Description: 文章介绍了利用车牌的纹理特征和区域形状特征检测车牌区域情况。为提高区域检
测的正确性, 利用新的改进的主动轮廓模型, 利用先验知识确定精确的车牌边界。在精确的车牌区
域中切割出车牌中的单个字符, 最后利用支持矢量机对字符进行分类识别。
汤志勇 杨晨晖 王炳波
(厦门大学 厦门361005)-article describes the use of the plates texture characteristics and the shape feature detection plates regional situations. To improve the accuracy of detection region, the use of new initiatives to improve the contour model, the use of a priori knowledge to determine the exact license plate boundary. The exact number plates region cut out plates of a single character, the final use of support vector machines for character recognition and classification. Tang Zhiyong Yang Chen-hui Wangbingbei (Xiamen University, Xiamen 361005) Platform: |
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Author:adnsid |
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Description: 我们给出一个模板 和一幅图象 。不难发现原图中左边暗，右边亮，中间存在着一条明显的边界。进行模板操作后的结果如下： 。
可以看出，第3、4列比其他列的灰度值高很多，人眼观察时，就能发现一条很明显的亮边，其它区域都很暗，这样就起到了边沿检测的作用。
为什么会这样呢？仔细看看那个模板就明白了，它的意思是将右邻点的灰度值减左邻点的灰度值作为该点的灰度值。在灰度相近的区域内，这么做的结果使得该点的灰度值接近于0；而在边界附近，灰度值有明显的跳变，这么做的结果使得该点的灰度值很大，这样就出现了上面的结果。
这种模板就是一种边沿检测器，它在数学上的涵义是一种基于梯度的滤波器，又称边沿算子，你没有必要知道梯度的确切涵义，只要有这个概念就可以了。梯度是有方向的，和边沿的方向总是正交(垂直)的，例如，对于上面那幅图象的转置图象，边是水平方向的，我们可以用梯度是垂直方向的模板 检测它的边沿。
例如，一个梯度为45度方向模板 ，可以检测出135度方向的边沿。-we give a template and an image. It is not difficult to find the maximum were left dark, right-liang, in the middle there is a clear boundary. After the template for the operation results are as follows :. Can be seen, three, four out other than the gray value is much higher, eye observation, we can obviously found a bright side. Other regions are dark, and this has played a role in the detection of 2500. Why is this the case? A closer look at the template on which to understand it. It means the right to the point o gray minus left point as a gray value of the point of gray values. In a similar gray area, do so as a result of the point of gray values close to 0; And near the border. gray values jump significantly changed, the results do make the point very gray value, and this appeared to Platform: |
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Author:李涯 |
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Description: 一种比较好用的边缘检测方法CORNER Find corners in tensity image.
％ CORNER works by the following step:
％ 1. Apply the Canny edge detector to the gray level image and obtain a
％ binary edge-map.
％ 2. Extract the edge contours from the edge-map, fill the gaps in the
％ contours.
％ 3. Compute curvature at a low scale for each contour to retain all
％ true corners.
％ 4. All of the curvature local maxima are considered as corner
％ candidates, then rounded corners and false corners due to boundary
％ noise and details were eliminated.
％ 5. End points of line mode curve were added as corner, if they are not
％ close to the above detected corners.-A more useful edge detection method CORNER Find corners in tensity image. CORNER works by the following step: 1. Apply the Canny edge detector to the gray level image and obtain a binary edge-map. 2. Extract the edge contours from the edge-map, fill the gaps in the contours. 3. Compute curvature at a low scale for each contour to retain all true corners. 4. All of the curvature local maxima are considered as corner candidates, then rounded corners and false corners due to boundary noise and details were eliminated. 5. End points of line mode curve were added as corner, if they are not close to the above detected corners. Platform: |
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Author:林蛋大 |
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Description: 自己做的关于边缘检测与边界跟踪的matlab程序-Own on the edge of the boundary detection and tracking procedures matlab Platform: |
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Author:wang |
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Description: Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF-Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al- Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF Platform: |
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Author:tra ba huy |
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Description: The present report outlines the design, implementation and performance of the application of Hough
Transform (HT) to detect circles in arbitrary pictures.
Detecting circles in arbitrary pictures involves a two step process in this project. First, edges within the
image have to be detected then, by means of a voting mechanism the most probable circles will be
located.
Edge detection goal is to look for boundary locations that naturally happen between objects. Objects
normally have continuous intensity values therefore sudden changes in that pattern might indicate a
boundary condition. Changes of intensity in one direction can be calculated by the gradient operator
however, noise can alter meaningful edges. Convolution is then normally applied to account for a small
amount of smoothing, thus reducing noise.-The present report outlines the design, implementation and performance of the application of Hough
Transform (HT) to detect circles in arbitrary pictures.
Detecting circles in arbitrary pictures involves a two step process in this project. First, edges within the
image have to be detected then, by means of a voting mechanism the most probable circles will be
located.
