imagezengqiang

广度优先搜索和深度优先搜索在网络拓扑图上的实现。广度优先搜索是从某一节点开始，搜索与其线连接的所有节点，按照广度方向像外扩展，直到不重复遍历所有节点；深度优先搜索是从某一节点开始，沿着其搜索到的第一个节点不断深入下去，当无法再深入的时候，回溯节点，然后再在回溯中的某一节点开始沿另一个方向深度搜索，直到不重复的遍历所有节点。 (Breadth-first search and depth-first search in the network topology is realized. Breadth-first search is started from a node, searching all the nodes connected to its line, in accordance with the breadth direction as extracapsular extension, until no repeat traverse all nodes depth-first search is started from a node along its search to the first nodes deepening it, when it can not go any further, backtracking node, and then back again in the beginning of a node depth search in the other direction until the non-repetition of the traverse all nodes.)

大量图片的处理代码,特别是图片的批量处理，非常有利于在处理图片时使用(A large number of image processing code, especially images of batch processing, image processing is very conducive to the use of)

深度图像处理,主要的是对深度图像进行区域分割(depth map)

介绍matlab下求解偏微分方程的函数和用法(Introduction matlab function for solving partial differential equations and usage)

利用matlab对只包含RBG三个波段的图像进行植被提取，通过设定一定的阈值将植被与背景进行分割(The vegetation is extracted from the image containing only three bands of rbg by matlab, and the vegetation and background are segmented by setting a certain threshold.)

一个基于MATLAB环境的图像分割算法，运用最大熵解决图像分割问题。(An image segmentation algorithm based on MATLAB environment is applied to solve the problem of image segmentation using maximum entropy.)

OCR的概念是在1929年由德国科学家Tausheck最先提出来的，后来美国科学家Handel也提出了利用技术对文字进行识别的想法。而最早对印刷体汉字识别进行研究的是IBM公司的Casey和Nagy，1966年他们发表了第一篇关于汉字识别的文章，采用了模板匹配法识别了1000个印刷体汉字。(The concept of OCR in 1929 by the German scientist Tausheck Handel first put forward later, scientists in the United States also put forward ideas of text recognition using technology. In 1966, they published their first article on Chinese character recognition, using template matching method to recognize 1000 printed Chinese characters.)

图像分割是把图像划分成具有实际意义的互补交迭的区域的集合。在图像分割之前，图像区域的数目未知，而在分割后各个区域同时满足均匀性和连通性的条件，故图像分割是一个复杂的过程，目前大多数研究都是针对某一类型图像或者某一具体应用的分割。图像阈值化分割的基本思想是确定一个阈值，然后把每个像素点的像素值和阈值相比较，根据比较的结果把像素划分为两类，前景(1)或背景(0)。该方法的关键是确定一个最优的阈值。常用的阈值确定方法有直接门限法、类间最大方差法(otsu法)、分水岭算法、最小误差法、最大熵法等。该段代码就是图像的阈值分割。(The image segmentation is divided into the practical significance of the emerging area of complementary set. In the image segmentation, before the number of image region, the unknown, and after each area in division at the same time satisfy the uniformity and connectivity condition, so the image segmentation is a complicated process now, most research are for a certain type image or a particular application of the division. Image threshold segmentation of the basic idea is to determine a threshold, then put each pixel threshold value of the pixels and compared, according to the result of the comparison the pixel divided into two categories, the prospect (1) or background (0). This method is the key to determine an optimal threshold. Commonly used method to determine the threshold value of direct threshold method, the largest of the variance between method (otsu method), watershed algorithm, the minimum error method, the maximum entropy method, etc. When the code is image threshold segm)

用于指纹识别中，对指纹图象的预处理，包括分割、二值化、细化等。(for fingerprint identification, the fingerprint image preprocessing, including segmentation, two values, such as thinning.)

说明：  对指定的一幅含有多个目标的灰度图像，进行基于阈值的图像分割处理，对结果图像进行基于数学形态学的后处理设计，以得到更理想的目标区域。最后对结果图像进行区域标记（以颜色区分不同区域）并提取目标个数、各目标区域的面积、圆形度等特征。(The gray image with multiple targets is segmented based on threshold, and the result image is designed based on mathematical morphology to get a better target area. Finally, we mark the region of the result image (distinguish different regions by color) and extract the number of targets, the area of each target region, roundness and other features.)