Pixel label propagation method based on directional tracing windows

Abstract

The invention discloses a pixel label propagation method based on directional tracing windows. The method includes the steps of firstly, determining a region to be labeled in a target image; secondly, setting tracing windows in the target image and allowing the tracing windows to cover the region to be labeled; thirdly, establishing a Gaussian mixture model for each label by applying pixels in the input image, covered by the tracing windows, as samples; fourthly, calculating density in probability of belonging to each label for each pixel, to be labeled and covered by the tracing window; and fifthly, calculating confidence in probability estimated by the tracing windows for each pixel to be labeled; sixthly, processing all the tracing windows in all directions; and seventhly, determining a label belonging to each pixel to be labeled according to the probability and confidence of belonging to the label for the pixel to be labeled, covered by each tracing window. The method has the advantages that spatial context is effectively utilized, and errors caused by ambiguous features are reduced.

Description

technical field [0001] The invention relates to a pixel label propagation method, in particular to a pixel label propagation method based on a directional tracking window. Background technique [0002] Label propagation between video frames is a common problem in video processing, especially video editing. Labels can usually represent the results of video processing, and label propagation can be understood as the process of solving the results of other frames when the results of a certain frame are known. For example, in region tracking and foreground segmentation, users can obtain the results of a certain frame through interaction, and then use label propagation to obtain the results of other video frames. Label propagation generally adopts the following three methods: [0003] 1. Method based on image matching [0004] The method based on image matching first registers the image of the input frame and the target frame, and then copies the pixel label of the input frame ...

AI Technical Summary

Problems solved by technology

The larger the tracking window, the more complex the distribution of features in each window, and the greater the possibility of containing ambiguous features, which will lead to similar problems with the global classifier; The robustness will be better, but at the same time it will be sensitive to local discontinuities between video frames, and it is more prone to errors in fast motion and emerging areas; existing local classifiers use regular-shaped tracking windows, that is, square or Circular tracking window, but it is difficult to obtain satisfactory results when facing the problem of ambiguous features and inter-frame discontinuity at the same time; the directional tracking window disclosed by the present invention will help to overcome this shortcoming of the local classifier

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