reference standard. The orientation standard is the normal of the appraisal of two specialists utilizing IVAN (a semi-mechanized PC program created by the University of Wisconsin, Madison, WI, USA) on the pictures. ${ }^{20}$

The retinal fundus image databases with the number of images available for $\mathrm{HR}$ classification are mentioned in Table 3.4.

## 计算机代写|计算机视觉代写Computer Vision代考|ANNOTATED DATASET FOR VESSEL SEGMENTATION AND CALCULATION OF ARTERIOVENOUS RATIO-AVRDB

AVRDB 是最近创建的人力资源未来可在 www.biomisa.org 上免费访问的数据库，供考虑网络使用。它有 100 张眼底视网膜图片，这些图片是通过 TOPCON TRC-NW8 捕获的，并在武装部队眼科研究所的眼科医生的协助下进行了解释。血管系统分为小动脉和小静脉设计。这 100 张图片的尺寸为1504×1000包括视网膜路线、静脉、AVR 和整个血管结构，以确保确定性。它在图片级别也有解释人力资源32

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

INSPIRE AVR 带有 40 张血管和视圈的阴影图片以及动静脉比例参考标准。定位标准是两名专家在图片上使用 IVAN（由美国威斯康星大学麦迪逊市麦迪逊市创建的半机械化 PC 程序）评估的标准。20

## 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。