Edge detection goal is to look for boundary locations that naturally happen between objects. Objects
normally have continuous intensity values therefore sudden changes in that pattern might indicate a
boundary condition. Changes of intensity in one direction can be calculated by the gradient operator
however, noise can alter meaningful edges. Convolution is then normally applied to account for a small
amount of smoothing, thus reducing noise. Platform: |
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Author:donna |
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Description: 边缘检测1.制作一张包括色块、线条的单色背景图片，先对其作低通滤波产生一幅色块和线条边缘模糊的降质图片待处理，采用任意二种边缘检测算法检测待处理图片中的色块和线条的边缘，得到二值化的处理结果图。从结果图中提取色块和线条的边界坐标值，与实际生成原始图像时采用的真实坐标数据作比较，对产生的检测误差作分析。2.拍摄一张包含待检测物体的黑白或者彩色照片，试采用一种边缘检测的算法做待检测物体的自动提取，对生成的结果作分析。-Edge Detection 1. To produce a including blocks of color lines color the background picture, the first of its low-pass filter for a color blocks and lines produced lower quality images fuzzy edges to be processed, using any two edge detection algorithm to detect when processing in the image blocks of color and line the edge of the two values are the result of the treatment plan. Extracted from the results map the boundary blocks of color and line coordinates, the original image and the actual generated coordinate data used to compare the true, and on the detection of error for analysis. 2. Take a test to be included black and white or color photographs of objects, try using a edge detection algorithm on automatic extraction of objects to be detected, the results generated for analysis. Platform: |
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Author:tosh |
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Description: 自动检测提取彩色图像边界，使用matlab实现，下载文件后，请将文件后缀改为.m。-Automatic detection of color images to extract the boundary, the use of matlab to achieve, download files, change the file extension to. M. Platform: |
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Author:kaka |
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Description: 该文件夹中有六个M文件，说明如下：
boundary_extraction.m : 目标区域边界抽取主函数，其中调用ostu、four_neighbor、eight_neighbor、globe_graph
otsu.m : 类判别分类法（otsu全局二值化算法）
four_neighbor.m : 四邻域法边界抽取
eight_neighbor.m : 八邻域法边界抽取
globe_graph.m : 全局检测法边界抽取
area_peri.m : 目标区域周长计算主函数，调用ostu
4A.bmp
face.bmp
gun.bmp : 原始灰度图片
PeriAnalysis.txt : 记录了实验过程中目标区域的总数和各自的周长
实验结果文件夹 : 保存了实验过程中生成的图像和程序流程图-The folder has six M-file, as follows: boundary_extraction.m: the main function of the target area boundary extraction, which calls ostu, four_neighbor, eight_neighbor, globe_graph otsu.m: class discriminant classification (otsu global binarization algorithm) four_neighbor . m: neighbors-domain method boundary extraction eight_neighbor.m: eight neighborhood boundary extraction method globe_graph.m: Global detection boundary extraction area_peri.m: calculate the circumference of the main function of the target area, call ostu 4A.bmp face.bmp gun.bmp : The original grayscale pictures PeriAnalysis.txt: recorded during the experiment the total number of the target area and their perimeter results Folder: to save the images generated during the experiment and the program flow chart Platform: |
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Author:许胜强 |
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Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse. Platform: |
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Author:boss |
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Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse. Platform: |
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Author:boss |
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Description: 本文主要讨论获得图片后如何提取其边界信息，首先利用图像灰度均匀化解决了对比度不清晰的问题，然后利用高斯滤波法对图片进行滤波去噪，进而利用Canny算法实现边缘检测提取边界，该方法与Matlab自带的求边界方法有明显的提高，为利用Matlab程序求图像分形维数提供了更精确的前提。-This article focuses on how to get the image to extract the boundary information, the first to use to the gray uniform solve the contrast is not clear, and then use the Gaussian filter image filtering noise, and then use the Canny edge detection algorithm to extract the boundary of the and Matlab comes boundary method has increased significantly, and provide a more accurate premise for the use of Matlab program for image fractal dimension. Platform: |
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Author:陈泽奇 |
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Description: 边缘提取：边缘检测的基本思想首先是利用边缘增强算子，突出图像中的局部边缘，然后定义象素的“边缘强度”，通过设置阈值的方法提取边缘点集。由于噪声和模糊的存在，监测到的边界可能会变宽或在某点处发生间断。因此，边界检测包括两个基本内容：
（1）用边缘算子提取出反映灰度变化的边缘点集。
（2）在边缘点集合中剔除某些边界点或填补边界间断点，并将这些边缘连接成完整的线。(Edge extraction: the basic idea of edge detection is to use edge enhancement operator to highlight the local edge of image, and then define the edge intensity of pixels, and set the threshold method to extract the edge point set. Due to the presence of noise and blurring, the monitored boundaries may be widened or discontinuous at some point. Therefore, the boundary detection includes two basic contents:
(1) edge operators are used to extract the set of edge points that reflect the change of gray level.
(2) eliminate some boundary points in the set of edge points or fill the breakpoints between the boundaries, and connect these edges into a complete line.) Platform: |
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Author:Schonien
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