机器视觉代写|图像处理作业代写Image Processing代考|RESEARCH BACKGROUND

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我们提供的图像处理Image Processing及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 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
SURF Conference-Environmental Sciences – Rhode Island IDeA Network of  Biomedical Research Excellence
机器视觉代写|图像处理作业代写Image Processing代考|RESEARCH BACKGROUND

机器视觉代写|图像处理作业代写Image Processing代考|RESEARCH BACKGROUND

Sea ice, defined as any form of ice that forms as a result of sea water freezing [91], occurs primarily in the polar regions and covers approximately $7 \%$ of the total area of the world’s oceans [168]. Sea ice is turbulent because of wind, wave, and temperature fluctuations, and it influences the movement of ocean waters, fluxes of heat, and circulation between atmosphere and ocean [1]. Sea ice plays important roles in climatology, meteorology, oceanography, physics, maritime navigation, marine biology, Arctic (and Antarctic) offshore operations, and world trade [137]. For example, if gradual warming melts sea ice over time, the abnormal changes in the amount of sea ice can affect the habitats of the animals that live in the polar regions, and it can disrupt normal atmosphere/ice/ocean momentum transfer and heat exchange that thereby may lead to further changes in global climate [2]. Moreover, the prevalence of sea ice will be a determining factor to human activities in the Arctic regions, such as scientific voyages, oil and gas activities, and Arctic shipping through the availability of the Arctic sailing routes from northern European to northern Pacific ports.

Ice concentration, ice floe size distribution, and ice types are important parameters in the field observations of sea ice. Because the sizes of the ice floes and brash ice can range from about one meter to a few kilometers, the temporally and spatially continuous field observations of sea ice are necessary for safe marine activities and understanding of the Arctic climate change. To that end, one of the most efficient ways to observe the ice conditions in the oceans is by using satellite, aerial, or nautical imagery and applying digital image processing techniques to the ice image data.
The analysis of image information obtained from remote sensors can reduce or suppress the ambiguities, incompleteness, uncertainties, and errors of the object and the environment via various processing techniques. It can also make the information of the object and environment more accurate and reliable by maximizing the use of image information from a variety of information sources, obtaining a more comprehensive and distinct environment. Therefore, various types of remotely sensed data and imaging technologies have been aiding the development of sea ice observation. Particularly the satellite observing systems and corresponding data processing algorithms have been widely used in the determination of sea ice parameters, such as extracting ice concentration $[141,34,136,79]$, classifying ice types $[59,26,144,14,180,47]$, and analyzing ice floe properties $[7,86,145]$. Nowadays the ice concentration data on a global scale has become available on a daily basis due to the development of microwave satellite sensors. According to this innovation, it has become possible to monitor the variability of sea ice extent on a global basis. However, it is still a big issue to predict the sea ice behavior in the numerical sea ice model due to the lack of our knowledge about the sub-grid scale information of in the JZ20-2 oil-gas field of the Liaodong Bay, and by this system, ice thickness, ice concentration, and ice velocity of the whole ice period in the Bohai Sea were determined continuously during the winter of $2009-2010$.

机器视觉代写|图像处理作业代写Image Processing代考|ICE CONCENTRATION

Ice concentration $(I C)$ is the ratio of ice on unit area of sea surface. To obtain $I C$ from a visual ice image, only the visible ice can be considered, including brash ice and, if visible in the image, submerged ice. With the image area, the height of the image taken above the ice sheet, and the segmentation, which is the identification of the ice pixels from the water pixels, the actual area of sea ice and sea surface can be derived. However, the actual domain area is not necessary for calculating the ice concentration.

In simplified terms, ice concentration from a digital visible image is, in this book, defined as the area covered by visible ice observable in the 2-dimensional image, taken vertically from above, over the total sea surface domain area of the image.
A digital image is a numerical representation of a 2-dimensional picture as a finite set of values called pixels. Hence, ice concentration can be derived by calculating the fraction of the number of pixels of visible ice to the total number of pixels within the image domain. An image may contain parts of land or other non-relevant areas. Thus, the domain area is an effective area within the image after the non-relevant parts have been removed. The ice concentration is then given by:
I C &=f(\text { image area, height above ice sheet, segmentation }) \
&=\frac{\text { Area of all visible ice within domain }}{\text { Actual domain area }} \
&=\frac{\text { Number of pixels of visible ice in the image domain }}{\text { Total number of pixels in the image domain }}

