外文翻译---MATLAB 在图像边缘检测中的应用

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1、英文资料翻译 MATLAB application in image edge detection MATLAB of the 1984 countries MathWorkscompany to market since, after 10 years of development, has become internationally recognized the best technology application software. MATLAB is not only a kind of direct, efficient computer language, and at the

2、 same time, a scientific computing platform, it for data analysis and data visualization, algorithm and application development to provide the most core of math and advanced graphics tools. According to provide it with the more than 500 math and engineering function, engineering and technical person

3、nel and scientific workers can integrated environment of developing or programming to complete their calculation. MATLAB software has very strong openness and adapt to sex. Keep the kernel in under the condition of invariable, MATLAB is in view of the different application subject of launch correspo

4、nding Toolbox (Toolbox), has now launched image processing Toolbox, signal processing Toolbox, wavelet Toolbox, neural network Toolbox and communication tools box, etc multiple disciplines special kit, which would place of different subjects research work. MATLAB image processing kit is by a series

5、of support image processing function from the composition, the support of the image processing operation: geometric operation area of operation and operation； Linear filter and filter design； Transform (DCT transform)； Image analysis and strengthened； Binary image manipulation, etc. Image processing

6、 tool kit function, the function can be divided into the following categories: image display； Image file input and output； Geometric operation； Pixels statistics； Image analysis and strengthened； Image filtering； Sex 2 d filter design； Image transformation； Fields and piece of operation； Binary imag

7、e operation； Color mapping and color space transformation； Image types and type conversion； Kit acquiring parameters and Settings. 1. Edge detection this Use computer image processing has two purposes: produce more suitable for human observation and identification of the images； Hope can by the auto

8、matic computer image recognition and understanding. No matter what kind of purpose to, image processing the key step is to contain a variety of scenery of decomposition of image information. Decomposition of the end result is that break down into some has some kind of characteristics of the smallest

9、 components, known as the image of the yuan. Relative to the whole image of speaking, this theyuan more easily to be rapid processing. Image characteristics is to point to the image can be used as the sign of the field properties, it can be divided into the statistical features of the image and imag

10、e visual, two types of levy. The statistical features of the image is to point to some people the characteristics of definition, through the transform to get, such as image histogram, moments, spectrum, etc.； Image visual characteristics is refers to person visual sense can be directly by the natura

11、l features, such as the brightness of the area, and texture or outline, etc. The two kinds of characteristics of the image into a series of meaningful goal or regional process called image segmentation. The image is the basic characteristics of edge, the edge is to show its pixel grayscale around a

12、step change order or roof of the collection of those changes pixels. It exists in target and background, goals and objectives, regional and region, the yuan and the yuan between, therefore, it is the image segmentation dependent on the most important characteristic that the texture characteristics o

13、f important information sources and shape characteristics of the foundation, and the image of the texture characteristics and the extraction of shape often dependent on image segmentation. Image edge extraction is also the basis of image matching, because it is the sign of position, the change of th

14、e original is not sensitive, and can be used for matching the feature points. The edge of the image is reflected by gray not continuity. Classic edge extraction method is investigation of each pixel image in an area of the gray change, use edge first or second order nearby directional derivative cha

15、nge rule, with simple method of edge detection, this method called edge detection method of local operators. The type of edge can be divided into two types: (1) step representation sexual edge, it on both sides of the pixel gray value varies significantly different； (2) the roof edges, it is located

16、 in gray value from the change of increased to reduce the turning point. For order jump sexual edge, second order directional derivative in edge is zero cross； For the roof edges, second order directional derivative in edge take extreme value. If a pixel fell in the image a certain object boundary,

17、then its field will become a gray level with the change. The most useful to change two features is the rate of change and the gray direction, they are in the range of the gradient vector and the direction to said. Edge detection operator check every pixel grayscale rate fields and evaluation, and al

18、so include to determine the directions of the most use based on directional derivative deconvolution method for masking. Digital image processing technique has been widely applied to the biomedical field, the use of computer image processing and analysis, and complete detection and recognition of ca

19、ncer cells can help doctors make a diagnosis of tumor cancers. Need to be made in the identification of cancer cells, the quantitative results, the human eye is difficult to accurately complete such work, and the use of computer image processing to complete the analysis and identification of the mic

20、roscopic images have made great progress. In recent years, domestic and foreign medical images of cancer cells testing to identify the researchers put forward a lot of theory and method for the diagnosis of cancer cells has very important meaning and practical value. Cell edge detection is the cell

21、area of the number of roundness and color, shape and chromaticity calculation and the basis of the analysis their test results directly affect the analysis and diagnosis of the disease. Classical edge detection operators such as Sobel operator, Laplacian operator, each pixel neighborhood of the image gray scale changes to detect the edge. Although these operators is simple, fast, but there are sensitive to noise, get isolated or in short sections of a