Abstract: Salience-preserving image fusion is described. In one aspect, multi-channel images are fused into a single image. The fusing operations are based on importance-weighted gradients. The importance weighted gradients are measured using respective salience maps for each channel in the multi-channel images.

Abstract: Progressive cut interactive object segmentation is described. In one implementation, a system analyzes strokes input by the user during iterative image segmentation in order to model the user's intention for refining segmentation. In the user intention model, the color of each stroke indicates the user's expectation of pixel label change to foreground or background, the location of the stroke indicates the user's region of interest, and the position of the stroke relative to a previous segmentation boundary indicates a segmentation error that the user intends to refine. Overexpansion of pixel label change is controlled by penalizing change outside the user's region of interest while overshrinkage is controlled by modeling the image as an eroded graph. In each iteration, energy consisting of a color term, a contrast term, and a user intention term is minimized to obtain a segmentation map.

Abstract: Salience-preserving image fusion is described. In one aspect, multi-channel images are fused into a single image. The fusing operations are based on importance-weighted gradients. The importance weighted gradients are measured using respective salience maps for each channel in the multi-channel images.