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fig 1. Left: set 4 points (Left Top, Right Top, Right Bottom, Left Bottom), right:warped image to (0,0) (300,0), (300,300), (0,300) Fi... -
Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm... -
The MNIST dataset is a dataset of handwritten digits, comprising 60 000 training examples and 10 000 test examples. The dataset can be downl... -
Created Date : 2011.10 Language : C/C++ Tool : Microsoft Visual C++ 2008 Library & Utilized : OpenCV 2.3 Reference : SIFT referenc... -
This source code based on -> http://feelmare.blogspot.kr/2011/08/two-image-mosaic-paranoma-based-on-sift.html This link page introduces... -
After training SVM, we should test the trained XML data is reliable or not.. The method to extract HOG feature is refer to -> http://fe...
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I am wondering that two hog features can compare or not. There was a article about this question on this page -> http://stackoverflow... -
This post is about how to copy Mat data to vector and copy vector data to Mat. Reference this example source code. printf ( "/////...
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Background subtractor example souce code. OpenCV support about 3 types subtraction algorithm. Those are MOG, MOG2, GMG algorithms. Det...
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ReplyDeleteThis one is better than "More stringent pedestrian detection by Deep learning verification" and "More stringent pedestrian detection by HOG verification". Do you use OpenCV to achieve such result?
ReplyDeleteDetecting pedestrian using Deep learning is recorded high rate false positive, but not sensitive variable pedestrian pose. and processing speed is not good.
DeleteHog is fast, but to detect typical appearance when training data pose.
So, I can not say yet, which way is better..
Thank you.
can you share this source code! Thank you
ReplyDelete