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Method for splicing videos in real time based on SURF (Speeded UP Robust Features) algorithm

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TLDR
In this article, a method for real-time splicing of video frames is presented, which uses a phase correlation method to compute the overlapped area of video frame images to be spliced.
Abstract
The invention discloses a method for splicing videos in real time and belongs to technical field of video image processing. The method computes the overlapped area of video frame images to be spliced by using a phase correlation method, improves a SURF (Speeded UP Robust Features) algorithm, simplifies the generation process of a characteristic point descriptor in the SURF algorithm, reduces the dimensionality of the descriptor, extracts the characteristic points of the overlapped area of the video frames by using the improved SURF algorithm. The invention provides an image registration method based on characteristic block matching to match the characteristic points, decrease calculated amount, increase computational efficiency, and quickly solve an image conversion model by using the characteristic block matching. Finally the invention provides a projection matrix updating method based on a correlation coefficient. The projection matrix updating method is used for updating a projection matrix, prevents erroneous splicing results, and fusing the video frame images so as to splice the videos in real time.

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Real-time stitching method for high-definition video

TL;DR: In this paper, a real-time stitching method for high-definition video is proposed, which mainly comprises the following steps of synchronous video decoding, initialization, frame-by-frame stitching and the like.
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TL;DR: In this article, a video stitching method for real-time video processing is described, which comprises the following steps: when a video frame is a specific video frame, using an image overlapping area between the to-be-processed video frame and an adjacent video frame corresponding to the video frame as a ROI video image; performing significant area detection on the ROI image, performing feature point detection, and performing rough matching on the significant area by using the SURF algorithm, and purifying the feature points subjected to the rough matching.
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References
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