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Manish-TMM-(1)
全参考型视频质量评价方法，基于机器学习和视频特征提取，发表与2012年TMM(FR VQA method based on machine learning and video feature )
- 2013-08-29 17:31:51下载
- 积分：1
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Remote sensing
采用MFC，实现遥感图像的基本增强，滤波，变换等功能。还可以保存数据。(Using MFC, to achieve the basic enhancement of remote sensing images, filtering, transformation and other functions. You can also save data)
- 2020-12-21 14:29:08下载
- 积分：1
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barcode
基于图像的条形码识别程序（识别EAN13码），只要条形码的背景部分是纯色，程序就能有相当强的鲁棒性。(Image-based bar code recognition program (recognize EAN13 code), as long as the background of the bar code is solid, the program can be quite strongly robust.)
- 2010-01-23 02:13:23下载
- 积分：1
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lane-detect
说明： 车道线检测，对道路的车道线进行检测，检测道路线(Lane line detection, detect the lane line of the road, detect the road line and extract it)
- 2020-06-04 21:14:48下载
- 积分：1
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DMC2C80-V1.1
VC development motion control routines, vb development 12 axis motion control routines
- 2013-12-08 21:55:18下载
- 积分：1
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caffe-ssd.tar
说明： 人工智能 caffe 用与ssd yolo(Artificial intelligence SSD Yolo)
- 2020-06-19 18:00:01下载
- 积分：1
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covariance-tracking
该代码用于实现视觉目标跟踪研究中的协方差跟踪，能够把多种时空特征融合于统一的模型中，在实现视觉目标跟踪时具有较好的鲁棒性，而且其维数等于使用特征的数量，与各个特征的维数无关，因此，其计算复杂度较小，实时性较好。(This code is used to realize covariance tracking in the research field of visual object tracking of computer vision. It enables fusion of various spatial-temporal features into an unified object feature model, hence robust to noise. In addition, its dimension is equal to the number of the features used and independent of the dimensions of the features used. Therefore, it satisfies the requirement of real-time application due to its low computation cost.)
- 2011-07-02 06:58:54下载
- 积分：1
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bandpassfilter
带通滤波器的例子,详细的看，适合初学matlab和图形图像处理的例子(band pass filter example, the detailed, suitable for beginners Matlab and graphic examples of image processing)
- 2007-02-15 10:56:17下载
- 积分：1
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lowfrefus
基于区域方差的加权融合方法，主要解决原始图像分解的低频成分的结合(Weighted fusion method based on regional variance)
- 2020-09-03 17:08:08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
