HOG test

所属分类图形图象
开发工具:matlab
文件大小:31671KB
下载次数:2
上传日期:2018-05-16 17:44:17
上 传 者大葱666
说明:  方向梯度直方图(Histogram of Oriented Gradient, HOG)特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。HOG特征通过计算和统计图像局部区域的梯度方向直方图来构成特征。我们可以拿它对图像处理图像识别,采用HOG算法可以直观的检测出图像的HOG特征,对图像进行识别。可以应用:车牌识别。
(The Histogram of Oriented Gradient (HOG) feature is a feature descriptor for object detection in computer vision and image processing. HOG features are formed by computing and statistical histogram of gradient directions in local regions of the image. We can take it to image processing image recognition, using the HOG algorithm can directly detect the HOG features of the image, and identify the image. It can be applied: license plate recognition.)

文件列表
HOG.cpp, 11742 , 2015-10-30
Pedestrians64x128\03_ReadFileNames.bat, 480 , 2015-06-14
Pedestrians64x128\Classifier.xml, 70468 , 2015-10-30
Pedestrians64x128\HOG_SVM.txt, 39339 , 2015-10-30
Pedestrians64x128\Negative\000001.jpg, 11477 , 2015-06-15
Pedestrians64x128\Negative\000002.jpg, 13598 , 2015-06-15
Pedestrians64x128\Negative\000003.jpg, 12938 , 2015-06-15
Pedestrians64x128\Negative\000004.jpg, 10205 , 2015-06-15
Pedestrians64x128\Negative\000005.jpg, 11850 , 2015-06-15
Pedestrians64x128\Negative\000006.jpg, 10799 , 2015-06-15
Pedestrians64x128\Negative\000007.jpg, 13579 , 2015-06-15
Pedestrians64x128\Negative\000008.jpg, 10725 , 2015-06-15
Pedestrians64x128\Negative\000009.jpg, 13357 , 2015-06-15
Pedestrians64x128\Negative\000010.jpg, 14305 , 2015-06-15
Pedestrians64x128\Negative\000011.jpg, 9266 , 2015-06-15
Pedestrians64x128\Negative\000012.jpg, 7886 , 2015-06-15
Pedestrians64x128\Negative\000013.jpg, 13057 , 2015-06-15
Pedestrians64x128\Negative\000014.jpg, 14991 , 2015-06-15
Pedestrians64x128\Negative\000015.jpg, 10894 , 2015-06-15
Pedestrians64x128\Negative\000016.jpg, 7504 , 2015-06-15
Pedestrians64x128\Negative\000017.jpg, 13590 , 2015-06-15
Pedestrians64x128\Negative\000018.jpg, 12755 , 2015-06-15
Pedestrians64x128\Negative\000019.jpg, 8221 , 2015-06-15
Pedestrians64x128\Negative\000020.jpg, 9533 , 2015-06-15
Pedestrians64x128\Negative\000022.jpg, 11639 , 2015-06-15
Pedestrians64x128\Negative\000023.jpg, 15275 , 2015-06-15
Pedestrians64x128\Negative\000024.jpg, 11677 , 2015-06-15
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Pedestrians64x128\Negative\000026.jpg, 10517 , 2015-06-15
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Pedestrians64x128\Negative\000029.jpg, 12249 , 2015-06-15
Pedestrians64x128\Negative\000030.jpg, 12044 , 2015-06-15
Pedestrians64x128\Negative\000031.jpg, 12357 , 2015-06-15
Pedestrians64x128\Negative\000032.jpg, 8757 , 2015-06-15
Pedestrians64x128\Negative\000033.jpg, 8194 , 2015-06-15
Pedestrians64x128\Negative\000034.jpg, 11459 , 2015-06-15
Pedestrians64x128\Negative\000035.jpg, 11216 , 2015-06-15
Pedestrians64x128\Negative\000036.jpg, 10707 , 2015-06-15
Pedestrians64x128\Negative\000037.jpg, 13520 , 2015-06-15
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Pedestrians64x128\Negative\000039.jpg, 8923 , 2015-06-15
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Pedestrians64x128\Negative\000043.jpg, 13454 , 2015-06-15
Pedestrians64x128\Negative\000044.jpg, 8712 , 2015-06-15
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Pedestrians64x128\Negative\000046.jpg, 14530 , 2015-06-15
Pedestrians64x128\Negative\000047.jpg, 13943 , 2015-06-15
Pedestrians64x128\Negative\000048.jpg, 11744 , 2015-06-15
Pedestrians64x128\Negative\000049.jpg, 8327 , 2015-06-15
Pedestrians64x128\Negative\000050.jpg, 11093 , 2015-06-15
Pedestrians64x128\Negative\000051.jpg, 9994 , 2015-06-15
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Pedestrians64x128\Negative\000053.jpg, 8419 , 2015-06-15
Pedestrians64x128\Negative\000054.jpg, 10886 , 2015-06-15
Pedestrians64x128\Negative\000055.jpg, 11899 , 2015-06-15
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Pedestrians64x128\Negative\000075.jpg, 13365 , 2015-06-15
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Pedestrians64x128\Negative\000077.jpg, 6954 , 2015-06-15
Pedestrians64x128\Negative\000078.jpg, 11233 , 2015-06-15
Pedestrians64x128\Negative\000079.jpg, 9459 , 2015-06-15
Pedestrians64x128\Negative\000080.jpg, 8356 , 2015-06-15
Pedestrians64x128\Negative\000081.jpg, 12525 , 2015-06-15
Pedestrians64x128\Negative\000082.jpg, 9810 , 2015-06-15
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Pedestrians64x128\Negative\000099.jpg, 9821 , 2015-06-15
Pedestrians64x128\Negative\000100.jpg, 9222 , 2015-06-15

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