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Image size of origin is 320*240. Processing time is 30.96 second took. The result of stitching The resul... -
In the YUV color format, Y is bright information, U is blue color area, V is red color area. Show the below picture. The picture is u-v col... -
As you can see in the following video, I created a class that stitching n cameras in real time. https://www.youtube.com/user/feelmare/sear...
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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... -
1. At approach Mat image (ROW, COL, CV_TYPE); image.at (WANT_ROW, WANT_COL); - ROW: Row - COL: Column - CV_TYPE: data type ( for exa...
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In past, I wrote an articel about YUV 444, 422, 411 introduction and yuv rgb converting example code. refer to this page -> http://feel...
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* Introduction - The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm... -
This article explain how to access the thread index when you make block and thread with two dimensions. please refer to this page about me... -
Created Date : 2011.10 Language : C/C++ Tool : Microsoft Visual C++ 2008 Library & Utilized : OpenCV 2.3 Reference : SIFT referenc... -
This is GPU version of this page -> http://feelmare.blogspot.kr/2013/12/finding-largest-subset-images-that-is.html Please refer detail ...
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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