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1、此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：A New Approach for Image Segmentation with MATLAB外文作者：Saurabh Chaudhury, Indrani Pal 文献出处: 2018 International Conference on Computer and Communication Technology (ICCCT)(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文2472单词，14788字符(字符就是印
2、刷符)，中文3702汉字。（如果字数多了，可自行删减，大多数学校都是要求选取外文的一部分内容进行翻译的。）A New Approach for Image Segmentation with MATLABAbstract Image segmentation is the first step in many computer vision methods and serves to simplify the problem by grouping the pixels in the image in logical ways. Here we develop a new approach t
3、o deal with the problem of image segmentation in the framework of MATLAB programming environment which integrates thresholding, edge extraction and color information and shows good results when applied to some sample images. Further, we describe the merits and demerits of the proposed algorithm. In
4、fact we can use this segmentation technique as an improvement over the in-built MATLAB edge detection function.Keywords- image, segmentation, edge detection, threshold, colour information, MATLABI.INTRODUCTIONImage Segmentation is the technique of partitioning a digital image into disjoint connected
5、 sets of pixels, each of which corresponds to an object or region before we can analyze image content, identify or classify objects into different groups. Moreover, segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural p
6、arts of objects. However, there are many challenges to deal with image segmentation. The information we know directly about an image is the intensity/color and location of each pixel, and therefore these two variables are the central source of data for any segmentation. However, color and intensity
7、can vary significantly over a single object. Also, shadows, reflections, and textures add sharp colour contrasts to the same surface. Image segmentation has been a long term research initiative. The problems of image segmentation and grouping remain great challenges. Several algorithms and technique
8、s have been developed for image segmentation. However, there is no general solution to the image segmentation problem because there are many levels of detail in an image and therefore many possible ways of meaningfully grouping pixels. The main problem is to decide the criteria on which the grouping
9、 of pixels is to be made. Additionally, after choosing a definition for an optimal segmentation, there are many computational difficulties in finding such a segmentation technique. An image segmentation technique must have the following two properties: first it must capture perceptually important gr
10、oupings or regions, which often reflect global aspects of the image and secondly it must be highly efficient, running in time nearly linear in the number of image pixels. The K-means algorithm 1 is an iterative technique that is used to partition an image into K clusters. This algorithm guarantee to
11、 converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters 2 and the value of K. Histogram-based methods are very efficient when compared to other image segmentation methods because they typically require only one pass through the pixels
12、. A refinement of this technique is to recursively apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This is repeated with smaller and smaller clusters until no more clusters are formed as in 23. One disadvantage of the histogram-seeking metho
13、d is that it may be difficult to identify significant peaks and valleys in the image. Edge detection 4 is a well-developed field on its own within image processing. Region boundaries and edges are closely related, since there is often a sharp adjustment in intensity at the region boundaries. Edge de
14、tection techniques have therefore been used as the base of other segmentation techniques. Region growing method 5 takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. Good segmentation results are dependent on the choice of seeds. Noise in the image
15、 can cause the seeds to be poorly placed. Unseeded region growing is a modified algorithm that doesnt require explicit seeds. Graph partitioning methods 6 can effectively be used for image segmentation. In these methods, the image is modeled as a weighted, undirected graph. Usually a pixel or a grou
16、p of pixels are associated with nodes and edge weights define the (dis)similarity between the neighborhood pixels. The graph (image) is then partitioned according to a criterion designed to model good clusters. Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image. Some popular algorithms of this category are normalized cuts 7, random walker 8, minimum-cut 9, isoperimetric partitionin
