1 research outputs found
Superpixel-Based Background Recovery from Multiple Images
In this paper, we propose an intuitive method to recover background from
multiple images. The implementation consists of three stages: model
initialization, model update, and background output. We consider the pixels
whose values change little in all input images as background seeds. Images are
then segmented into superpixels with simple linear iterative clustering. When
the number of pixels labelled as background in a superpixel is bigger than a
predefined threshold, we label the superpixel as background to initialize the
background candidate masks. Background candidate images are obtained from input
raw images with the masks. Combining all candidate images, a background image
is produced. The background candidate masks, candidate images, and the
background image are then updated alternately until convergence. Finally,
ghosting artifacts is removed with the k-nearest neighbour method. An
experiment on an outdoor dataset demonstrates that the proposed algorithm can
achieve promising results.Comment: Technical Repor