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ACM
主动轮廓模型图像分割算法，讲XU改进的算法用MATLAB进行实现(Active Contour Model for Image Segmentation Algorithm, speaking XU improved algorithm using MATLAB for the realization of)
- 2008-01-17 20:45:45下载
- 积分：1
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motion-detection
复杂情况下的物体自动跟踪检测程序 希望喜欢 谢谢(Automatic tracking of objects in the complex case detection procedures hope like thank you)
- 2012-12-18 15:03:17下载
- 积分：1
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recognize_face
利用灰度积分投影直接对人脸图像进行检测和眼睛定位是一种常用的算法,但是直接采用该算法会受到背
景、特征等因素的影响,识别准确率较低。提出了一种基于最大类间方差阈值和区域膨胀相结合的检测与定位算法。该算
法首先计算最大类间方差设置阈值,把灰度图像转换为二值图像并检测出人脸区域,然后通过对该人脸区域中的连通区域
进行膨胀及连通性处理,精确定位眼睛坐标。实验表明,此算法可靠,具有较好的识别效果。(face recognized)
- 2010-08-19 16:45:54下载
- 积分：1
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BlurKernelEstimation
模糊图像去模糊处理，Amit等人发表的模糊核的估计算法，图像去模糊，得到清晰图像(image deblurring by Amit)
- 2015-08-05 16:43:24下载
- 积分：1
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SRG
本方法的种子点选取采用Harris角点法，阈值则通过云模型获得，对lena图像进行了较好的分割(The method selected seed points using Harris corner point method, the threshold is obtained through the cloud model, for better lena image segmentation)
- 2020-12-28 19:19:02下载
- 积分：1
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wenli
说明： 基于灰度共生矩阵的纹理特征提取方法实现目标的识别(Gray-level co-occurrence matrix based texture feature extraction methods to achieve the target identification)
- 2009-08-02 11:20:29下载
- 积分：1
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matlabronghe
基于压缩感知与小波变换的图像处理，图像融合等程序(Based on compressed sensing and image processing, wavelet transform image fusion procedures)
- 2012-06-02 12:35:37下载
- 积分：1
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c# bmp图片转 jpg格式实例 附源码
c# bmp图片转 jpg格式实例 附源码
- 2013-05-19下载
- 积分：1
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PG_BOW_DEMO
图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )
- 2011-11-01 17:01:09下载
- 积分：1
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image_enhangce_value
图像增强效果评价指标：1.欧拉范数误差指标 2.等效视数 3.边缘保持指数 4.对比度增强指数(Evaluating results of image enhancement: 1. Oula Fan error index number 2. Equivalent number of 3. Edge-preserving index 4. Contrast enhancement index)
- 2011-05-16 09:14:13下载
- 积分：1
