图像的向量和矩阵块表示及可视化方法设计
文献综述
摘要：随着互联网技术的高速发展，尤其是计算机的大范围普及，我们得以见证图像测量分析向数字信息分析的巨大转变。图像分析与图像信息转化的联系日益紧密，这与当代信息技术飞速发展引起的巨大变革和当代社会逐渐融入信息化大潮密不可分。就目前而言，如何有效地将图像信息转化为数据信息是一个难点，当前业界大多采用向量和矩阵来表示图像信息。除此以外，在转化过程中如何保证图像信息不缺失也是研究的重点方向，同时这也是本次试验的研究重点和解决方向。我们都知道，图像数据是指用数值表示的各像素的灰度值的集合。对真实世界的图像一般由图像上每一点光的强弱和频谱来表示，把图像信息转换成数据信息时，须将图像分解为很多小区域，这些小区域称为像素，可以用一个数值来表示它的灰度，对于彩色图像常用红、绿、蓝三原色分量表示。顺序地抽取每一个像素的信息，就可以用一个离散的阵列来代表一幅连续的图像。如地理信息系统中一般指栅格数据。 本课题是研究图像的表示方法，以向量和矩阵形式描述，重点是考虑图像信息的无损表示方法，并通过C 语言以及matlab使用可视化方法展示表示结果。
关键词：图像表示、matlab、可视化
THEME：Vector and matrix block representation of image and design of visualization method
Abstract：With the rapid development of Internet technology, especially the popularization of computers, we can witness the great transformation from image measurement analysis to digital information analysis. The relationship between image analysis and image information transformation is increasingly close, which is closely related to the great changes caused by the rapid development of contemporary information technology and the gradual integration of contemporary society into the information tide. At present, how to effectively transform image information into data information is a difficulty. At present, vector and matrix are mostly used to represent image information in the industry. In addition, how to ensure that the image information is not missing in the process of transformation is also the key direction of the study, which is also the research focus and solution direction of this experiment. As we all know, image data refers to the collection of gray values of each pixel represented by numerical value. The image of the real world is generally represented by the intensity and frequency spectrum of each light point on the image. When the image information is converted into data information, the image must be decomposed into many small areas, which are called pixels, and their gray levels can be represented by a numerical value. For color images, the red, green and blue primary color components are commonly used. By extracting the information of each pixel in order, a discrete array can be used to represent a continuous image. Such as grid data in GIS. This topic is to study the representation method of image, which is described in the form of vector and matrix. The emphasis is to consider the lossless representation method of image information, and show the representation results by using the visual method of C language and MATLAB.
Keywords：Image representation, MATLAB, visualization
前言
随着信息时代的到来，我们的工作和生活与信息的关系日益紧密。如今的数据可视化致力于用更加生动、友好的形式，呈现隐藏在事物背后的各种信息。在图像领域同样如此。不同的感官获取图像的速度和效率是不一样的，面对一张密密麻麻的图像，你可能会很头疼，也难以判断所要分析的部分的具体状况。这是因为大家看图像的时候，先认知的是图像的整体信息，然后将信息转化成数字信号，最后再在头脑里进行的二次分类。但实际上以上操作，远不如一张可视化的图表更令人一目了然和易于解读。
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