机器视觉代写|图像处理作业代写Image Processing代考|ICE TYPES

Various types of sea ice can be found in ice-covered regions, and different types of sea ice have different physical properties. As defined in Løset et al. [91]:

  • Floe is any relatively flat piece of sea ice $20 \mathrm{~m}$ or more across. It is subdivided according to horizontal extent. A giant flow is over $10 \mathrm{~km}$ across; a vast floe is 2 to $10 \mathrm{~km}$ across; a big floe is 500 to $2000 \mathrm{~m}$ across; a medium floe is 100 to $500 \mathrm{~m}$ across; and a small floe is 20 to $100 \mathrm{~m}$ across.
  • Ice cake is any relatively flat piece of sea ice less than $20 \mathrm{~m}$ across.
  • Brash ice is accumulations of floating ice made up of fragments not more than $2 \mathrm{~m}$ across and the wreckage of other forms of ice. It is common between colliding floes or in regions where pressure ridges have collapsed.
  • Slush is snow that is saturated and mixed with water on land or ice surfaces, or as a viscous floating mass in water after heavy snowfall.
    In this book, for simplicity, the size of the sea ice piece is the only criterion to distinguish ice floe and brash ice. That is, any relatively flat piece of sea ice $2 \mathrm{~m}$ or more across is considered as “ice floe”, while any relatively flat piece of sea ice less than $2 \mathrm{~m}$ across is considered as “brash ice (piece)”. The residual of ice pixels are considered as “slush”.
Ice-Floe Impact with a Rubble-Mound Causeway at the Port of Nome, Alaska |  Journal of Cold Regions Engineering | Vol 33, No 2
机器视觉代写|图像处理作业代写Image Processing代考|RESEARCH BACKGROUND


机器视觉代写|图像处理作业代写Image Processing代考|RESEARCH BACKGROUND


对从遥感器获取的图像信息进行分析,可以通过各种处理技术减少或抑制物体和环境的模糊性、不完整性、不确定性和误差。它还可以通过最大限度地利用来自各种信息源的图像信息,使物体和环境的信息更加准确和可靠,获得更加全面和鲜明的环境。因此,各类遥感数据和成像技术一直在助力海冰观测的发展。特别是卫星观测系统和相应的数据处理算法已广泛应用于海冰参数的确定,例如提取冰浓度[141,34,136,79], 冰种分类[59,26,144,14,180,47], 并分析浮冰特性[7,86,145]. 如今,由于微波卫星传感器的发展,全球范围内的冰浓度数据已成为每天可用的数据。根据这项创新,可以在全球范围内监测海冰范围的变化。然而,由于缺乏对辽东湾JZ20-2油气田亚网格尺度信息的了解,在数值海冰模型中预测海冰行为仍然是一个很大的问题。该系统连续测定了渤海整个冰期的冰厚度、冰浓度和冰速。2009−2010.

机器视觉代写|图像处理作业代写Image Processing代考|ICE CONCENTRATION


一世C=F( 图像区域,冰盖上方的高度,分割 ) = 域内所有可见冰的面积  实际域面积  = 图像域中可见冰的像素数  图像域中的像素总数 

机器视觉代写|图像处理作业代写Image Processing代考|ICE TYPES

在冰雪覆盖地区可以发现各种类型的海冰,不同类型的海冰具有不同的物理性质。正如 Løset 等人所定义的那样。[91]:

  • 浮冰是任何相对平坦的海冰20 米或更多。它是根据水平范围细分的。一个巨大的流程结束了10 ķ米穿过; 巨大的浮冰是 2 到10 ķ米穿过; 一大块浮冰是 500 到2000 米穿过; 中等漂浮物是 100 到500 米穿过; 一个小浮冰是 20 到100 米穿过。
  • 冰糕是任何相对平坦的海冰,小于20 米穿过。
  • 碎冰是由不超过2 米穿过和其他形式的冰的残骸。在碰撞的浮体之间或在压力脊塌陷的区域中很常见。
  • 雪泥是在陆地或冰面上饱和并与水混合的雪,或者是大雪后在水中形成的粘性漂浮块。
    在本书中,为简单起见,海冰块的大小是区分浮冰和碎冰的唯一标准。也就是说,任何相对平坦的海冰2 米或更大的海冰被认为是“浮冰”,而任何相对平坦的海冰小于2 米横跨被认为是“碎冰(片)”。冰像素的残差被认为是“slush”。
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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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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