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: 需要arcengine的开发包
基于国土行业的图幅号的小工具：根据经纬度坐标生成图幅号，根据导入的文件（txt，shape文件，手动输入一个范围的点坐标）
保存为shape文件 图幅号是各比例尺-The need for the development of packet-based arcengine Homeland industry Maps gadget No.: In accordance with latitude and longitude coordinates to generate its maps, according to a document to import (txt, shape files, to manually enter a range of point coordinates) saved as shape file maps number is the scale Platform: |
Size: 20361216 |
Author:郑志华 |
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Description: 阵元数16下均匀圆阵的零限形成，先建立约束函数，再创建目标函数，计算零陷-Element number 16 under the Uniform Circular Array of zero-threshold shape, to create constraint function, and then create the objective function, calculated Null Platform: |
Size: 36864 |
Author:蓝云 |
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Description: 对一幅具有颗粒形状的图像进行了处理和分析。能够计算出图像中的颗粒数和颗粒的相关特征，能够较为快速有效地计算出颗粒的特征信息。附有详细算法说明和报告-Particle shape of an image has been processed and analyzed. Be able to calculate the number of particles in the image and the relevant characteristics of particles that can be more quickly and efficiently calculate the characteristics of particles of information. Statements and reports with a detailed algorithm Platform: |
Size: 28672 |
Author:sy |
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Description: 上午上课在稿纸上画了半天，整理下思路，下午动手写的，所有模块已大概成型，本着照顾后学者，以及资源共享的原则，贴上核心的bp算法部分，已经封装好了，使用可以直接调用。三层，基本三层就可以解决我们的这些简单的问题，输入为8×8，输出4，隐层数目conts num=8 还可进一步封装，const输入层节点，输出层节点。-Morning class in the writing paper drew a long time, under the idea of finishing the afternoon hands-on writing, and all modules have been roughly in shape, in line with care, academics, and the principle of resource sharing, the core of the bp algorithm is part of the paste has a good package, and the use of can be directly invoked. Three, the basic three layers can be resolved by these simple questions to our input for the 8 × 8, the output 4, the number of hidden layers conts num = 8 can be further packaged, const input layer nodes, output layer nodes. Platform: |
Size: 2048 |
Author:杨元龙 |
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Description: 这是NURBS曲线的绘制程序，在MATLAB-2008a环境下进行编写，包含了几个基函数子程序。可以通过调节控制顶点和点的权重来调节曲面形状。
-This is a NURBS curve drawing program, in the MATLAB-2008a environments prepared, including a number of basis function subprogram. Can be controlled by adjusting the weight of vertex and point to adjust the surface shape. Platform: |
Size: 15360 |
Author:小胡 |
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Description: 在matlab中对服从威布尔分布的寿命数据进行尺寸、特征寿命、形状参数的估计。数据开始只用输入向量n的一组数就好了。-In matlab Weibull distribution of life data size, estimated lifetime characteristic shape parameters. Data starts with only a set number of input vectors n enough. Platform: |
Size: 2048 |
Author:张舒翔 |
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Description: Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p. Platform: |
Size: 3072 |
Author:fatemeh |
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Search - shape number matlab