计算机代写|图像处理代写Image Processing代考|COMP345

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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

计算机代写|图像处理代写Image Processing代考|IMAGE BASICS

First, some basic concepts and terminology related to images are reviewed.
1.1.1 Image Representation and Display
Let’s first introduce how to represent and display images.
1.1.1.1 Images and Pixels
The objective world is three-dimensional (3-D) in space, but the image obtained from the objective scene is generally two-dimensional (2-D). An image can be represented by a 2-D array $f(x, y)$, where $x$ and $y$ represent the position of a coordinate point in the 2-D space $X Y$, and $f$ represents the image value of a property $F$ at a certain point $(x, y)$. For example, $f$ in a grayscale image represents a gray value, which often corresponds to the observed brightness of an objective scene. Text images are often binary images, and there are only two values for $f$, corresponding to text and blank space, respectively. The image at the point $(x, y)$ can also have multiple properties at the same time. In this case, it can be represented by a vector $f$. For example, a color image has three values of red, green, and blue at each image point, which can be recorded as $\left[f_r(x, y), f_g(x, y), f_b(x, y)\right]$. It needs to be pointed out that people always use images according to the different properties at different positions in the image.

An image can represent the spatial distribution of radiant energy. This distribution can be a function of five variables $T(x, y, z, t, \lambda)$, where $x, y$, and $z$ are spatial variables,and $t$ represents time variables, $\lambda$ is wavelength (corresponding to the spectral variable). For example, a red object reflects light with a wavelength of $0.57-0.78 \mu \mathrm{m}$ and absorbs almost all energy of other wavelengths; a green object reflects light with a wavelength of $0.48-0.57 \mu \mathrm{m}$; a blue object reflects light with a wavelength of $0.40-0.48 \mu \mathrm{m}$. Ultraviolet (color) objects reflect light with a wavelength of $0.25-0.40 \mu \mathrm{m}$, and infrared (color) objects reflect light with a wavelength of $0.78-1.5 \mu \mathrm{m}$. Together, they cover a wavelength range of $0.25-1.5 \mu \mathrm{m}$. Since the actual image is finite in time and space, $T(x, y, z, t, \lambda)$ is a 5-D finite function.

计算机代写|图像处理代写Image Processing代考|Spatial Resolution and Amplitude Resolution

From the above introduction and discussion of image representation and display, it can be known that the content of a 2-D grayscale image is determined by the number of pixels (the number of rows of the image multiplicd by the number of columns of the image) and by the number of gray levels for each pixel. The former determines the spatial resolution of the image, while the latter determines the amplitude resolution of the image. From the perspective of image acquisition, the acquisition of images is to record the spatial distribution of the light reflection intensity of the scene within a certain field of view. The accuracy in the spatial field of view here corresponds to the spatial resolution of the image, and the accuracy in the intensity range corresponds to the amplitude resolution of the image. The former corresponds to the number of digitized spatial sampling points while the latter corresponds to the quantization levels of the sampling point value (for grayscale images, it refers to gray levels; for depth images, it refers to depth levels). They are all important performance indicators of image acquisition devices.

The spatial resolution and amplitude resolution of the image are determined by sampling and quantization, respectively. Taking a typical CCD camera as an example, the spatial resolution of the image is mainly determined by the size and arrangement of the photoelectric sensing units in the image acquisition matrix in the camera, and the amplitude resolution of the grayscale image is mainly determined by the number of stages in the quantization of the electrical signal intensity. As shown in Figure 1.3, the signal radiated from the photoreceptive unit in the image acquisition matrix is sampled in space and quantized in intensity.

The sampling process can be seen as dividing the image plane into regular grids. The position of each grid is determined by a pair of Cartesian coordinates $(x, y)$, where $x$ and $y$ are integers. Let $f(\cdot)$ be a function that assigns gray values to the grid point $(x, y)$, where $f$ is an integer in $F$, then $f(x, y)$ is a digital image, and this assignment process is a quantization process.

From the perspective of computer processing of images, an image must be discretized in space and gray level before it can be processed by the computer. The discretization of spatial coordinates is called spatial sampling (abbreviated as sampling), which determines the spatial resolution of the image; the discretization of gray values is called grayscale quantization (abbreviated as quantization), which determines the amplitude resolution of the image.

图像处理代考

1.1.1 图像表示与显示

1.1.1.1 图像和像素

有限元方法代写

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MATLAB代写

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